Congress on Intelligent Systems (CIS 2020) · An Empirical Analysis of Hierarchical and Partition Based Clustering Techniques in Optic ... Accuracy Evaluation of Plant Leaf Disease

2020

Organized by

Soft Computing

Research Society

www.scrs.in

September 05-06, 2020

Congress on Intelligent Systems (CIS 2020)

Souvenir

i

TABLE OF CONTENTS

Message......................................................................................................................................1

General Chair, CIS 2020 ..........................................................................................................11

Organising Secretary, CIS 2020 ..............................................................................................11

Publicity Committee, CIS 2020 ...............................................................................................11

Publication Committee, CIS 2020 ...........................................................................................12

Finance Committee, CIS 2020 .................................................................................................12

Registration Committee, CIS 2020 ..........................................................................................12

Session Management Committee, CIS 2020 ...........................................................................12

Website Committee, CIS 2020 ................................................................................................12

Organizing Committee, CIS 2020............................................................................................13

Advisory Committee, CIS 2020 ...............................................................................................13

Keynote Speakers.....................................................................................................................16

Special Tracks ..........................................................................................................................27

Programme Schedule ...............................................................................................................28

Paper Details ............................................................................................................................33

Abstract of Accepted Papers ....................................................................................................40

Escalating Convergence in Differential Evolution using Adaptive Local Search Strategies ..40

Artificial Intelligence Based Power Quality Improvement Techniques in WECS ..................40

Chaotic Henry Gas Solubility Optimization Algorithm ..........................................................40

Designing Controller Parameter of Wind Turbine Emulator using Artificial Bee Colony

Algorithm .................................................................................................................................41

ii

Dynamic Stability Enhancement of Grid Connected Wind System using Grey Wolf

Optimization Technique...........................................................................................................41

Robotic Arm Based Storage and Retrieval System .................................................................41

Designing of Smart Bio-nano Robotics System for Cancer Cell Therapy and Effective Drug

Delivery....................................................................................................................................42

A Comparative Analysis on Wide Area Power System Control with Mitigation the Effects of

Imperfect Medium ...................................................................................................................42

Adware Attack Detection on IoT Devices using Deep Logistic Regression SVM (DL-SVM-

IOT)..........................................................................................................................................43

Intrusion Detection System for Securing Computer Networks Using Machine Learning: A

Literature Review.....................................................................................................................43

8-bit ALU with PCB Implementation ......................................................................................43

Vision Based System for Vehicle Detection and Tracking System .........................................44

An Exploration of Entropy Techniques for Envisioning Announcement Period of Open

Source Software .......................................................................................................................44

Raspberry Pi Based Smartphone ..............................................................................................45

Digital Brain Building a Key to Improve Cognitive Functions by an EEG–Controlled

Videogames as Interactive Learning Platform .........................................................................45

A Literature Review on Generative Adversarial Networks with its Applications in Healthcare

..................................................................................................................................................45

Occurrence Prediction of Pests and Diseases in Rice of Weather Factors using Machine

Learning ...................................................................................................................................46

A Federated Search System for Online Property Listings Based on SIFT Algorithm ............46

Intelligent Car Cabin Safety System Through IoT Application ..............................................46

IoT Security: A Survey of Issues, Attacks and Defences ........................................................47

Employing Data Augmentation for Recognition of Hand Gestures using Deep Learning......47

Utilization of Delmia Software for Saving Cycle Time in Robotics Spot Welding ................47

iii

Data Protection Techniques Over Multi-Cloud Environment - A Review ..............................48

Hierarchical Ontology Based Word Sense Disambiguation of English to Hindi Language ...48

Review Paper: Error Detection and Correction onboard Nanosatellites .................................48

Kerala Floods : Twitter Analysis using Deep Learning Techniques .......................................49

An Empirical Analysis of Hierarchical and Partition Based Clustering Techniques in Optic

Disc Segmentation ...................................................................................................................49

An Improved DVFS Circuit & Error Correction Technique ...................................................49

Detecting the Nuclei in Different Pictures Using Region Convolutional Neural Networks ...50

Effective Predictive Maintenance to overcome System Failures – A Machine Learning

Approach ..................................................................................................................................50

Modelling Subjective Happiness with a Survey Poisson Model and XGBoost using an

Economic Security Approach ..................................................................................................51

IN-LDA: An Extended Topic Model for Efficient Aspect Mining .........................................51

Imbalance Rectification using Venn Diagram based Ensemble of Undersampling Methods for

Disease Datasets.......................................................................................................................51

Personalized Route Finding System Using Genetic Algorithm ...............................................52

Efficient Approach for Encryption of Lossless Compressed Grayscale Images .....................52

A Comparative Study on Handwritten Devanagari Character Recognition ............................52

Hand Gesture Recognition System using IoT and Machine Learning ....................................53

Deep Learning based Framework for Retinal Vasculature Segmentation ...............................53

A Novel Unified Scheme for Missing Image Data Suggestion Based on Collaborative

Generative Adversarial Network .............................................................................................54

Application of Social Big Data in Crime Data Mining............................................................54

A Low-Cost Embedded Computer Vision System for the Classification of Recyclable Objects

..................................................................................................................................................55

IoT Based RGB LED Information Display System .................................................................55

iv

Personal Assistant for Career Coaching ..................................................................................55

A Software Reusability Paradigm for Assessing Software-as-a-Service for Cloud Computing

..................................................................................................................................................56

Determination of Breakdown Voltage for Transformer Oil Testing using ANN ....................56

V. Srividhya1a, Jada Satish Babu1b, K.Sujatha1c, J. Veerendrakumar1c, M. Aruna2a, Shaik

Shafiya2b, SaiKrishna2c and M. Anand2d ..................................................................................56

Delivering Newspapers Using Fixed Wing Unmanned Aerial Vehicles .................................57

Code Buddy: A Machine Learning-Based Automatic Source Code Quality Reviewing System

..................................................................................................................................................57

Monitoring Web-based Evaluation of Online Reputation in Barcelona ..................................57

Telugu Scene Text Detection using Dense Textbox ................................................................58

Detection of Cardiac Stenosis using Radial Basis Function Network .....................................58

Impacts of Environmental Pollution on the Growth and Conception of Biological Populations

Involving Incomplete I-function ..............................................................................................59

An Improved Machine Learning Model for IoT-based Crop Management System ................59

Accuracy Evaluation of Plant Leaf Disease Detection and Classification using GLCM and

Multiclass SVM Classifier .......................................................................................................60

Information Technology for the Synthesis of Optimal Spatial Configurations with

Visualization of the Decision-Making Process ........................................................................60

Elastic Optical Networks survivability based on Spectrum Utilization and ILP model with

increasing Traffic .....................................................................................................................61

Bluetooth Controlled Arduino Robot.......................................................................................61

Residual Vibration Suppression of Non-Deformable Object for Robot Assisted Assembly

Operation Using Vision Sensor ...............................................................................................61

Open MV - Micro Python based DIY Low Cost Service Robot in Quarantine Facility of

COVID-19 Patients ..................................................................................................................62

v

An Improved Inception Layer Based Convolutional Neural Network for Identifying Rice

Leaf Diseases ...........................................................................................................................62

Educator's perspective towards the implementation of Technology-Enabled Education in

schools......................................................................................................................................62

Sensorless Speed Control of Induction Motor Using Modern Predictive Control ..................63

Software Log Anomaly Detection Method using HTM Algorithm.........................................63

A Study on Intelligent Control in PV System Recycling Industry ..........................................63

An Automatic Digital Modulation Classifier Using Higher-Order Statistics for Software

Defined Radios.........................................................................................................................64

Low Profile Wide Band Micro Strip Antenna for 5G Communication ...................................64

Hybrid Cryptography Algorithm for Secure Data Communication in WSNs: DECRSA .......64

Hybrid Approach to Combinatorial and Logic Graph Problems .............................................65

Malware attacks on Electronic Health Records .......................................................................65

Energy Efficient Algorithms Used In Datacenters : A Survey ................................................65

A Perspective of Security Features in Amazon Web Services ................................................66

Question Answering System Using Knowledge Graph Generation and Knowledge Base

Enrichment with Relation Extraction.......................................................................................66

A Study on Design Optimization of Spur Gear Set .................................................................67

Text-Document Orientation Detection Using Convolutional Neural Networks......................67

Air Quality Monitoring System using Machine Learning and IoT..........................................67

Optimization of Regularization and Early Stopping to Reduce Overfitting in Recognition of

Handwritten Characters ...........................................................................................................68

Comparison of Various Classifiers for Indian Sign Language Recognition using State of the

Art Features ..............................................................................................................................68

Logical Inference in Predicate Calculus with the Definition of Previous Statements .............69

vi

Congestion Management Considering Demand Response Programs using Multi-objective

Grasshopper Optimization Algorithm......................................................................................69

Effectiveness of Software Metrics on Reliability for Safety Critical Real-Time Software .....69

A Hardware Accelerator Implementation of Multilayer Perceptron .......................................70

Building a Classifier of Behavior Patterns for Semantic Search based on Cuckoo Search

Meta-heuristics .........................................................................................................................70

Impact of Quasi-variable Nodes on Numerical Integration of Parameter-Dependent

Functions: A Maple Suite ........................................................................................................70

Text Clustering Techniques for Voice of Customer Analysis .................................................71

Analyzing the Impact of Software Requirements Measures on Reliability Through Fuzzy

Logic ........................................................................................................................................71

Social Network Opinion Mining and Sentiment Analysis: Classification Approaches, Trends,

Applications and Issues............................................................................................................71

Dynamic Analysis of a Novel Modular Robot ........................................................................72

A Comparative Analysis on Edge Preserving Approaches for Image Filtering ......................72

TETRA Enhancement Based on Adaptive Modulation ...........................................................72

Progress in the Mathematical Modelling of the Emotional States ...........................................73

Design and Analysis of Mechanical Properties of Simple Cloud-based Assembly Line Robots

..................................................................................................................................................73

Impact of Dynamic Metrics on Maintainability of System using Fuzzy Logic Approach ......73

Feature Based AD Assessment using ML ...............................................................................74

Notification System For Text Detection On Document Images Using Graphical User

Interface (GUI).........................................................................................................................74

A Tripod Type Walking Assistance for the Stroke Patient......................................................75

COVID-19 Risk Management .................................................................................................75

Speech Emotion Recognition Using Machine Learning Techniques ......................................75

vii

Model Based Data Collection Systems on Fog Platforms .......................................................76

Data Routing With Load Balancing Using Ant Colony Optimization Algorithm ..................76

Show Based Logical Profound Learning Demonstrates Utilizing ECM Fuzzy Deduction

Rules in DDoS Assaults for WLAN 802.11 ............................................................................76

Handwritten Devanagari Character Recognition using CNN with Transfer Learning ............77

Estimation of Road Damage for Earthquake Evacuation Guidance System Linked with SNS

..................................................................................................................................................77

IoT Based Home Vertical Farming ..........................................................................................77

Improved Video Compression using Variable Emission Step ConvGRU based Architecture78

Analysis of Lightweight Cryptography Algorithms for IoT Communication .........................78

Comparative Design Analysis of Optimized Learning Rate for Convolutional Neural Network

..................................................................................................................................................78

Reliability Evaluation of Distribution System based on Interval type-2 Fuzzy System .........79

Atmospheric Temperature Prediction Using Ensemble Deep Learning Technique ................79

Text Recognition Using Convolution Neural Network for Visually Impaired People ............80

Tweets Reporting Abuse Classification Task: TRACT ...........................................................80

Design of Automatic Answer Checker ....................................................................................80

Community Detection using Fire Propagation and Boundary Vertices ..................................81

Towards Grammatical Evolution Based Automated Design of Differential Evolution

Algorithm .................................................................................................................................81

Video Surveillance System with Auto Informing Feature .......................................................81

An Optimal Feature based Automatic Leaf Recognition Model using Deep Neural Network82

Input Parameter Optimization with Simulated Annealing Algorithm for Predictive HELEN-I

ion Source ................................................................................................................................82

LRSS-GAN: Long Residual Paths and Short Skip Connections Generative Adversarial

Networks for Domain Adaptation and Image Inpainting ........................................................82

viii

Load Equilazation Technique and Security Mechanism for Cloud Performance....................83

Automatic Recognition of ISL Dynamic Signs with Facial Cues ...........................................84

Enhance The Prediction of Air Pollutants Using K-Means++ Advanced Algorithm with

Parallel Computing ..................................................................................................................84

Passive Motion Tracking for Assisting Augmented Scenarios................................................84

Plasma Density Prediction for Helicon Negative Hydrogen Plasma Source using Decision

Tree and Random Forests Algorithm .......................................................................................85

Efficient Fuzzy Similarity based Text Classification with SVM and Feature Reduction .......85

Disease Prediction from Speech using Natural Language Processing and Deep Learning

Methods....................................................................................................................................85

Classification of Human Postural Transition and Activity Recognition Using Smartphone

Sensor Data ..............................................................................................................................86

A Blockchain based Multi-Layer Framework for securing Healthcare Data on Cloud ..........86

ComVisMD - Compact 2D Visualization of Multidimensional Data: Experimenting with two

different datasets ......................................................................................................................87

Design of Compact Size Tri-Band Stacked Patch antenna for GPS and IRNSS Applications87

Investigation on Error Correcting Channel Codes for 5G New Radio ....................................88

A Deep Learning Technique for Automatic Teeth Recognition in Dental Panoramic X-Ray

Images Using Modified Palmer Notation System ...................................................................88

Super Resolution of Level-17 Images using Generative Adversarial Networks .....................89

Analysis of the Messages from Social Network for Emergency Cases Detection ..................89

Color Image Watermarking Technique using Principal Component in RDWT Domain ........89

A Framework for Disaster Monitoring using Fog Computing ................................................90

Query Auto-Completion Using Graphs ...................................................................................90

Comparative Analysis of Load Flows and Voltage Depended Load Modeling methods of

Distribution Networks ..............................................................................................................90

ix

Efficient Machine Learning Algorithm for Cancer Genome Classification ............................91

Attitude Control in Unmanned Aerial Vehicles using Reinforcement Learning – A Survey .91

Intelligent Simulation of Competitive Behavior in a Business System ...................................92

Minimizing the Subset of Features on BDHS Dataset to Improve Prediction on Pregnancy

Termination ..............................................................................................................................92

A Comparative Analysis of Software Development Models from Traditional to Present-day

Approaches ..............................................................................................................................92

Improved Image Super Resolution using Enhanced Generative Adversarial Network a

comparative study ....................................................................................................................93

Smart Lady E-Wearable Security System for Women Working in The Field ........................93

Black Hole-White Hole Algorithm for Dynamic Optimization of Chemically Reacting

Systems ....................................................................................................................................94

Self-Supervised Learning Approaches for Traffic Engineering in Software Defined Networks

..................................................................................................................................................94

Automated Cooperative Robot for Screwing Application .......................................................94

Evaluation of Electric Vehicle Charging Cost Using HOMER Grid ......................................95

PUNER - Parsi ULMFiT for Named-Entity Recognition in Persian Texts .............................95

Linguistic Classification using Instance Based Learning ........................................................95

Multi Class Support Vector Machine Based Household Object Recognition System Using

Features Supported by Point Cloud Library ............................................................................96

A Review of Nature-inspired Algorithm-based Multi-objective Routing Protocols ...............96

Hopping Spider Monkey Optimization ....................................................................................97

A Coverage and Connectivity of WSN in 3D Surface Using Sailfish Optimizer ...................97

An Automatic Emotion Analysis of Real Time Apple Mobile Tweets ...................................97

Flexible Bolus Insulin Intelligent Recommender System for Diabetes Mellitus using Mutated

Kalman Filtering Techniques ...................................................................................................98

x

Detection of Parkinson’s disease from hand-drawn images using Deep Transfer Learning ...98

Adaption of Smart Devices and Virtual Reality (VR) in Secondary Education ......................99

Deep Learning Technique for Predicting Optimal ‘Organ At Risk’ Dose Distribution for

Brain Tumor Patients ...............................................................................................................99

An Optimal Feature Selection Approach based on IBBO for Histopathological Image

Classification..........................................................................................................................100

A Fractional Model to Study the Diffusion of Cytosolic Calcium ........................................100

Initialization of MLP Parameters using Deep Belief Networks for Cancer Classification ...100

A Classification Model for Software Bug Prediction Based on Ensemble Deep Learning

Approach Boosted with SMOTE Technique .........................................................................101

Modeling the Relationship Between Distance and Received Signal Strength Indicator of the

Wi-Fi Over the Sea to Extract Data in Situ From a Marine Monitoring Buoy ......................101

Signal Processing Techniques for Coherence Analysis Between Ecg and Eeg Signals with A

Case Study .............................................................................................................................102

Data Classification Model for Fog-Enabled Mobile IoT Systems .........................................102

Assessing the Role of Age, Population Density, Temperature and Humidity in the Outbreak

of COVID19 Pandemic in Ethiopia .......................................................................................103

A Review on Dimensionality Reduction in Fuzzy and SVM based Text Classification

Strategies ................................................................................................................................103

Multi-Objective Teaching–Learning-based Optimization for Vehicle Fuel Saving

Consumption ..........................................................................................................................104

Soft Computing Tool for Prediction of Safe Bearing Capacity of Soil .................................104

Effective Teaching of Homogenous Transformations and Robot Simulation using Web

Technologies ..........................................................................................................................105

An Automated Citrus Disease Detection System using Hybrid Feature Descriptor .............105

Adaptive Fuzzy Algorithm to Control the Pump Inlet Pressure ............................................105

xi

Clustering High Dimensional Datasets using Quantum Social Spider Optimization with DWT

................................................................................................................................................106

Intuitive Control of 3 Omni-wheel based Mobile Platform using Leap Motion ...................106

Abnormal Event Detection in Public Places by Deep Learning Methods .............................107

Maximum Power Point Tracking Of Photovoltaic System Using Artificial Neural Network

................................................................................................................................................107

Design & Implementation of Traffic Sign Classifier Using Machine Learning Model ........107

Multipurpose Advanced Assistance Smart Device for Patient Care with Intuitive Intricate

Control ...................................................................................................................................108

Smart Saline Monitoring System for Automatic Control Flow Detection and Alertness using

IoT Application ......................................................................................................................108

Comparative Study on AutoML approach for Diabetic Retinopathy Diagnosis ...................109

Development and Control of a 7-DOF Bionic Arm of with Data Gloves and EMG Arm Band

................................................................................................................................................109

A Deep Learning based Segregation of Housing Image Data for Real Estate Application ..109

Design of Decision Support System to Identify Crop Water Need .......................................110

Development of Inter-ethnic Harmony Search Algorithm based on Inter-ethnic Harmony .110

Electric Load Forecasting Using Fuzzy Knowledge Base System with Improved Accuracy

................................................................................................................................................111

1

Prof. Kusum Deep

Indian Institute of Technology, Roorkee, India

Message

On behalf of the organizing committee and on my personal behalf as the president of Soft

Computing Research Society, it is my great pleasure to welcome you to the Congress on

Intelligent Systems 2020. The CIS 2020 scientific program will foster discussions and hopes

to inspire participants from a wide array of themes to initiate collaborations within and across

disciplines for the advancement of our field.

The various thematic sessions will showcase important scientific advances and highlight

recent research which is going to impact the world a lot. I welcome all of you to attend the

keynote and oral presentations and invite you to interact with the conference participants.

I thank the organizing members, participants, session chairs and keynote speakers for helping

us to build this very exciting conference program. The Organizing and Scientific Committees

will make any possible effort to make sure that your participation will be scientifically

rewarding.

Prof. Kusum Deep

Honorary Chair, CIS 2020

2

Prof. Atulya K. Nagar

Pro Vice-Chancellor for Research

Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom.

Message

We currently live in unprecedented times of the menace caused by a pandemic which has and

will impact the future of research in many different ways. There are many short-term changes

to adapt to – a good example of this is that this SCRS supported conference is taking place

virtually, using online platform, to ensure everybody’s health and safety. We should make

this opportunity as productive and enjoyable as possible – although, the real face to face

interaction, intellectual discourse, and a collegium environment will be much missed.

However, apart from the short-term changes, to which we will adapt to, the challenges facing

research and the way we conduct research will be broad and long-lasting. For instance,

organisations and governments are already taking measures to commit to research on

Artificial Intelligence (AI) and Intelligent Systems (IS) and Robotics technologies that can

learn from the ambient environment. We are also seeing the rush to adopt new technology

during coronavirus-driven remote working which could lead to use more tools powered by

advanced artificial intelligence as we get used to working in newer ways.

Intelligent Systems (IS) has already become the keyword which defines the future and

everything that it holds. Not only has IS and AI taken over traditional methods of computing,

but it has also changed the way industries perform. From modernising healthcare and finance

streams to research and manufacturing, everything is set to change rapidly. Many of us in the

scientific community believe that in the wake of the global pandemic, the Artificial

Intelligence and Robotics revolution which was supposed to take place in the next five to ten

3

years (or so) will perhaps take place sooner rather than later. In other words, there is no

denying the fact that IS has and will revolutionise the very essence of the way we work and

live our lives.

To achieve the maximum benefit for all humanity a coordinated approach will be vital in

these circumstances where resources are already stretched. Much of the Research endeavour

has already become a vital component of the response to COVID-19 and its aftermath; the

work of mathematicians, engineers and scientists has been at the forefront of scientific

response to current crisis; but we have to ensure that we avoid competition between similar

attempts and instead collaborate and work together, encourage multi/inter-disciplinary and

multi-stakeholder co-operation and knowledge exchange both nationally and internationally

by the scientific community, medical community, mathematicians, developers and policy

makers to formulate the problem, identify relevant data and open datasets, develop models,

share tools, techniques, and train models. We need an Intelligent Systems approach for

humanising technology – i.e. making technology that should be about more than making

technology look, sound and feel human. It should advance humanity. Push-forward.

Work over the past two decades in the areas of Artificial Intelligence (AI) and Intelligent

Systems (IS) has shown that AI is not a silver bullet. AI systems based on machine learning

work by identifying patterns in data, and require large amounts of data to find these patterns.

The outputs are only as good as the training data, and in some cases, diagnostic claims have

been called into question. We need to ensure that IS systems are developed and deployed

responsibly.

We must find ways to support research across the globe as research is of course not national,

it is global. I hope this CIS forum, supported by SCRS, will help us build strong research

connections and links. SCRS already has enormously strong research links across India and

globally, and we will continue to support such efforts and initiatives through SCRS. This is

particularly important for our early career and upcoming researchers.

I appreciate your attendance and trust The CIS-2020 will be a rewarding and worthwhile

experience for you.

Prof. Atulya Nagar

Honorary Chair, CIS 2020

4

Prof. Joong Hoon Kim

Professor of Engineering College, Korea University, South Korea

Message

First, I would like to congratulate the Local Organizing Committee and the Soft Computing

Research Society for the successful landing of CIS 2020. I heard there are almost 700 papers

submitted and only a limited number of quality papers have been accepted. With ten keynote

speeches from world-known researchers, this event will be a cornerstone for all conferences

regarding intelligent systems.

I invented an optimization algorithm called Harmony Search and have managed the ICHSA

(International Conference on Harmony search, Soft computing, and Applications) conference

series. The conferences have been held in South Korea, Spain, India, China, and Turkey. The

one held in Istanbul, Turkey in July this year was in virtual format like CIS 2020. Although

we are missing the face to face meeting and networking, I am certain that participants in this

conference can benefit from the presentation of high standard papers and quality keynotes.

I hope everybody participating this congress will enjoy the materials and contents of the

event and wish a healthy living in the pandemic situation. Thank you.

Prof. Joong Hoon Kim

General Chair, CIS 2020

5

Prof. Jagdish Chand Bansal

South Asian University Delhi, India

Message

On behalf of the Congress on Intelligent Systems (CIS-2020) organizing committee, I extend

heartfelt welcome to this conference being held virtually. The success of this conference

would not have been possible without the extensive contributions from many volunteers and

dedication from many colleagues on the CIS-2020 team. The high quality of the technical

program owes much to the efforts of the program committee and external reviewers. Special

thanks to Keynote speakers for their insightful vision to be delivered to the attendees.

This Conference is unique in its depth and breadth of subjects focused on intelligent systems,

and the various application areas underscores that diversity. I would like to draw your

attention to three elements of the Conference that will enhance your time at the CIS-2020:

education, networking, and working with other researchers. In addition to two days of strong

technical papers and reports, a key element of the Conference is the opportunity to share and

exchange ideas with other attendees. You will find networking opportunities between the

Technical Sessions. I hope all of you enjoy CIS-2020 and find this a productive opportunity

to learn, exchange ideas, make new contacts and renew old ones.

Please take a few minutes to examine this book of abstracts and explore the variety of

sessions and papers that make up this Conference. The selected papers from the conference

will be published in Springer book series, AISC and LNDECT. Both the series are SCOPUS

indexed. Once again, I appreciate your attendance and trust. The CIS-2020 Conference will

be a rewarding and worthwhile experience for you.

Prof. Jagdish Chand Bansal


6

Dr. Mukesh Saraswat

Jaypee Institute of Information Technology, Noida, India

Message

We are pleased to welcome you to the first edition of International Conferences, “Congress

on Intelligent Systems (CIS2020)”, organized by the Soft Computing Research Society

(SCRS), India in a virtual format that will brings all the researchers from various national and

international research communities under one umbrella.

The innovations in the field of intelligent system have produced new opportunities across all

the dimensions of human endeavors. As our dependency on such systems is continuously

increasing, the new challenges have aroused along with the existing ones. With the evolution

of the intelligent systems, the need of the hour is to handle such challenges proficiently. This

conference emphasizes on the issues and applications of intelligent systems through the

keynotes and technical perspectives. In its first version, a large number of manuscripts have

been submitted in one general track and 11 special tracks. For this dissemination

contributions of the Publicity Committee members and special session chairs are highly

appreciated.

The selection of the quality papers among the submitted ones was a challenging task for the

Technical Program Committee and I am very proud to announce that we had an acceptance

ratio of approximately 27% which is comparable to the top conferences of this domain. I

whole-heartedly appreciate the hard work of the Reviewers for making this quality selection

possible.

7

This conference would not have been successfully reached to its peak without the guidance,

dedication and contribution of Prof. Kusum Deep, Prof. Atulya K. Nagar and Eminent

Professors of advisory committee. I also thank the keynotes speakers, Prof. Meng-Hiot Lim,

Prof. Andries Engelbrecht, Prof. Mohammad Shorif Uddin, Prof. Dumitru Baleanu, Prof.

Maurice Clerc, Prof. Swagatam Das, Prof. Johnathan H. Chan, Prof. Nischal K. Verma, Prof.

Amir H. Gandomi, Prof. Akhil Ranjan Garg and Prof. Aninda Bose, for leading us sharing

their knowledge. Furthermore, I am grateful to all the session chairs and various committee

members for their endless efforts to make this event successful.

I am thankful to the Program Co-Chairs, Dr. Harish Sharma, Prof. Joong Hoon Kim and Dr.

Jagdish Chand Bansal for their undying endeavors for the seamless conduct of the conference

in putting together. I pay my gratitude to all the authors who submitted their research work to

CIS 2020.

In this conference we have also recognized the outstanding work among the presented papers

from each track. To acknowledge the same, a small incentive along with the appreciation

certificate has been provided to the prospective authors, which will motivate the authors to

submit the quality work in future conferences. To provide significant exposure of the state of

the knowledge in a field, a conference should maintain high standards while accepting the

papers, attract quality work from researchers globally, and an adaptable organizing

committee.

I hope CIS2020 meets all these standards and will continue the same in coming years. I

believe you all have a great learning and knowledge sharing during the paper presentation,

and keynote sessions.

Dr. Mukesh Saraswat


8

Dr. Harish Sharma

Rajasthan Technical University, Kota, India

Message

I take great pleasure in welcoming delegates to the Congress on Intelligent Systems CIS 2020

organized by the Soft Computing Research Society.

With changing times and widening horizons, processing information is indeed an important

task for the benefit of humanity overall. The importance of computers and communication to

the success of an organization cannot be ignored in an organizational landscape, the success

of any organization depends on the organization's ability to manage computing as an asset.

The conference seeks to target emerging issues related to AI, machine learning, the Internet

of things, robotics and genetic engineering. I feel honored to welcome all the delegates

attending this conference and wish that the effort to bring professionals and researchers

together on a common platform provides an inspiration in overcoming issues in the field of

computational intelligence.

With great enthusiasm I wish that this conference would add value in the real world and

provide practical solutions to meet the challenges in the field of intelligence. Thank you all

for participating in CIS-2020. Enjoy conference.

Dr. Harish Sharma


9

Dr. Anupam Yadav

Dr B R Ambedkar National Institute of Technology, Jalandhar, India

Message

We are indeed privileged and delighted to host the Congress on Intelligent Systems (CIS

2020) September 5-6, 2020. The congress is sponsored and hosted by SCRS. I express my

heartfelt thanks to honorary chairs of the conference Prof. Kusum Deep, President, SCRS,

Prof. Atulya K. Nagar, Pro-Vice-Chancellor, Liverpool Hope University UK, Dr J.C. Bansal

General Secretary, SCRS for agreeing upon to organize such needful scientific event.

I am also thankful to the General Chairs of the Conference, Prof. Joong Hoon Kim, Dr J.C.

Bansal, Dr Mukesh Saraswat, and Dr Harish Sharma for their continuous guidance and

untiring efforts to make this conference a reality. I am pleased to inform you that the

conference is going to witness top-notch Keynote Speakers from across the globe. I am sure

their knowledge and the exposure in their fields shall benefit to the participants to develop

new ideas. I am thankful to the entire team of CIS 2020 for their hard work and effort to

make this conference a success.

I also take this opportunity to extend a warm welcome to the delegates and wish the event a

great success.

Dr. Anupam Yadav,


10

Organising Secretary, CIS 2020

Message

Dr. Sandeep Kumar

Amity University Rajasthan, Jaipur, India

I am delighted to welcome the participants of the Congress

on Intelligent Systems CIS 2020. This scientific meeting

would provide an excellent opportunity for the young

students and researchers to interact with eminent Scientists

from India and abroad, belonging to diverse disciplines of engineering, information

technology, management and applied sciences.

Dr. S. D. Purohit


It gives me great pleasure to welcome the delegates of the

Congress on Intelligent Systems CIS 2020 Organized by

Soft Computing Research Society.

Dr. Prashant Singh Rana

Thapar Institute of Engg. & Tech., Patiala,

India

I feel honoured to welcome all the delegates attending of the

Congress on Intelligent Systems CIS 2020 and wish that the

effort to bring together professionals and researchers on a

common platform gives an impetus in overcoming issues in the field of Computational

Intelligence.

11


Harish Sharma, Rajasthan Technical University, Kota, India

Mukesh Saraswat, Jaypee Institute of Inormation Technology, Noida, India

Joong Hoon Kim, Korea University, South Korea

Jagdish Chand Bansal, South Asian University Delhi, India

Organising Secretary, CIS 2020

Sandeep Kumar, Amity University Rajasthan, Jaipur, India

Anupam Yadav, Dr B R Ambedkar National Institute of Technology, Jalandhar, India

S. D. Purohhit, Rajasthan Technical University, Kota, India

Prashant Singh Rana, Thapar Institute of Engineering & Technology, India

Publicity Committee, CIS 2020

Anirban Das, University of Engineering & Management, Kolkata, India

Ramesh C. Poonia, Cyber-Physical Systems Laboratory NTNU, Ålesund, Norway

Vijander Singh, Manipal University Jaipur, Jaipur, India

C. Rani, Vellore Institute of Technology, Vellore, India

Neha, National Institute of Technology, Hamirpur, India

D. L. Suthar, Wollo University, Ethiopia

Faruk Ucar, Marmara University, Turkey

Linesh Raja, Manipal University Jaipur, India

Paveen Agrawal, Anand International College of Engineering, Jaipur, India

Ponnambalam P, Vellore Institute of Technology, Vellore, India

Shambhu Shankar Bharti, Loknayak Jai Prakash Institute of Technology, Chhapra, India

V. K. Vyas, Sur University College, Oman

12

Publication Committee, CIS 2020

Sumit Kumar, Amity School of Engineering and Technology, Noida, India

Raju Pal, Jaypee Institute of Information Technology, Noida, India

Finance Committee, CIS 2020

Ajay Sharma, Government Engineering College Jhalawar, India

Nirmala Sharma, Rajasthan Technical University, Kota, India

Registration Committee, CIS 2020

Ashish Tripathi, Malaviya National Institute of Technology, Jaipur, India

Praveen Kumar Shukla, BBD University Lucknow, India

Shimpi Singh Jadon, Govt. Rajkiya Engineering College Kannauj UP, India

Soniya Lalwani, BKBIT, Kota, India

Session Management Committee, CIS 2020

Kusum Lata Agarwal, Jodhpur Institute of Engineering & Technology, Jodhpur, India

Avinash Pandey, Jaypee Institute of Information Technology, Noida, India

Himanshu Mittal, Jaypee Institute of Information Technology, Noida, India

Kamlesh Jangid, Rajasthan Technical University, Kota, India

Kusum Kumari Bharti, Indian Institute of Information Technology, Design and

Manufacturing, Jabalpur, India

Website Committee, CIS 2020

Sakshi Shringi, Rajasthan Technical University, Kota

Shitu Singh, South Asian University Delhi, India

Twinkle Tiwari, Jaypee Institute of Information Technology, Noida

13

Organizing Committee, CIS 2020

Rajani K Poonia, JECRC, University, Jaipur. India

Kedar Nath Das, National Institute of Technology, Silchar, India

Manoj Thakur, Indian Institute of Technology, Mandi, India

Dhiraj Sangwan, Sr. Scientist, CSIR-CEERI, Pilani, India

Devendra Kumar, University of Rajasthan, India

Jagdev Singh, JECRC University, Jaipur, India

Jayprakash, National Institute of Technology Calicut, India

Lokesh Chauhan, National Institute of Technology, Hamirpur, India

Pinkey Chauhan, Jaypee Institute of Information Technology, Noida

Nafis uddin Khan, Jaypee University of Information Technology, Solan, India

Saroj Hiranwal, Rajasthan Institute of Engineering & Technology, Jaipur, India

Ritu Agrawal, Malaviya National Institute of Technology (MNIT), Jaipur, India

K G Sharma, Government Engineering College Ajmer, India

Satya Narayan Tazi, Government Engineering College Ajmer, India

Ravindra N. Jogekar, RTM Nagpur University, Nagpur

Harish V. Gorewar, RTM Nagpur University, Nagpur

Shantanu A. Lohi, SGB Amravati University, Amravati

Advisory Committee, CIS 2020

A. K. Singh, Motilal Nehru National Institute of Technology, Allahabad

A K Verma, Western Norway University of Applied Sciences, Haugesund, Norway

Abdel Salam Gomaa, Qatar University, Doha

Aboul Ella Hassanien, Cairo University, Egypt

Adarsh Kumar, University of Petroleum and Energy Studies, Dehradun, India

Ajay Vikram Singh, AIIT, Amity University Uttar Pradesh, India

Akhil Ranjan Garg, MBM Engg. College, Jodhpur, India

Ali A. Al –Jarrah, Sur University College, Oman

Ali Mirjalili, Torrens University, Australia

Alok Kanti Deb, Indian Institute of Technology, Kharagpur, India

14

Anand Nayyar, Scientist, Graduate School, Duy Tan University, Da Nang, Viet Nam

Anand Paul, Kyungpook National University, South Korea

Anuradha Ranasinghe, Liverpool Hope University, UK

Anurag Jain, GGSIP University, Delhi, India

Aruna Tiwari, Indian Institute of Technology, Indore, India

Arun Solanki, Gautam Buddha University, Greater Noida, India

Ashish Kr. Luhach, The PNG University of Technology, Papua New Guinea

Ashvini Chaturvedi, National Institute of Technology, Suratkal

Atulya K. Nagar, Liverpool Hope University, UK

Ayush Dogra, Biomedical Instrumentation Unit, CSIR-CSIO (Research Lab- Government of

India), Chandigarh, India

B. Padmaja Rani, JNTU Hyderabad, India

Basant Agarwal, IIIT Kota, Rajasthan India

Carlos A Coello Coello, Investigador CINVESTAV 3F (Professor with Distinction)

D.L. Suthar, Wollo University, Ethiopia

Dan Simon, Cleveland State University, USA

Debasish Ghose, IISc Bangalore, India

Deepak Garg, Bennett University, India

Dhirendra Mathur, Rajasthan Technical University, Kota

Dinesh Goyal, Poornima Institute of Engineering & Technology, Jaipur

Dumitru Baleanu, Cankaya University

Faruk Ucar, Marmara University, Turkey

Garima Mittal, IIM Lucknow, India

Gonçalo Marques, University of Beira Interior, Portugal

Hanaa Hachimi, Ibn Tofail University, Morocco

J. Senthilnath, Scientist, Machine Intellection, Institute for Infocomm Research (I²R) |

Agency for Science, Technology and Research (A*STAR), Singapore

Janmenjoy Nayak, Aditya Institute of Technology and Management, Andhra Pradesh, India

Janos Arpad Kosa, Neumann Janos University, Hungary

K. S. Nisar, Prince Sattam bin Abdulaziz University, Riyadh, Saudi Arabia

Kapil Sharma, Head Department of IT, DTU, Delhi, India

Kedar Nath Das, National Institute of Technology Silchar (NITS), Silchar, India

Kusum Deep, Indian Institute of Technology, Roorkee, India

15

Lipo Wang, Nanyang Technological University, Singapore

Mahesh Bundele, Poornima College of Engineering, Jaipur, India

Manju, Jaypee Institute of Information Technology, Noida, India

Manoj Thakur, Indian Institute of Technology, Mandi, India

Mario José Diván, Data Science Research Group Universidad Nacional de La Pampa

Coronel Gil 353, Primer Piso - Santa Rosa (CP 6300), La Pampa, Argentina

Maurice Clerc, Independent Consultant, France

Mohammad S Khan, East Tennessee State University, Johnson City, USA

N. R. Pal, Indian Statistical Institute, Kolkata, India

Neil Buckley, Liverpool Hope University, UK

Nilanjan Dey, Techno India College of Technology, India

Nishchal K. Verma, Indian Institute of Technology Kanpur, India

Noor Zaman, Taylor's University, Malaysia

P. Vijaykumar, University College of Engineering Tindivanam

Pankaj Srivastava, MNNIT, Prayagraj, India

Prashant Jamwal, Nazarbayev University, Kazakhstan

R. C. Mittal, Jaypee Institute of Information Technology, India

Ravinder Rena, North West University, Mafikeng Campus, South Africa

Ravi Raj Choudhary, Central University of Rajasthan, India

S. Sundaram, IISc Bangalore, India

Said Salhi, Kent Business School | University of Kent

Sarbani Roy, Jadavpur University, Kolkata, India

Satish Chand, Jawaharlal Nehru University, India

Sanjeevikumar Padmanaban, Aalborg University, Esbjerg, Denmark

Sudeep Tanwar, NIRMA University, Gujrat, India

Sunita Agrawal, Motilal Nehru National Institute of Technology, Allahabad, India

Suresh Satapathy, KIIT Deemed to be University, Bhubaneswar, India

Swagatam Das, Indian Statistical Institute, Kolkata, India

T. V. Vijay Kumar, Jawaharlal Nehru University, Delhi, India

V. K. Vyas, Sur University College, Oman

Vivek Jaglan, Dean Research, GEHU, Dehradun, India

16

Keynote Speakers

Swagatam Das

Indian Statistical Institute, Kolkata, India

Title of Talk: Deep Generative Adversarial Networks with Application to Class-

Imbalanced Learning

Abstract: Generative Adversarial networks (GANs) are by far the most intriguing

addition to the deep learning paradigm post 2014. For a given a training set, GANs learn

to generate new data with the same statistics as the training set. For example, a GAN

trained on photographs of particular kind can generate new photographs that look at least

superficially authentic to human observers, having many realistic characteristics. This

talk starts with an introduction to the working principle of GANs and illustrate a very

important application of GANs toward class imbalanced learning, where one or more

classes have very few representatives in the training sample. In particular, the talk

focuses on GAN based oversampling of the minority class. We also discuss an end-to-

end data level approach called Generative Adversarial Minority Oversampling (GAMO)

which can adaptively oversample the minority class(es) to mitigate the effects of class

imbalance on a deep image classifier. In essence, GAMO attempts to adversarially

connect a generator with a classifier. Such a relationship ensures that the generator will

be able to combat class imbalance by routinely supplying new difficult to classify

samples to the classifier.

The talk finally unearths a few future research avenues concerning GANs.

17

Maurice Clerc

Independent Consultant, France

Title of Talk: Iterative Optimisation: the questionable balance mantra

Abstract: In iterative optimisation a classical belief is that for efficiency a fine balance

between local intensive exploitation and global exploration should be achieved.

However, to date there is no rigorous approaches (theoretical or experimental) that

support it. We present here how exploitation and exploration can be defined and

measured in order to really monitor the progress of their ratio. Preliminary results show

that it may be far from what one would expect.

18

Mohammad Shorif Uddin

Jahangirnagar University, Bangladesh

Title of Talk: Prospects and Challenges of Deep Learning for Object Detection and

Recognition

Nowadays machine learning, in particular, the deep neural network has gained huge

popularity due to its outstanding performance in a wide range of applications in

computer vision for diverse image-based object detection, recognition, and classification

tasks. It bears competitive as well as the conflicting strategy that provides a way to learn

deep representations without extensive annotated training data. In this talk, 1) a brief

overview of different deep neural networks, such as the CNN, RNN and GAN will be

presented; 2) Applications, prospects as well as challenges of deep neural network for

different object detection, recognition and classification will be highlighted; 3)

Promising research directions and unsolved tasks will be discussed to serve as guidelines

for future work in solving practical problems.

19

Jonathan H. Chan

King Mongkut's University of Technology Thonburi Bangkok,

Thailand

Title of Talk: Innovative Cognitive Computing

With rapid advancement in AI, innovative cognitive computing (which we coined “IC2 -

I see too”) is needed to develop effective, efficient and safe technologies for the benefits

of mankind. Traditionally, cognitive computing is a sub-field of AI, as coined by IBM,

and typically involves the use of a computer system, such as IBM Watson

supercomputer, to aid in human decision making. However, the current trend is also

about extending AI by augmenting it with cognitive abilities of human and possibly other

entities. Innovative Cognitive Computing (IC2) integrates the inherently human attribute

of cognition with computing which is arguably superior using machines for

augmentation. The vision of IC2 is to enable us to “see” the (new) world in a different

light. This talk provides an overview of the latest innovative scientific research applied

to the multidisciplinary field of cognitive computing.

20

Andries Engelbrecht

Stellenbosch University, South Africa

Title of Talk: Particle Swarm Optimization for Large Scale Optimization

Abstract: It is known that standard particle swarm optimization (PSO) algorithms do not

scale well to large-scale optimization problems. As the number of dimensions of the

search landscape increases, the volume of the search space grows exponentially, and as a

result, the performance of standard PSO algorithms deteriorate significantly. While a

number of successful adaptations to the PSO have been developed to solve such large-

scale optimization problems, these approaches have been developed without first gaining

a clear understanding of the reasons why the PSO does not scale well. This presentation

will analyze the scalability of standard PSO algorithms with the main goal to identify the

reasons for its poor scalability. A number of approaches will be discussed to explore how

the curse of dimensionality can be addressed for the PSO.

21

Aninda Bose

Senior Editor, Springer, India

Title of Talk: Nuances and Tools of Scientific Publishing

Abstract: The importance of research publishing can be defined by a simple quote of

Gerard Piel, which says “Without publication, science is dead.” The first scientific

journal was published in 1665 and we have traveled 355 years since then. In the last 20

years, science and reporting of science have undergone revolutionary changes.

Computerization and the Internet have changed the traditional ways of reading and

writing. Hence, it is very important for scientists and students of the sciences in all

disciplines to understand the complete process of writing and publishing scientific papers

in good journals. There is also a downside of digital publishing. The principal challenge

for publishers is to handle ethical issues and it is of utmost importance for the authors to

understand the ethical practices involved in the process. The talk is designed to provide

information on different elements of publishing and also on how to make use of various

author services for the publishing work.

22

Meng-Hiot Lim

NTU Singapore

Title of Talk: Memetic Mission Management and Planning Framework

Abstract: In a broader context, a mission can be defined as a significant task with

specific objectives in mind. Typically, such a task involves the execution of a set of

operations or procedures. There are many tasks in the real-world, particularly in the non-

military domain that can benefit from coordinated and cooperative planning (CCP). To

achieve this, a platform for CCP should be scalable across applications or problem

domains, at the same time drawing upon reusable modules (APIs and memes) to

facilitate rapid prototyping of turnkey solution. In this talk, we discuss the overall

framework and illustrate using some practical scenarios.

23

Amir H Gandomi

University of Technology Sydney, Australia

Title of Talk: Evolutionary (Big) Data Analytics

Abstract: Evolutionary computation (EC) has been widely used during the last two

decades and has remained a highly-researched topic, especially for complex real-world

problems. The EC techniques are a subset of artificial intelligence, but they are slightly

different from the classical methods in the sense that the intelligence of EC comes from

biological systems or nature in general. The efficiency of EC is due to their significant

ability to imitate the best features of nature which have evolved by natural selection over

millions of years. The central theme of this presentation is about EC techniques and their

application to civil structures and infrastructures. On this basis, the presentation I about

an evolutionary approach called genetic programming for data mining. Applied

evolutionary computing in the data mining field will be presented, and then their new

advances will be mentioned such as big data mining. Here, some of my studies on big

data mining and modeling using EC and genetic programming, in particular, will be

presented.

24

Nishchal K. Verma

Indian Institute of Technology Kanpur, India

Title of Talk: Intelligent Health Monitoring of Machines: An Artificial Intelligence

Framework

Artificial Intelligence has emerged as a key player in the rapidly changing paradigm of

the fourth industrial revolution. This rapid transformation of industries can be attributed

to smart sensors. Data captured by these sensors can be processed and analyzed by AI to

achieve a vast array of objectives ranging from fault diagnosis to residual life prediction.

This keynote covers the future development of health monitoring systems based on the

principle of advanced AI techniques. A brief overview of emerging AI techniques,

including deep learning, transfer learning, and a few more, will be discussed. This

keynote will also share the novel trends of deep learning strategies and their applications,

particularly in the field of Machine health monitoring systems. This keynote will also

cover how to deal with the challenges of machine health monitoring using deep learning.

25

Akhil Ranjan Garg

J.N.V. University, Jodhpur, India

Title of Talk: Convolutional Neural Network: The Biologically inspired Deep

Neural Network

Abstract: Biological neural systems inspire convolutional Neural Networks (CNN's), like

other neural networks. However, these networks show a more considerable resemblance

to the biological system. The connectivity patterns of CNN's in Convolutional layers

resemble the connectivity pattern of the mammalian visual system. The recent success of

CNNs in accomplishing the tasks, as mentioned above, is attributed to the advancement

of high technology CPUs, GPUs, availability of massive amounts of data, and

development of new CNN architectures. This talk aims to introduce the participants with

the resemblance of CNN with the biological system, architecture, and working of the

primary Convolutional Neural Network. The talk will (i) describe the need to improvise

the underlying architecture of CNN's, (ii) throw light on different architectures of CNN,

(iii) provide the details on the strategies adopted for regularization in CNN's. Further, it

will discuss the concept of transfer learning and its specific application.

26

Dumitru Baleanu

Cankaya University, Turkey

Title of Talk: On fractional optimization and some applications

The fractional calculus has a fundamental role in better understanding of the dynamics of

complicated nonlinear dynamical systems. A short presentation of the properties of the

fractional calculus operators will be given. Besides, a novel sign fractional least mean

square algorithms are presented for ease in hardware implementation by utilizing sign

function to input data and estimation error corresponding to first and fractional-order

derivative terms in weight update mechanism of the standard approach. Finally, the

comparison of the results from true parameters of the model shows clearly the worth of

the scheme in terms of accuracy, convergence and robustness.

27

Special Tracks

1. Internet of Things, Cyber-Physical Systems, and Machine Learning: Systems and

Applications

Chair: Gonçalo Marques

2. Computational Intelligence based Techniques for Smart Communication System

Chairs: Seema Nayak, Amrita Rai, Rajiv Ranjan

3. Artificial Intelligence for Smart Healthcare

Chairs: Sowmya V, E.A. Gopalakrishnan, Vinayakumar Ravi, Soman K.P,

Chinmay, Chakraborty

4. Recent Advancement in Computer Vision and Multimedia Security

Chairs: Suneeta Agarwal, Shambhu Shankar Bharti

5. Applications of Emerging Information and Communications Technology trends in/to

the Cloud Computing, AI, and IoT Platforms

Chairs: Ahmad J Obaid, Alex Khang

6. Fusion of Artificial Intelligence and Blockchain Technologies

Chairs: Prakash Gopalakrishnan, Uma Maheswari B

7. Healthcare and Intelligent Systems

Chairs: Suma Dawn, Amlan Chakrabarti, Michael Sheng, Sateesh Kumar

8. Innovations in Internet of Things and Data Science

Chairs: Sachin Sharma, Anupriya Jain, Sushil Kumar

9. Intelligent Wireless Communication Systems and Biomedical Signal Processing

Chairs: Nikhil Marriwala, Sunil Sharma

10. Computational Intelligence in Big Data Analytics with Machine Learning and

Artificial Intelligence

Chairs: Ankur Saxena, Nicolas Brault

11. Computational Intelligence for Smart Technology

Chairs: K. Sujatha, N. Pappa, A. Kalaivani

28

Programme Schedule

Friday, 4th September 2020

International

Workshop

Saturday, 5th September 2020 Sunday, 6th September 2020

Opening Ceremony

09:00 AM – 10:00 AM

Keynote 9

9:30 AM – 10:15 AM

Opening Ceremony

09:30 AM – 10:00 AM

Keynote 5

10:00 AM – 10:45 AM

Keynote 10

10:15 AM – 11:00 AM

Keynote 1

10:00 AM – 11:00 AM

Tea/Coffee Break

10:45 AM- 11:00 AM

Tea/Coffee Break

11:00 AM- 11:15 AM

Tea/Coffee Break

11:00 AM- 11:30 AM

Keynote 6

11:00 AM – 11:45 AM

Parallel Session 17 to 24

11:15 AM – 12:45 PM

Keynote 2

11:30 AM – 12:30 PM

Tea/Coffee Break

11:45 AM – 12:00 PM

Tea/Coffee Break

12:45 AM – 01:00 PM

Tea/Coffee Break

12:30 PM – 01:00 PM

Parallel Sessions 1 to 8

12:00 PM – 01:30 PM

Keynote 11

01:00 PM – 01:45 PM

Keynote 3

01:00 PM – 02:00 PM

Lunch Break

01:30 PM – 02:00 PM

Lunch Break

01:45 PM – 02:30 PM

Lunch Break

02:00 PM – 03:00 PM

Keynote 7

02:00 PM – 02:45 PM

Parallel Session 25 to 30

02:30 PM – 04:00 PM

Keynote 4

03:00 PM – 04:00 PM

Keynote 8

02:45 PM – 03:30 PM

Tea/Coffee Break

04:00 PM- 04:30 PM

Tea/Coffee Break

03:30 PM- 03:45 PM Award Announcements

and

Valedictory Function Parallel Sessions 9 to 16

03:45 PM – 05:15 PM

Note: All the times are Indian Standard Time.

Keynote Speaker Title

Keynote 1 Swagatam Das Indian Statistical Institute, Kolkata, India

DEEP GENERATIVE ADVERSARIAL NETWORKS WITH APPLICATION TO CLASS-IMBALANCED LEARNING

Keynote 2 Maurice Clerc Independent Consultant France

ITERATIVE OPTIMIZATION: THE QUESTIONABLE BALANCE MANTRA

Keynote 3 Mohammad Shorif Uddin Jahangirnagar University, Bangladesh

PROSPECTS AND CHALLENGES OF DEEP LEARNING FOR OBJECT DETECTION AND RECOGNITION

Keynote 4 Jonathan H. Chan King Mongkut's University of Technology Thonburi Bangkok, Thailand

INNOVATIVE COGNITIVE COMPUTING

Keynote 5 Andries Engelbrecht Stellenbosch University, South Africa

PARTICLE SWARM OPTIMIZATION FOR LARGE SCALE OPTIMIZATION

Keynote 6 Aninda Bose Senior Editor, Springer, India

NUANCES AND TOOLS OF SCIENTIFIC PUBLISHING

Keynote 7 Meng-Hiot Lim NTU, Singapore

MEMETIC MISSION MANAGEMENT AND PLANNING FRAMEWORK

Keynote 8 Amir H. Gandomi University of Technology Sydney, Australia

EVOLUTIONARY (BIG) DATA ANALYTICS

Keynote 9 Nishchal K. Verma Indian Institute of Technology Kanpur, India

INTELLIGENT HEALTH MONITORING OF MACHINES: AN ARTIFICIAL INTELLIGENCE FRAMEWORK

Keynote 10 Akhil Ranjan Garg J.N.V. University, Jodhpur, India

CONVOLUTIONAL NEURAL NETWORK: THE BIOLOGICALLY INSPIRED DEEP NEURAL NETWORK

Keynote 11 Dumitru Baleanu Cankaya University, Turkey

ON FRACTIONAL OPTIMIZATION AND SOME APPLICATIONS

29

Details of Parallel Sessions

Date & Time Parallel Session Paper ID Session Chairs

5th September

2020

12:00 PM

To

01:30 PM

Parallel Session 1

(Internet of Things and Applications)

49, 102, 129, 200, 221,

283

Dr. Prashant Singh Rana

Dr. Mahendra Lalwani

Dr. Sachin Sharma

Parallel Session 2

(Swarm and Evolutionary

Computation)

3, 4, 5, 18, 183, 387

Dr. Amar Kishore

Dr. Seema Nayak

Dr. Shimpi Singh Jadon

Parallel Session 3

(Artificial Intelligence)

13, 122, 136, 155, 225,

235

Dr. Raju Pal

Dr. Sowmya V

Dr. Sandeep Dalal

Parallel Session 4

(Computer Vision)

90, 145, 149, 153, 188,

204

Dr. Himanshu Mittal

Dr. Suneeta Agarwal

Dr. Deepti Patole

Parallel Session 5

(Cloud Computing & Security)

74, 133, 147, 230, 297,

333

Dr. Sandeep Kumar

Dr. Ahmad J Obaid

Dr. Lokesh Chouhan

Parallel Session 6

(Computer Software and

Networking)

40, 96, 249, 281, 282,

295

Dr. Sumit Kumar

Dr. Prakash

Gopalakrishnan

Dr. Kusum Lata Agrawal

Parallel Session 7

(Big Data Analytics with Machine

Learning)

108, 116, 120, 139, 154,

156

Dr. Kusum Kumari

Bharti

Dr. Suma Dawn

Dr. Vishal Gupta

Parallel Session 8

(Intelligent Systems and Robotics)

10, 131, 250, 252, 317,

347

Dr. Anupam Yadav

Dr. Aakash Saxena

Dr. M. Nageswara Rao

30


5th September

2020

03:45 PM

To

05:15 PM

Parallel Session 9

(Internet of Things and

Applications)

138, 285, 359, 631,

661, 668

Dr. Neeraj Jain

Dr. Anupriya Jain

Dr. Hitendra Garg

Parallel Session 10


Computation)

395, 429, 594, 644,

650

Dr. Nirmala Sharma

Dr. K P singh

Dr. Soniya Lalwani

Parallel Session 11


239, 240, 246, 273,

298, 384

Dr. Avinash Pandey

Dr. Ayush Dogra

A. KALAIVANI

Parallel Session 12

(Computer Vision)

211, 216, 258, 294,

303, 338

Dr. Nitin Shukla

Dr. Shambhu Shankar

Bharti

Dr. Bindu Verma

Parallel Session 13

(Computer Vision)

614, 619, 624, 632,

659, 675

Dr. Vijay Bohat

Dr. Dusyant Kumar

Singh

Dr. Argha Sarkar

Parallel Session 14


Networking)

306, 331, 341, 389,

398, 464

Dr. Anirban Das

Dr. Nikhil Marriwala

Dr. Uma Maheswari B

Parallel Session 15

(Big Data Analytics with Machine

Learning)

162, 214, 229, 242,

370, 397

Dr. Ashish Tripathi

Dr. Ankur Saxena

Dr. Kamlesh Jangid

Parallel Session 16

(Intelligent Systems and

Robotics)

392, 410, 418, 427,

542

Dr. S. D. Purohit

Dr. K. Sujatha

Dr. Dhiraj

31


6th September

2020

11:15 AM

To

12:45 PM

Parallel Session 17


Computation)

472, 502, 662, 666,

676, 678, 680,

Dr. Shubham

Dr. Amrita Rai

Dr. Pinki Chauhan

Parallel Session 18


404, 406, 422, 431,

458, 461

Dr. Abhishek Verma

Dr. Chetna Gupta

Dr. Seema Nayak

Parallel Session 19

(Computer Vision)

363, 413, 456, 457,

469, 475

Dr. Himanshu Mittal

Dr. Devendra Kumar

Dr. Janki Ballab

Sharma

Parallel Session 20


Networking)

546, 570, 571, 575,

578, 603

Dr. Raju Pal

Dr. Rajitha B

Dr. Alex Khang

Parallel Session 21

(Big Data Analytics with

Machine Learning)

407, 428, 232, 434,

436, 460

Dr. Praveen Kumar

Shukla

Dr. Shambhu

Shankar Bharti

Dr. Himani

Parallel Session 22

(Big Data Analytics with

Machine Learning)

514, 545, 569, 589,

615, 527

Dr. Kusum Kumari

Bharti

Dr. R K Sharma

Dr. Amrit Pal

Parallel Session 23

(Intelligent Systems and

Robotics)

573, 598, 622, 648,

653, 655, 671

Dr. Ajay Sharma

Dr. D. Vanitha

Dr. Chinmay

Chakraborty

Parallel Session 24

(Computer Vision)

559, 560, 566, 579,

581, 605

Dr. Manish Gupta

Dr. Dhiraj Sangwan

Dr. Charu

32


6th September

2020

02:30 PM

To

04:00 PM

Parallel Session 25 (Artificial Intelligence)

462, 587, 601, 606, 639, 681

Dr. Sandeep Kumar Dr. Navnit Jha Dr. Nafis Uddin Khan

Parallel Session 26 (Computer Vision)

481, 482, 513, 518, 536, 555

Dr. Vijay Bohat Dr. Ramesh Chand Poonia Dr. Vijander Singh

Parallel Session 27 (Big Data Analytics with Machine Learning)

487, 488, 493, 508, 510, 515

Dr. Ashish Tripathi Dr. Suma Dawn Dr. R. K. Banyal

Parallel Session 28 (Big Data Analytics with Machine Learning)

629, 649, 658, 664, 670, 677

Dr. Avinash Pandey Dr. Lokesh Chauhan Dr. Jay Prakash

Parallel Session 29 (Intelligent Systems and Robotics)

50, 89, 101, 219, 342, 452, 592

Dr. Sumit Kumar Dr. D. K. Sambariya Dr. D. L. Suthar

Parallel Session 30 (Intelligent Systems and Robotics)

609, 621, 425, 465, 531, 572, 599

Dr. Deepak Bhatia Dr. Vivek Srivastava Dr. Dinesh Bisht

Note: Paper presentation time for each paper is 15 Min including Q&A.

33

Paper Details

PAPER

ID TITLE

3 ESCALATING CONVERGENCE IN DIFFERENTIAL EVOLUTION USING ADAPTIVE LOCAL SEARCH STRATEGIES

4 DESIGNING CONTROLLER PARAMETER OF WIND TURBINE EMULATOR USING ARTIFICIAL BEE COLONY ALGORITHM

5 DYNAMIC STABILITY ENHANCEMENT OF GRID CONNECTED WIND SYSTEM USING GREY WOLF OPTIMIZATION TECHNIQUE

10 DESIGNING OF SMART BIO-NANO ROBOTICS SYSTEM FOR CANCER CELL THERAPY AND EFFECTIVE DRUG DELIVERY

13 ARTIFICIAL INTELLIGENCE BASED POWER QUALITY IMPROVEMENT TECHNIQUES IN WECS

18 CHAOTIC HENRY GAS SOLUBILITY OPTIMIZATION ALGORITHM

40 A COMPARATIVE ANALYSIS ON WIDE AREA POWER SYSTEM CONTROL WITH MITIGATION THE EFFECTS OF IMPERFECT MEDIUM

49 ADWARE ATTACK DETECTION ON IOT DEVICES USING DEEP LOGISTIC REGRESSION SVM (DL-SVM-IOT)

50 ROBOTIC ARM BASED STORAGE AND RETRIEVAL SYSTEM

74 INTRUSION DETECTION SYSTEM FOR SECURING COMPUTER NETWORKS USING MACHINE LEARNING: A LITERATURE REVIEW

89 8-BIT ALU WITH PCB IMPLEMENTATION

90 VISION BASED SYSTEM FOR VEHICLE DETECTION AND TRACKING SYSTEM

96 AN EXPLORATION OF ENTROPY TECHNIQUES FOR ENVISIONING ANNOUNCEMENT PERIOD OF OPEN SOURCE SOFTWARE

101 RASPBERRY PI BASED SMARTPHONE

102 IOT SECURITY: A SURVEY OF ISSUES, ATTACKS AND DEFENCES

108 OCCURRENCE PREDICTION OF PESTS AND DISEASES IN RICE OF WEATHER FACTORS USING MACHINE LEARNING

116 A LITERATURE REVIEW ON GENERATIVE ADVERSARIAL NETWORKS WITH ITS APPLICATIONS IN HEALTHCARE

120 A FEDERATED SEARCH SYSTEM FOR ONLINE PROPERTY LISTINGS BASED ON SIFT ALGORITHM

122 DIGITAL BRAIN BUILDING A KEY TO IMPROVE COGNITIVE FUNCTIONS BY AN EEG–CONTROLLED VIDEOGAMES AS INTERACTIVE LEARNING PLATFORM

129 INTELLIGENT CAR CABIN SAFETY SYSTEM THROUGH IOT APPLICATION

131 UTILIZATION OF DELMIA SOFTWARE FOR SAVING CYCLE TIME IN ROBOTICS SPOT WELDING

133 DATA PROTECTION TECHNIQUES OVER MULTI-CLOUD ENVIRONMENT- A REVIEW

136 HIERARCHICAL ONTOLOGY BASED WORD SENSE DISAMBIGUATION OF ENGLISH TO HINDI LANGUAGE

138 REVIEW PAPER: ERROR DETECTION AND CORRECTION ONBOARD NANOSATELLITES

139 KERALA FLOODS : TWITTER ANALYSIS USING DEEP LEARNING TECHNIQUES

145 AN EMPIRICAL ANALYSIS OF HIERARCHICAL AND PARTITION BASED CLUSTERING TECHNIQUES IN OPTIC DISC SEGMENTATION

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147 AN IMPROVED DVFS CIRCUIT & ERROR CORRECTION TECHNIQUE

149 EMPLOYING DATA AUGMENTATION FOR RECOGNITION OF HAND GESTURES USING DEEP LEARNING

153 DETECTING THE NUCLEI IN DIFFERENT PICTURES USING REGION CONVOLUTIONAL NEURAL NETWORKS

154 EFFECTIVE PREDICTIVE MAINTENANCE TO OVERCOME SYSTEM FAILURES – A MACHINE LEARNING APPROACH

155 MODELLING SUBJECTIVE HAPPINESS WITH A SURVEY POISSON MODEL AND XGBOOST USING AN ECONOMIC SECURITY APPROACH

156 IN-LDA: AN EXTENDED TOPIC MODEL FOR EFFICIENT ASPECT MINING

162 IMBALANCE RECTIFICATION USING VENN DIAGRAM BASED ENSEMBLE OF UNDERSAMPLING METHODS FOR DISEASE DATASETS

183 PERSONALIZED ROUTE FINDING SYSTEM USING GENETIC ALGORITHM

188 EFFICIENT APPROACH FOR ENCRYPTION OF LOSSLESS COMPRESSED GRAYSCALE IMAGES

200 HAND GESTURE RECOGNITION SYSTEM USING IOT AND MACHINE LEARNING

204 DEEP LEARNING BASED FRAMEWORK FOR RETINAL VASCULATURE SEGMENTATION

211 A NOVEL UNIFIED SCHEME FOR MISSING IMAGE DATA SUGGESTION BASED ON COLLABORATIVE GENERATIVE ADVERSARIAL NETWORK

214 APPLICATION OF SOCIAL BIG DATA IN CRIME DATA MINING

216 A LOW-COST EMBEDDED COMPUTER VISION SYSTEM FOR THE CLASSIFICATION OF RECYCLABLE OBJECTS

219 A COMPARATIVE STUDY ON HANDWRITTEN DEVANAGARI CHARACTER RECOGNITION

221 IOT BASED RGB LED INFORMATION DISPLAY SYSTEM

225 DETERMINATION OF BREAKDOWN VOLTAGE FOR TRANSFORMER OIL TESTING USING ANN

229 CODE BUDDY: A MACHINE LEARNING-BASED AUTOMATIC SOURCE CODE QUALITY REVIEWING SYSTEM

230 A SOFTWARE REUSABILITY PARADIGM FOR ASSESSING SOFTWARE-AS-A-SERVICE FOR CLOUD COMPUTING

235 PERSONAL ASSISTANT FOR CAREER COACHING

239 MONITORING WEB-BASED EVALUATION OF ONLINE REPUTATION IN BARCELONA

240 DETECTION OF CARDIAC STENOSIS USING RADIAL BASIS FUNCTION NETWORK

242 TELUGU SCENE TEXT DETECTION USING DENSE TEXTBOX

246 INFORMATION TECHNOLOGY FOR THE SYNTHESIS OF OPTIMAL SPATIAL CONFIGURATIONS WITH VISUALIZATION OF THE DECISION-MAKING PROCESS

249 ELASTIC OPTICAL NETWORKS SURVIVABILITY BASED ON SPECTRUM UTILIZATION AND ILP MODEL WITH INCREASING TRAFFIC

250 RESIDUAL VIBRATION SUPPRESSION OF NON-DEFORMABLE OBJECT FOR ROBOT ASSISTED ASSEMBLY OPERATION USING VISION SENSOR

252 OPEN MV - MICRO PYTHON BASED DIY LOW COST SERVICE ROBOT IN QUARANTINE FACILITY OF COVID-19 PATIENTS

258 AN IMPROVED INCEPTION LAYER BASED CONVOLUTIONAL NEURAL NETWORK FOR IDENTIFYING RICE LEAF DISEASES

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273 EDUCATOR'S PERSPECTIVE TOWARDS THE IMPLEMENTATION OF TECHNOLOGY-ENABLED EDUCATION IN SCHOOLS

281 HYBRID APPROACH TO COMBINATORIAL AND LOGIC GRAPH PROBLEMS

282 AN AUTOMATIC DIGITAL MODULATION CLASSIFIER USING HIGHER-ORDER STATISTICS FOR SOFTWARE DEFINED RADIOS

283 AN IMPROVED MACHINE LEARNING MODEL FOR IOT-BASED CROP MANAGEMENT SYSTEM

285 A STUDY ON INTELLIGENT CONTROL IN PV SYSTEM RECYCLING INDUSTRY

294 IMPACTS OF ENVIRONMENTAL POLLUTION ON THE GROWTH AND CONCEPTION OF BIOLOGICAL POPULATIONS INVOLVING INCOMPLETE I-FUNCTION

295 ENERGY EFFICIENT ALGORITHMS USED IN DATACENTERS : A SURVEY

297 MALWARE ATTACKS ON ELECTRONIC HEALTH RECORDS

298 QUESTION ANSWERING SYSTEM USING KNOWLEDGE GRAPH GENERATION AND KNOWLEDGE BASE ENRICHMENT WITH RELATION EXTRACTION

303 ACCURACY EVALUATION OF PLANT LEAF DISEASE DETECTION AND CLASSIFICATION USING GLCM AND MULTICLASS SVM CLASSIFIER

306 DELIVERING NEWSPAPERS USING FIXED WING UNMANNED AERIAL VEHICLES

317 A STUDY ON DESIGN OPTIMIZATION OF SPUR GEAR SET

331 HYBRID CRYPTOGRAPHY ALGORITHM FOR SECURE DATA COMMUNICATION IN WSNS: DECRSA

333 A PERSPECTIVE OF SECURITY FEATURES IN AMAZON WEB SERVICES

338 TEXT-DOCUMENT ORIENTATION DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

341 SOFTWARE LOG ANOMALY DETECTION METHOD USING HTM ALGORITHM

342 BLUETOOTH CONTROLLED ARDUINO ROBOT

347 SENSORLESS SPEED CONTROL OF INDUCTION MOTOR USING MODERN PREDICTIVE CONTROL

359 AIR QUALITY MONITORING SYSTEM USING MACHINE LEARNING AND IOT

363 OPTIMIZATION OF REGULARIZATION AND EARLY STOPPING TO REDUCE OVERFITTING IN RECOGNITION OF HANDWRITTEN CHARACTERS

370 COMPARISON OF VARIOUS CLASSIFIERS FOR INDIAN SIGN LANGUAGE RECOGNITION USING STATE OF THE ART FEATURES

384 LOGICAL INFERENCE IN PREDICATE CALCULUS WITH THE DEFINITION OF PREVIOUS STATEMENTS

387 CONGESTION MANAGEMENT CONSIDERING DEMAND RESPONSE PROGRAMS USING MULTI-OBJECTIVE GRASSHOPPER OPTIMIZATION ALGORITHM

389 EFFECTIVENESS OF SOFTWARE METRICS ON RELIABILITY FOR SAFETY CRITICAL REAL-TIME SOFTWARE

392 A HARDWARE ACCELERATOR IMPLEMENTATION OF MULTILAYER PERCEPTRON

395 BUILDING A CLASSIFIER OF BEHAVIOR PATTERNS FOR SEMANTIC SEARCH BASED ON CUCKOO SEARCH META-HEURISTICS

397 TEXT CLUSTERING TECHNIQUES FOR VOICE OF CUSTOMER ANALYSIS

398 LOW PROFILE WIDE BAND MICRO STRIP ANTENNA FOR 5G COMMUNICATION

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404 ANALYZING THE IMPACT OF SOFTWARE REQUIREMENTS MEASURES ON RELIABILITY THROUGH FUZZY LOGIC

406 IMPACT OF QUASI-VARIABLE NODES ON NUMERICAL INTEGRATION OF PARAMETER-DEPENDENT FUNCTIONS: A MAPLE SUITE

407 SOCIAL NETWORK OPINION MINING AND SENTIMENT ANALYSIS: CLASSIFICATION APPROACHES, TRENDS, APPLICATIONS AND ISSUES

410 DYNAMIC ANALYSIS OF A NOVEL MODULAR ROBOT

413 A COMPARATIVE ANALYSIS ON EDGE PRESERVING APPROACHES FOR IMAGE FILTERING

416 TETRA Enhancement Based on Adaptive Modulation

418 DESIGN AND ANALYSIS OF MECHANICAL PROPERTIES OF SIMPLE CLOUD-BASED ASSEMBLY LINE ROBOTS

422 IMPACT OF DYNAMIC METRICS ON MAINTAINABILITY OF SYSTEM USING FUZZY LOGIC APPROACH

425 PROGRESS IN THE MATHEMATICAL MODELLING OF THE EMOTIONAL STATES

427 A TRIPOD TYPE WALKING ASSISTANCE FOR THE STROKE PATIENT

428 MODEL BASED DATA COLLECTION SYSTEMS ON FOG PLATFORMS

429 DATA ROUTING WITH LOAD BALANCING USING ANT COLONY OPTIMIZATION ALGORITHM

431 SPEECH EMOTION RECOGNITION USING MACHINE LEARNING TECHNIQUES

432 FEATURE BASED AD ASSESSMENT USING ML

434 NOTIFICATION SYSTEM FOR TEXT DETECTION ON DOCUMENT IMAGES USING GRAPHICAL USER INTERFACE (GUI)

436 ESTIMATION OF ROAD DAMAGE FOR EARTHQUAKE EVACUATION GUIDANCE SYSTEM LINKED WITH SNS

452 IOT BASED HOME VERTICAL FARMING.

456 IMPROVED VIDEO COMPRESSION USING VARIABLE EMISSION STEP CONVGRU BASED ARCHITECTURE

457 COMPARATIVE DESIGN ANALYSIS OF OPTIMIZED LEARNING RATE FOR CONVOLUTIONAL NEURAL NETWORK

458 SHOW BASED LOGICAL PROFOUND LEARNING DEMONSTRATES UTILIZING ECM FUZZY DEDUCTION RULES IN DDOS ASSAULTS FOR WLAN 802.11

460 ATMOSPHERIC TEMPERATURE PREDICTION USING ENSEMBLE DEEP LEARNING TECHNIQUE

461 RELIABILITY EVALUATION OF DISTRIBUTION SYSTEM BASED ON INTERVAL TYPE-2 FUZZY SYSTEM

462 DESIGN OF AUTOMATIC ANSWER CHECKER

464 COMMUNITY DETECTION USING FIRE PROPAGATION AND BOUNDARY VERTICES

465 COVID-19 RISK MANAGEMENT

469 VIDEO SURVEILLANCE SYSTEM WITH AUTO INFORMING FEATURE

472 HANDWRITTEN DEVANAGARI CHARACTER RECOGNITION USING CNN WITH TRANSFER LEARNING

475 INPUT PARAMETER OPTIMIZATION WITH SIMULATED ANNEALING ALGORITHM FOR PREDICTIVE HELEN-I ION SOURCE

37

481 TEXT RECOGNITION USING CONVOLUTION NEURAL NETWORK FOR VISUALLY IMPAIRED PEOPLE

482 LRSS-GAN: LONG RESIDUAL PATHS AND SHORT SKIP CONNECTIONS GENERATIVE ADVERSARIAL NETWORKS FOR DOMAIN ADAPTATION AND IMAGE INPAINTING

487 TWEETS REPORTING ABUSE CLASSIFICATION TASK: TRACT

488 ENHANCE THE PREDICTION OF AIR POLLUTANTS USING K-MEANS++ ADVANCED ALGORITHM WITH PARALLEL COMPUTING

493 TOWARDS GRAMMATICAL EVOLUTION BASED AUTOMATED DESIGN OF DIFFERENTIAL EVOLUTION ALGORITHM

502 EFFICIENT FUZZY SIMILARITY BASED TEXT CLASSIFICATION WITH SVM AND FEATURE REDUCTION

508 PLASMA DENSITY PREDICTION FOR HELICON NEGATIVE HYDROGEN PLASMA SOURCE USING DECISION TREE AND RANDOM FORESTS ALGORITHM

510 AUTOMATIC RECOGNITION OF ISL DYNAMIC SIGNS WITH FACIAL CUES

513 A BLOCKCHAIN BASED MULTI-LAYER FRAMEWORK FOR SECURING HEALTHCARE DATA ON CLOUD

514 COMVISMD - COMPACT 2D VISUALIZATION OF MULTIDIMENSIONAL DATA: EXPERIMENTING WITH TWO DIFFERENT DATASETS

515 ANALYSIS OF LIGHTWEIGHT CRYPTOGRAPHY ALGORITHMS FOR IOT COMMUNICATION

518 AN OPTIMAL FEATURE BASED AUTOMATIC LEAF RECOGNITION MODEL USING DEEP NEURAL NETWORK

531 LOAD EQUILAZATION TECHNIQUE AND SECURITY MECHANISM FOR CLOUD PERFORMANCE

536 DESIGN OF COMPACT SIZE TRI-BAND STACKED PATCH ANTENNA FOR GPS AND IRNSS APPLICATIONS

542 DISEASE PREDICTION FROM SPEECH USING NATURAL LANGUAGE PROCESSING AND DEEP LEARNING METHODS

545 INVESTIGATION ON ERROR CORRECTING CHANNEL CODES FOR 5G NEW RADIO

546 CLASSIFICATION OF HUMAN POSTURAL TRANSITION AND ACTIVITY RECOGNITION USING SMARTPHONE SENSOR DATA

555 SUPER RESOLUTION OF LEVEL-17 IMAGES USING GENERATIVE ADVERSARIAL NETWORKS

559 A DEEP LEARNING TECHNIQUE FOR AUTOMATIC TEETH RECOGNITION IN DENTAL PANORAMIC X-RAY IMAGES USING MODIFIED PALMER NOTATION SYSTEM

560 ANALYSIS OF THE MESSAGES FROM SOCIAL NETWORK FOR EMERGENCY CASES DETECTION

566 COLOR IMAGE WATERMARKING TECHNIQUE USING PRINCIPAL COMPONENT IN RDWT DOMAIN

569 QUERY AUTO-COMPLETION USING GRAPHS

570 COMPARATIVE ANALYSIS OF LOAD FLOWS AND VOLTAGE DEPENDED LOAD MODELING METHODS OF DISTRIBUTION NETWORKS

571 A FRAMEWORK FOR DISASTER MONITORING USING FOG COMPUTING

572 EFFICIENT MACHINE LEARNING ALGORITHM FOR CANCER GENOME CLASSIFICATION

573 ATTITUDE CONTROL IN UNMANNED AERIAL VEHICLES USING REINFORCEMENT LEARNING – A SURVEY

575 SELF-SUPERVISED LEARNING APPROACHES FOR TRAFFIC ENGINEERING IN

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SOFTWARE DEFINED NETWORKS

578 PASSIVE MOTION TRACKING FOR ASSISTING AUGMENTED SCENARIOS

579 IMPROVED IMAGE SUPER RESOLUTION USING ENHANCED GENERATIVE ADVERSARIAL NETWORK A COMPARATIVE STUDY

581 SMART LADY E-WEARABLE SECURITY SYSTEM FOR WOMEN WORKING IN THE FIELD

587 INTELLIGENT SIMULATION OF COMPETITIVE BEHAVIOR IN A BUSINESS SYSTEM

589 MINIMIZING THE SUBSET OF FEATURES ON BDHS DATASET TO IMPROVE PREDICTION ON PREGNANCY TERMINATION

592 A COMPARATIVE ANALYSIS OF SOFTWARE DEVELOPMENT MODELS FROM TRADITIONAL TO PRESENT-DAY APPROACHES

594 BLACK HOLE-WHITE HOLE ALGORITHM FOR DYNAMIC OPTIMIZATION OF CHEMICALLY REACTING SYSTEMS

598 AUTOMATED COOPERATIVE ROBOT FOR SCREWING APPLICATION

599 EVALUATION OF ELECTRIC VEHICLE CHARGING COST USING HOMER GRID

601 PUNER - PARSI ULMFIT FOR NAMED-ENTITY RECOGNITION IN PERSIAN TEXTS

603 A COVERAGE AND CONNECTIVITY OF WSN IN 3D SURFACE USING SAILFISH OPTIMIZER

605 MULTI CLASS SUPPORT VECTOR MACHINE BASED HOUSEHOLD OBJECT RECOGNITION SYSTEM USING FEATURES SUPPORTED BY POINT CLOUD LIBRARY

606 FLEXIBLE BOLUS INSULIN INTELLIGENT RECOMMENDER SYSTEM FOR DIABETES MELLITUS USING MUTATED KALMAN FILTERING TECHNIQUES

609 LINGUISTIC CLASSIFICATION USING INSTANCE BASED LEARNING

614 AN OPTIMAL FEATURE SELECTION APPROACH BASED ON IBBO FOR HISTOPATHOLOGICAL IMAGE CLASSIFICATION

615 DEEP LEARNING TECHNIQUE FOR PREDICTING OPTIMAL ‘ORGAN AT RISK’ DOSE DISTRIBUTION FOR BRAIN TUMOR PATIENTS

619 DETECTION OF PARKINSON’S DISEASE FROM HAND-DRAWN IMAGES USING DEEP TRANSFER LEARNING

621 AN AUTOMATIC EMOTION ANALYSIS OF REAL TIME APPLE MOBILE TWEETS

622 A FRACTIONAL MODEL TO STUDY THE DIFFUSION OF CYTOSOLIC CALCIUM

624 ADAPTION OF SMART DEVICES AND VIRTUAL REALITY (VR) IN SECONDARY EDUCATION

627 A CLASSIFICATION MODEL FOR SOFTWARE BUG PREDICTION BASED ON ENSEMBLE DEEP LEARNING APPROACH BOOSTED WITH SMOTE TECHNIQUE

628 MODELING THE RELATIONSHIP BETWEEN DISTANCE AND RECEIVED SIGNAL STRENGTH INDICATOR OF THE WI-FI OVER THE SEA TO EXTRACT DATA IN SITU FROM A MARINE MONITORING BUOY

629 SIGNAL PROCESSING TECHNIQUES FOR COHERENCE ANALYSIS BETWEEN ECG AND EEG SIGNALS WITH A CASE STUDY

631 DATA CLASSIFICATION MODEL FOR FOG-ENABLED MOBILE IOT SYSTEMS

632 INITIALIZATION OF MLP PARAMETERS USING DEEP BELIEF NETWORKS FOR CANCER CLASSIFICATION

639 A REVIEW ON DIMENSIONALITY REDUCTION IN FUZZY AND SVM BASED TEXT CLASSIFICATION STRATEGIES

644 MULTI-OBJECTIVE TEACHING–LEARNING-BASED OPTIMIZATION FOR VEHICLE FUEL

39

SAVING CONSUMPTION

648 ADAPTIVE FUZZY ALGORITHM TO CONTROL THE PUMP INLET PRESSURE

649 AN AUTOMATED CITRUS DISEASE DETECTION SYSTEM USING HYBRID FEATURE DESCRIPTOR

650 CLUSTERING HIGH DIMENSIONAL DATASETS USING QUANTUM SOCIAL SPIDER OPTIMIZATION WITH DWT

653 INTUITIVE CONTROL OF 3 OMNI-WHEEL BASED MOBILE PLATFORM USING LEAP MOTION

655 EFFECTIVE TEACHING OF HOMOGENOUS TRANSFORMATIONS AND ROBOT SIMULATION USING WEB TECHNOLOGIES

658 ABNORMAL EVENT DETECTION IN PUBLIC PLACES BY DEEP LEARNING METHODS

659 MAXIMUM POWER POINT TRACKING OF PHOTOVOLTAIC SYSTEM USING ARTIFICIAL NEURAL NETWORK

661 MULTIPURPOSE ADVANCED ASSISTANCE SMART DEVICE FOR PATIENT CARE WITH INTUITIVE INTRICATE CONTROL

662 A REVIEW OF NATURE-INSPIRED ALGORITHM-BASED MULTI-OBJECTIVE ROUTING PROTOCOLS

664 ASSESSING THE ROLE OF AGE, POPULATION DENSITY, TEMPERATURE AND HUMIDITY IN THE OUTBREAK OF COVID19 PANDEMIC IN ETHIOPIA

666 SOFT COMPUTING TOOL FOR PREDICTION OF SAFE BEARING CAPACITY OF SOIL

668 SMART SALINE MONITORING SYSTEM FOR AUTOMATIC CONTROL FLOW DETECTION AND ALERTNESS USING IOT APPLICATION

670 COMPARATIVE STUDY ON AUTOML APPROACH FOR DIABETIC RETINOPATHY DIAGNOSIS

671 DEVELOPMENT AND CONTROL OF A 7-DOF BIONIC ARM OF WITH DATA GLOVES AND EMG ARM BAND

675 A DEEP LEARNING BASED SEGREGATION OF HOUSING IMAGE DATA FOR REAL ESTATE APPLICATION

676 DESIGN OF DECISION SUPPORT SYSTEM TO IDENTIFY CROP WATER NEED

677 DESIGN & IMPLEMENTATION OF TRAFFIC SIGN CLASSIFIER USING MACHINE LEARNING MODEL

678 DEVELOPMENT OF INTER-ETHNIC HARMONY SEARCH ALGORITHM BASED ON INTER-ETHNIC HARMONY

680 HOPPING SPIDER MONKEY OPTIMIZATION

681 ELECTRIC LOAD FORECASTING USING FUZZY KNOWLEDGE BASE SYSTEM WITH IMPROVED ACCURACY

40

Abstract of Accepted Papers

Escalating Convergence in Differential Evolution using Adaptive Local

Search Strategies

Harish Sharma1, Prashant Sharma2 and Kavita Sharma3

1Rajasthan Technical University, Kota, 2Career Point University, Kota 3Govt Polytechnic College Kota

The differential evaluation (DE) algorithm is proved to solve the complex optimization problems that are difficult to solve by using the available deterministic methods. This paper presents an improved DE variant by incorporating three search strategies with the intention of balancing the diversification and convergence capabilities. After every DE search process, probability-based two local search strategies, namely Levy Flight Local Search (LFLS) and Classical Unidimensional Local Search (CULS) and one deterministic search, namely Stochastic Diffusion Scout Search (SDSS) is incorporated in the DE. The local search strategy contribute to improve in the convergence ability of the population while the SDSS helps to diversify the solutions in the search space. Proficiency of the newly proposed algorithm is established for 15 standard test problems of diverse characteristics and complexities. The competitiveness of the proposed variant is proved while comparing it against the DE and its recent variants through experimental results.

Artificial Intelligence Based Power Quality Improvement Techniques in

WECS

K G Sharma1, Nandkishor Gupta1, D. K. Palwalia2 and Mahendra Bhadu3

1Engineering College Ajmer, 2University Departments RTU Kota

3Engineering College Bikaner

In the past decade wind power capacity has experienced tremendous surge for a growing electrical energy demand. With generation of wind power, one of the employed promising technologies is direct driven permanent magnet synchronous generator (PMSG) with a full size back-to-back converter set. However, grid integration with Wind brings the problems of harmonic Distortion and voltage fluctuation. In this paper, AI based filter is placed between the network and wind system for reducing the total harmonic distortion (THD) which enhances the power quality during disturbances. The models of the AI based filter with wind turbine, PMSG, power electronic converters are implemented in Matlab/Simulink environment.

Chaotic Henry Gas Solubility Optimization Algorithm

Nand Kishor Yadav and Mukesh Saraswat

Jaypee Institute of Information Technology Noida Meta-heuristic algorithms mimic nature's optimization behaviour that has been used to solve real-world problems. Recently, Henry's law based Meta-heuristic algorithm, Henry gas solubility optimization algorithm, is presented. To improve its solution precision, a new variant, Chaotic Henry gas solubility optimization, is proposed in this paper. The proposed algorithm is validated on 47 benchmark functions of distinct modalities i.e., unimodal, multimodal, and fixed dimension multimodal in terms of mean fitness value and standard deviation. The experimental outcomes show that the proposed algorithm outperforms the existing algorithm.

41

Designing Controller Parameter of Wind Turbine Emulator using

Artificial Bee Colony Algorithm

Ajay Sharma1, Harish Sharma2, Ashish Khandelwal1 and Nirmala Sharma2

1Government Engineering College Jhalawar, India 2Rajasthan Technical University, Kota, India

In purview of limitedly available conventional energy sources, wind energy is the need of the day. It is available in plenty and is free of pollution as well. The present wind energy conversion systems (WECSs) consisting of wind turbines (WTs) are doing exceptionally well. To design a more accurate WT and efficient WECS is attracting researchers from the last many years. Further, looking at the size and remote location of WECSs it is not feasible to experiment for variation in their on-site experiments. That's why researchers are assuming WTs using motors in laboratories as wind turbine emulators (WTEs). So, it becomes an interesting problem to experiment a motor as WTE. In this paper, a separately excited DC (SEDC) motor is used to design a WTE. The parameters of PI controller installed in WTE are designed using artificial bee colony algorithm (ABC) algorithm. The ABC is selected as it is performing well to solve design optimization problems.

Dynamic Stability Enhancement of Grid Connected Wind System using

Grey Wolf Optimization Technique

Ashish Khandelwal1, Nirmala Sharma2, Ajay Sharma1 and Harish Sharma2

1Government Engineering College Jhalawar, India 2Rajasthan Technical University, Kota, India

To maintain the dynamic stability of a system is a significant problem in the field of grid connected wind system. The dynamic stability may disturb due to any type of fault or disturbance incurred in a wind system. To overcome this dynamic stability problem, in this article a wind energy converting system is used. Here, super conducting magnetic energy storage (SMEs) unit is applied with the considered system. The SMES controller is designed by a voltage source converter (VSC) and a chopper. This SMEs unit supplies active and reactive powers when required, thus balances frequency and voltage of the system which ultimately results in enhancement of dynamic stability of the system. In this work, a recent grey wolf optimization technique (GWO) is applied to optimize the duty cycle of DC-DC chopper. According to the GWO optimized duty cycle signal of chopper charging and discharging of SMEs unit has been controlled. The validation of GWO based control scheme is done with the help of MATLAB simulation results.

Robotic Arm Based Storage and Retrieval System

Pandian R

Sathyabama Institute of Science and Technology, India

In this work developed a robotic arm of six axis which is useful in pharmacies for storage and retrieval system in this project. The robot accepts the command given by the user and according to the instructions it brings the tablet box from the specified location and replace it back in the desired location on return command. ATMEGA 16A Microcontroller has been used to control the robotic arm. The keys are used as remote through which the desired locations are accepted by the microcontroller and the movement of robot arm is through the six motors which get signals that are sent by the microcontroller. Motor drivers are used to send signals to the motor. The position of the Robot arm in motion can be deducted through IR transmitters and Receivers.

42

Designing of Smart Bio-nano Robotics System for Cancer Cell Therapy

and Effective Drug Delivery

Deepak Bhatia


A bio-nano robotic system is defined as intelligent system which is capable of sensing, signalling, manipulation, information processing, and intelligence swarm behaviour at Nano-scale. Designing Nano robotic system deals with enormous variety of sciences, from quantum molecular dynamics, to kinematic analysis. Bio-Nano robot implies Nano robots which are built up by bio components. However there are so many difficulties, as well as complexities which are associated with using bio components. These bio components offer immense variety and functionality at a Nano scale and the advantages of using these are also quite considerable. Hence, these bio components seem to be a very reasonable and logical choice for designing of advanced nanorobots. This paper focuses on the highest developments in the field of designing of an intelligent bio-nano robotic system using molecular modelling techniques for diagnosis and treatment of cancer cell in surgeries, tumors and smart drug delivery inside the human body. Two different design approaches are used in molecular modelling technique i.e. Targeted Molecular Dynamics (TMD) and fuzzy logic structures (Mamdani structure), for diagnosis and efficiently identification of the defected cells and tumors. Takagi–Sugeno structure and Laser-Based Optical Tweezers (LBOT) were developed to cure, and treat the cells through delivery of effective delivery (dosages) of a drug. The designed system with simulated conditions validates and proved that the drug delivery of bio-nanorobots was robust to reasonable noise. Bio-nano robots is great hope for successful cancer cell therapy and effective drug delivery.

A Comparative Analysis on Wide Area Power System Control with

Mitigation the Effects of Imperfect Medium

Mahendra Bhadu1, K.G. Sharma2, D. K. Pawalia3 and Jeetendra Sharma4

1Engineering College Bikaner 2Engineering College Ajmer

3Rajasthan Technical University Kota 4The Imperial Electric Company, Kolkata

The increasing interest in wide area damping control poses major challenges for the reliable operation, control and stability of complete power systems. This study focuses on issues of wide area damping control and investigates the mitigation of the effect of imperfect communication medium in wide area damping control system. The imperfect communication medium is considered by including the process noise, measurement noise, and packet drop-out and signal latency in wide area remote signal. The residue method is taken into consideration for the selection of wide area control signal and controller location in wide area control system. The Modeling of imperfect communication network is done by considering the latency and random noises in wide area signal coming from remote location. The signal latency is modelled using third order of Pade approximation method and the random white noise is Gaussian in nature. The different control strategies are applied in wide area power system for mitigating the noise effects and overall stability enhancement of the complete grid. The performance of various controllers as wide area damping controller (WADC) as a power system stability agent is assessed using a typical test power systems via appropriate tools.

43

Adware Attack Detection on IoT Devices using Deep Logistic Regression

SVM (DL-SVM-IOT)

Arul Easwaramoorthy and Punidha Angusamy

Coimbatore Institute of Technology, Tamil Nadu

Malware is a daily imminent threat to businesses and users. It is a reality that defence systems today are unable to compete whether it is phishing emails or backdoor, exploits distributed across the web, together with numerous evasion strategies and other security vulnerabilities. The availability and the effectiveness of frameworks such as Veil, Shelter and others is known to be used by professionals for pen testing. Machine Learning can indeed be used for malware detection without the need for any signature or behavioural analysis. Using Support Vector Machine (Deep LR-SVM) to define such a firmware attack on IoT phones, deep logistic regression. A single output unit is called malicious or benign by training a DLR-SVM with multiple input clusters with a malicious or benign API. It was then trained to discover a malicious pattern in the unknown IoT firmware by Deep LR. The result shows that the truth positive rating is 98.11 percent, and that the adware attack is 0.07 percent positive.

Intrusion Detection System for Securing Computer Networks Using

Machine Learning: A Literature Review

Mayank Chauhan1, Ankush Joon1, Akshat Agrawal1, Shivangi Kaushal1 and Rajani Kumari2

1Amity University Haryana, Manesar, 2JECRC University, Jaipur

Network security is becoming very important for the networking society in recent years due to increasingly evolving technology and internet infrastructure. Intrusion detection system is primarily any security software, capable of identifying as well as immediately warning administrators in case somebody or something tries to access the network system by performing malicious practices. So, (Intrusion detection system) IDS are extremely important in order to provide security to network systems. It is a tool that attempts to defend the networks against a hacker. IDS is helpful not only to predict successful intrusions but also to monitor activities that attempts to breach security. This literature review aims to find out the importance of detecting intrusions using machine learning methods. This paper presents answers to questions like what machine learning techniques have been used so far for IDS, how effective these methods are for detecting intrusions in network systems, what are the demerits of previous studies and what areas are still open for research in this field.

8-bit ALU with PCB Implementation

Mayank Shrivastava, Shafali Jagga, Sarosh Jibreel, Shivam Singh, Shaheen Akhtar

Inderprastha Engineering College, Ghaziabad, Uttar Pradesh 201010 This paper aims to design an 8-bit ALU on a set of breadboards which can be used as a tool for learning and understanding the practical working of the basic concepts of electronics and electronic components by the junior students. The goal of this project is to design a simple system that covers the internal workings of a computer comprehensively and guides any user on how some of the earliest designs in the computing industry were made. Considering the learning objective, this microcomputer uses basic IC’s to compute output and follows a bus-oriented structure. This microcomputer makes use of 54 individual IC’s and over 250 resistors, capacitors and LED’s. After testing the microcomputer on breadboards, it is laid out for design on PCB and implement this circuit by the process of placement and routing on a PCB using KiCad Software tools.

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Vision Based System for Vehicle Detection and Tracking System

Kavitha N1 and Chandrappa D N2

1Dayananda Sagar Academy of Tech. and Management, Bangalore 2SJB Institute of Technology, Bangalore

Robust and reliable traffic surveillance system is one of the emerging concepts in the current technology, as it plays a vital role in intense monitoring of vehicle movement for efficient traffic management and safety which is one of the main concerns. Among which vehicle flow detection is the important part in surveillance system. The main draw- backs of the existing system is it consumes more time if video contains a high volume of information. Hence to overcome this drawback we intended to propose a traffic surveillance system for Vehicle detection and counting. Mixture of Gaussian (MoG2) model is proposed for extracting the efficient foreground. In addition to that Gaussian blur and morphological operation is implemented for efficient removal of noise present in the foreground segmented frames, followed by moving object detection using blob detection where vehicle is modeled as a rectangular patch from which meaningful features are extracted and identified as vehicle object through blob analysis. Finally the detected vehicles are been tracked and counted using multiple reference lines, where vehicles are been tracked by comparing the extracted features and measuring the minimal distance between consecutive frames through blob tracking. The experimental results show that the proposed system can provide real-time implementation for traffic surveillance.

An Exploration of Entropy Techniques for Envisioning Announcement

Period of Open Source Software

Anjali Munde Amity University Uttar Pradesh

At present, Machinery necessitate important characteristic software to attain novel apexes in relation to consistency, attribute and efficiency. Open Source Software (OSS) is apprised recurrently to encounter the necessities presented through the consumers. The source encryption of OSS experiences numerous alteration to disperse latest elements and apprise present elements in the structure, delivering an operator responsive edge. Through the rising intricacies of the software, the quantity of probable bugs is furthermore growing promptly. These bugs hamper the prompt software improvement series. Bugs, if deferred unanswered, may initiate complications in the elongated track. Moreover, with no former information around the position and the quantity of bugs, administrators might not be competent to assign supplies in a beneficial way. In order to affect this trouble, investigators have formulated abundant bug estimation methods till now. The existence of bugs in the software is essentially because of the uninterrupted variations in the software encryption. The constant alterations in the software encryption create the encryption complicated. The difficulty of the code alterations have already been measured in relations of entropy as trails in Hassan. These source encryptions practice periodic variations in order to encounter the novel characteristic introduction, characteristic improvement and faults fix. A significant part of concern for OSS is when to announce a latest edition. In this paper, a method by assuming the quantity of faults documented in numerous announcements of Bugzilla software has been established and distinctive degrees of entropy specifically, Shannon entropy and Kapur entropy aimed at variations in several software revisions during interval periods has been computed. A simple linear regression is employed initially to forecast the faults that are still impending. By means of these anticipated faults and entropy degrees in multiple linear regression, the announcement period of the software has been forecasted. Data visualization using Python has been elucidated. The outcomes are significantly effective for the software administrators to announce the edition on that interval. The outcomes of projected versions through the prevailing in the texts are evaluated and discovered that the projected simulations are beneficial fault forecaster since they have exhibited substantial enhancement in their operations.

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Raspberry Pi Based Smartphone

Samanta Sachdeva

Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

This paper presents the implementation of a smartphone designed using Raspberry Pi, which was launched in 2012 and its popularity has grown ever since. The proposed system is cost-effective and can perform a variety of functions. It would have a different GUI, its own merits and demerits and provide the users all the features that they desire in a smartphone. Such a device has never been available on a commercial scale before, but if built professionally, a phone based on Raspberry Pi could compete with the phones working on other operating systems in terms of cost and ease of usage.

Digital Brain Building a Key to Improve Cognitive Functions by an EEG–

Controlled Videogames as Interactive Learning Platform

P. K Parthasarathy, Archana Mantri, Amit Mittal and Praveen Kumar

Chitkara University, Punjab, India

This investigation provides a methodical review of Electroencephalography (EEG) oriented Brain-Computer Interfaces (BCI) integrated learning platform through videogames, a vast field of research that gives a path through for all questions concerning the future direction of BCI–Games. We tried to develop a basis of hypothesis on upgrading the mental skill through digital brain-building therapy. Everyone overcomes problems of any current situation through various debates but at the same time the difficulty faced tends us to fail. It is all that you do not have the specific skill, but our brain is not been trained to solve the problem that you face on the day to day life. Digital brain-building therapy will be as easy as taking a medicine for which gaming would be the indirect approach for training. The performance of the training through gaming is based on the psychological state of the person assigning the situation during the course of training. The paper tests the process of BCI published studies integrating through video games and describes that the video game platform offers plenty of benefits. One game application is been used to examine future directions.

A Literature Review on Generative Adversarial Networks with its

Applications in Healthcare

Viraat Saaran, Vaishali Kushwaha, Sachi Gupta and Gaurav Agarwal

Raj Kumar Goel Institute of Technology, Uttar Pradesh

Since the discovery of neural networks there is a trend of training the data (Structured or Unstructured) and predicting the outcome based on these training sets. Whereas this new neural networks, Generative Adversarial Networks or GAN’s are achieving popularity because of its capability to produce new data instead of classifying them. It operates on two neural networks fighting against one another that would be able to co-train through plain old back propagation. Its adaptive learning allows it to generate new data without replicating the previous outcome. Due to these advantages GANs have opened wide range of applications in medical and healthcare sector. Essential applications like image segmentation, image-to-image translation, style transfer and classification are getting more recognized. Given the growing trend of GANs in medical community, we present an overview of Generative Adversarial Networks with its potential applications in medical field. The main objective of this briefing is to investigate and provide the descriptive view of GANs and its applications in healthcare sector. This paper also makes an effort in identifying GANs’ advantages and disadvantages. Finally, we conclude the paper with future scope and conclusion.

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Occurrence Prediction of Pests and Diseases in Rice of Weather Factors

using Machine Learning

Sachit Dubey1, Raju Barskar1, Anjna Jayant Deen1, Nepal Barskar2, Gulfishan Firdose Ahmed3

1University Institute of Technology, RGPV, Bhopal (M.P)

2Department of Computer Science and Engineering, Institution of Engineering and Technology, DAVV Indore (M.P.)

3JNKVV, College of Agriculture, Powarkheda, Hoshangabad (M. P.)

Rice is one of the major cash crops in India and has been eaten in every part of the Indian subcontinent in every shape or form. In terms of production, India is one of the top producers of rice in the world along with China. Every year farmers lose a large amount of their produce to pest and disease infection. Often the change in climatic conditions favors the development of pest and disease. In this research paper, we discuss the possibility of using machine learning techniques to identify the climatic conditions which are favorable to pest and disease associated with rice. We also propose to develop a model which will identify whether a given weather condition will support the occurrence of pest and disease.

A Federated Search System for Online Property Listings Based on SIFT

Algorithm

Mohammad Chuttur and Yashi Arya

University of Mauritius, Mauritius

Facilitating access to accurate real estate’s information can contribute positively in the social and economic development of developing countries. Recently, in Mauritius, it has been observed that despite the increasing number of real estate agents marketing their products on the Internet, potential buyers are still facing difficulties in obtaining relevant information due to several duplications with varying information observed across multiple real estate platforms. Such a situation can leave buyers confused and reluctant to take a decision, thereby preventing progress. In an attempt to help buyers obtain adequate information about a property advertised online, we propose a federated search system, based on the SIFT algorithm, that can aggregate and display similar real estate property listings from multiple websites into one location. Buyers are able to then view all the details for the same property in a single location to decide which online platform to visit and pursue with their quest for a property acquisition. We present here the architecture, implementation and evaluation results of our proposed system.

Intelligent Car Cabin Safety System Through IoT Application

Rohit Tripathi, Nitin K and Honey Pratap

Galgotias University, Greater Noida, Uttar Pradesh, India

In present study, focus has been made on car cabin or driver safety and monitoring. Safety is the main concern of many industries and countries while designing a car. This study aims to develop a smart driving system to attain a safe and done up traffic society for aged drivers. By analyzing the data of total number of road accident 35%-40% and it is due to drowsiness or dizziness so to mind this issue, an attempt has been taken to solve this problem. Here, one hardware has been de-signed with help of sensors: alcohol sensor (MQ3), smoke sensor

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(MQ135), vibration sensor (SW-420) and gyroscope and IP Camera as well as GPS Module (GY-GPS6MV2). Raspberry pi has been used as microcontroller. It comes up with a solution of monitoring, alarming and minimizing entirely the respective percentage of accident due to mentioned reason. It is seen that the maximum drowsiness has been found in driver in evening time period whereas it gives alcohol drunk level with accuracy of ±0.05 mg/L. Every driver can be monitored and diagnosed with the help of data using sensors, GSM & GPS and shared over the internet using cloud to stop situation any mishappening while on road.

IoT Security: A Survey of Issues, Attacks and Defences

Vinesh Jain and Jyoti Gajrani

Engineering College Ajmer

Internet of Things (IoT) is the disruptive technology used in computer automation. In IoT things with embedded electronics hardware are connected to the Internet. Connecting IoT devices to the Internet, IoT security becomes an important issue. In this paper, we explain the recent case studies of attacks on IoT devices and networks along with vulnerabilities behind each attack. We enlist reasons behind these attacks along with preventive measures to make the IoT devices and network more secure. The paper aims to attract the attention of the research community towards IoT Security.

Employing Data Augmentation for Recognition of Hand Gestures using

Deep Learning

Deepak Kumar, Abdul Aleem and Manoj Madhava Gore

Motilal Nehru National Institute of Technology, Allahabad

Hand gestures are a form of non-verbal communication. Apart from traditional input devices, hand gestures are used for interaction with computers too. Communication via hand gestures finds many applications in the real world. Different people have hands with different shapes and orientations, which is termed as non-linearity. The non-linearity affects the performance of hand gesture models. A Convolutional Neural Network (CNN) is an approach of neural networks, specifically known as deep learning. CNN is used to recognize and classify images. Sometimes, CNN could not correctly understand the hand gesture due to non-linearity. Data augmentation helps CNN to understand the non-linearity and complexity of images better. Data augmentation generates enormous data from lesser data, thus increasing the data adversity. Data augmentation uses various operations like zooming, rotating, shifting, shearing, scaling, etc. to generate more data from existing data. This article executes a CNN model using augmented data for recognition of static hand gestures. The dataset consists of 10 different hand gestures. The experimented CNN model has been trained using 10000 images and tested using 1000 images. The changes in the output of CNN with and without data augmentation has been highlighted. The CNN model employing data augmentation achieved an accuracy of 98.10%; whereas, the CNN model excluding the data augmentation process attained an accuracy of 94.90% only.

Utilization of Delmia Software for Saving Cycle Time in Robotics Spot

Welding

Harish Kumar Banga, Parveen Kalra and Krishna Koli

PEC Chandigarh, India

In automobile manufacturing industry Resistance spot welding is widely used. Car’s body is built by welding sheets of metal. In industry common application where robots are being used

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is spot welding. In this paper robot movement between two welding points, Path followed while spotting, gripping and pay load carrying activities, number of holds moves and possibility to enhance interaction between four robots were analyzed on offline robot simulation software ‘Delmia V5’. Body shop assembly line has 4 Fanuc robots that perform about 209 welding spots in 532 seconds. After modification and proper sequencing 12.7% reduction in cycle time was observed.

Data Protection Techniques Over Multi-Cloud Environment - A Review

Rajkumar Chalse and Jay Dave

Indus University, Ahmedabad, Gujarat

Data protection is an essential and important process in a multi-cloud environment. Researchers proposed various techniques for data protection. This paper reviews recent literature (2015–2020) on data protection. Literature reported various techniques to data protection using Data Protection as a Service (DPaaS). Researchers explored various frameworks for data protection like Secure Data Storage (SCS), live migration, intrusion detection systems (IDS), Secret Sharing Made Short, and ESCUDO-CLOUD. Researchers also explored various approaches to machine learning and existing cloud services. DPaaS is a dynamic service for protecting data in the cloud. In this paper, we analyze various data protection techniques explored by authors in multi-cloud environments. By reviewing this we find DPaaS is not the only a solution for protecting data in a multi-cloud environment. The hybrid or combined approach using machine learning, physical parameter retrieval, and client-based validation is recommended for improvement of various frameworks leveraged across the platforms.

Hierarchical Ontology Based Word Sense Disambiguation of English to

Hindi Language

Shweta Vikram

BBA University Lucknow, India

The research carried in this paper clearly reveals that there are numbers of issues when it comes to question paper translation which should be effectively handled by applying suitable approaches and WSD algorithms in order to have an MT system which could be used for practical purposes. The further study and the analytical work carried in the present research to develop an efficient machine translation system would greatly reduce the dependency on human experts in translating questions into different Indian languages for various exams that require bilingual papers. This paper proposes an algorithm based on a hierarchical ontology which uses a tree structure. It uses a bilingual dictionary for the purpose. Corresponding to each English word respective Hindi words are assigned weights using the training data weights of the terms are updated by using TF (Term Frequency). Use of Hierarchical structure reduces the time of translation while ambiguity was also reduced. The experiment was done on a real dataset of questions of English language of NCERT and Other Source.

Review Paper: Error Detection and Correction onboard Nanosatellites

Caleb Hillier and Vipin Balyan

Cape Peninsula University of Technology, Symphony Way, Bellville, Capetown, South Africa

The work presented in this paper forms part of a literature review conducted during a study on error detection and correction systems. The research formed the foundation of understanding, touching on space radiation, glitches and upsets, geomagnetism, error detection and correction

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(EDAC) schemes and implementing EDAC systems. EDAC systems have been around for quite some time and certain EDAC schemes have been implemented and tested extensively. However, this work is a more focused study on understanding and finding the best-suited EDAC solution for nanosatellites in low earth orbits (LEO).

Kerala Floods : Twitter Analysis using Deep Learning Techniques

Chetana Nair and Bhakti Palkar

KJ Somaiya College of Engineering, Mumbai, Maharashtra

As technology and internet evolved, social media has become very important and unavoidable in our lives. Even during crisis or natural calamities people make use of social media to communicate more than any other means of communication. As a result, these social media platforms contain huge amount of information related to such events or incidents. We can use this social media data to get a lot of information which can be further used to understand the events through various aspects. In this paper we have extracted Kerala Floods related tweets from Twitter and classified them into various categories using natural language processing models like Bidirectional Encoder Representations from Transformers (BERT), XLNet, Ernie 2.0. It was observed that Ernie 2.0 gave better results compared to the other natural language processing models used for classifying the extracted tweets. These automatic text classification models designed using the above deep learning techniques can be very helpful in providing information while designing the solution and preventive measures for natural disasters like floods. Analysis of these tweets can also provide cognizance in handling such calamities in a better and efficient way.

An Empirical Analysis of Hierarchical and Partition Based Clustering

Techniques in Optic Disc Segmentation

Prakash J and Vinoth Kumar B PSG College of Technology, Coimbatore, Tamil Nadu

Optic disc segmentation in fundus image is a significant phase in diagnosis of eye disease like diabetic retinopathy and glaucoma. Segmenting the portion of optic disc which is bright yellowish in color is called as optic disc segmentation Automated optic disc segmentation is essential to diagnosis the eye disease at the earliest stage to prevent the eye sight loss. Segmentation of optic disc can be performed using clustering techniques. In this work hierarchical and partition-based clustering techniques are used to segment the optic disc. Five datasets namely: DIARETDB1, CHASE DB, HRF DB, INSPIRE DB and DRIONS DB are used to evaluate the clustering techniques. A comparative study was made based in the results using the performance parameters like Accuracy, Error Rate, Positive Predicted Value, Precision, Recall, False Discovery Rate and F1 score. The results show that the hierarchical clustering technique proves to be better than partition based clustering for the all considered datasets.

An Improved DVFS Circuit & Error Correction Technique

Keshav Raheja, Rohit Goel and Abhijit Asati

Birla Institute of Technology & Science, Pilani, India

Dynamic Voltage & Frequency Scaling (DVFS) is useful for low power digital circuit design. The work proposes a novel DVFS module offering any finer clock frequency change to produce an appropriate supply voltage to feed a digital circuit driven by DVFS module. In DVFS with varying supply and clock conditions the chances of setup and hold timing violations in D flip flop (DFF) circuit may increase. The DVFS module driving a digital circuit utilizing Razor D flip flop are used to correct errors occurring due to timing violations.

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The proposed circuit simulation shows that DVFS module driving simple D flip flop shows error due to timing violations while the DVFS module driving Razor D flip flop shows the correct operation. In the digital pipelined circuits any occurrence of timing violations, the Razor DFF uses the error correction mechanism to prevent data loss with a penalty of one additional clock cycle.

Detecting the Nuclei in Different Pictures Using Region Convolutional

Neural Networks

Naiswita Parmar

Indus University, Ahmedabad, Gujarat

Recognizing the cells’ nuclei at the initial stage is the most crucial process for most examinations. Reason for this being the fact that, a human body comprises about 30 trillion cells and each of them encompass a nucleus, which is brimming with DNA, the hereditary code that programs every cell. Recognizing nuclei enables researchers to distinguish every individual cell under investigation, and by estimating how cells respond to different medicines, the scientist can comprehend the fundamental biological procedures at work. Envision accelerating research for pretty much every infection, from lung malignant growth and coronary illness to uncommon disorders, by automating the detection of nuclei this vision can be achieved and we can speed up the entire process. We propose to use image segmentation technique to achieve this using Region convolution neural network algorithm. We will be improving this algorithm specifically for detecting microscopic nuclei in im-ages which varies in size and modality. Our dataset contains large number of nuclei images which were gained under varied conditions such as different imaging methodology (bright field versus fluorescence), type of cells and magnification level. Training data is selected in a way that maximizes algorithms ability to detect nuclei from these generalized images.

Effective Predictive Maintenance to overcome System Failures – A

Machine Learning Approach

Sai Kumar1, Nagendra Panini Ch2 and Shyam Mohan1

1Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya, Tamil Nadu, India

2Shri Vishnu Engineering College for Women, Kovvada, AP, India

As industry is getting advanced day by day incorporating new equipment’s on a large scale, there is a need to predict the machine lifetime in order to support the supply chain management. There are many ways of substitutions or upgradations required for any machine over a certain period of time, where its maintenance has become a major challenge. This problem is solved by building an effective predictive maintenance system which provides an intense spotlight for all types of machine industries. The log data is collected from the daily system activity from machines through deployment of various sensors facilitated to monitor the current state of equipment. A huge volume of numerical log data set is analyzed by the system for preparing the time series data for training and analyzing the model. The further intermediate steps are involved in bypassing the anomalies and fetching the clean data. Further the model is fed for testing using the forecasting model focusing on restoration time of any machine. This paper is targeted with identifying and predicting failures of heavy machines, thus facilitating the predictive maintenance scenario for effective working of the machine at all situations. This work is implemented by LSTM network model for gaining authentic results with numeric data which facilitates major cost savings and offers higher maintenance predictability rate.

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Modelling Subjective Happiness with a Survey Poisson Model and

XGBoost using an Economic Security Approach

Jessica Pesantez-Narvaez, Montserrat Guillen and Manuela Alcañiz

Universitat de Barcelona, Spain

The Living Conditions Survey of Ecuador contains a count variable measuring the subjective happiness of respondents. Two machine learning models are implemented to predict the level of happiness as a function of economic security among other factors. Even if the predictive performance is low, due to the fact that individuals tend to polarize extreme levels of happiness (either very low or very high), economic security is one of the most relevant determinants of a higher level of expected happiness, when we control for basic socio-demographic characteristics. Additionally, the analysis of missingness patterns in the target variable reveals some respondents’ characteristics at the time of self-reporting satisfaction.

IN-LDA: An Extended Topic Model for Efficient Aspect Mining

Nikhlesh Pathik and Pragya Shukla

Institute of Engineering & Technology (DAVV), Indore, Madhya Pradesh

In last decade LDA is extensively used for unsupervised topic modeling, and various extension of LDA has also been proposed. This paper presents a semi-supervised extension IN-LDA, which uses very few influential words related to the domain for providing supervision in the topic generation process. IN-LDA also improves the performance of the LDA generation process in two ways. First, it deals with multi-aspect terms by passing N-grams vectors and, the second simulated annealing based algorithm is used for tuning hyperparameters of LDA for more coherent output. The experiment is conducted on two popular datasets, movie reviews, and 20newsgroup. IN-LDA is showing improved results when compared with others on coherence value. It also shows a better interpretation of output due to influential words.

Imbalance Rectification using Venn Diagram based Ensemble of

Undersampling Methods for Disease Datasets

Soham Das, Soumya Deep Roy, Swaraj Sen and Ram Sarkar

Jadavpur University, Kolkata, West Bengal, India

Class imbalance is a major problem when dealing with real-world datasets, especially disease datasets. The majority class often consists of non-patient or negative samples while the minority class consists of patient, i.e., positive samples. This imbalance weakens the learning ability of a classifier as supervised learning is governed by classification accuracy, and the classifier disregards the minority class and identifies almost all the samples as belonging to the majority class. Researchers often use undersampling, oversampling or a combination of both techniques to address this skewness in datasets. In this paper, we use a novel method where we form a Venn diagram-based ensemble of different undersampling algorithms instead of relying on a standalone undersampling algorithm, thereby leading to a more robust model. The proposed method has been evaluated on three class imbalanced disease datasets namely, Indian Liver Patient dataset, Pima Indian Diabetes dataset and Cervical Cancer (Risk Factors) dataset. Experimental outcomes show that the ensemble approach outperforms some state-of-the-art methods considered here for comparison.

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Personalized Route Finding System Using Genetic Algorithm

Priyadharshini P and Karthikeyan Periasamy

Thiagarajar College of Engineering, Madurai, Tamil Nadu

The significant improvement of population and multiple types of vehicles causes traffic congestion and environment problem. The economic prosperity and continuing urbanization have led to quick raise in travel demand in urban areas. Rapid urbanization and motorization in cities cause congestion and an increase in vehicular emission and also increase the accident. Travelers face the practical problem of identifying the most efficient route on their daily journey. Fuel Consumption, emission, long travel time and accident can be direct and indirect consequence of vehicle traffic congestion and rough driving pattern. Personalized driving system needs some specific real time data like vehicle volume, weather condition of the area, road condition, carbon Emission factors and safety measures. Hence in this work, Genetic Algorithm (GA) is proposed to find the optimal route for the drivers based on their requirement. In this work, optimal path will be identified from several paths. Route maps of Madurai city, Delhi city, Northern utah city are considered. The GA result is compare to bellman ford algorithm. The GA finds the best route for 94.6% of accurately and bellman ford algorithm finds the shortest path for 92.4% of accurately. The results have proven that the proposed GA works better for the personalized route finding system.

Efficient Approach for Encryption of Lossless Compressed Grayscale

Images

Neetu Gupta and Ritu Vijay

Banasthali University, Vanasthali, Rajasthan

Transmission of images over a lower bandwidth communication medium with robustness against different attacks is a challenging task. Compression and encryption are the two important processes which provide transmission of images at lower bandwidth and security respectively. In this paper, Huffman lossless compression technique is used to compress the gray scale images. In this, different prefix codes of variable lengths are assigned to each pixel. The length of the prefix codes is inversely proportional to frequency of occurrence of characters. Compressed image is encrypted by RSA based asymmetrical en-cryption algorithm. In RSA based encryption algorithm, encryption key is kept public and different from decryption key which is kept secret. The efficiency of proposed algorithms is expressed by analyzing the compression parameters as well as the correlation coefficient analysis and entropy analysis. The results are verified on five gray scale test images of 512x512 sizes.

A Comparative Study on Handwritten Devanagari Character Recognition

Manoj Sonkusare, Roopam Gupta and Asmita Moghe

Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India

Handwritten text recognition is a challenging task because of the vast changes in writing styles. In India, a massive number of people use Devanagari Script to write their documents, but due to large complexity, research work accomplished on this script is much lesser as com¬pared to English script. Hence, recognition of handwritten Devanagari Script is amongst the most demanding research areas in the field of image processing. Feature extraction and recognition are key steps of OCR which affects the accuracy of the character recognition system. This paper gives a comparative study on distinct techniques used for feature extraction and classification by the researchers over the last few years.

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Hand Gesture Recognition System using IoT and Machine Learning

Ajay B N, Aditya S, Adharsha A S, Deekshitha N and Harshitha K

SJB Institute of Technology, Bengaluru, Karnataka

With the rapid advancement in the technology industry, innovation of newer technologies takes place at a rapid pace. Internet of things and Machine Learning being such niche domains with fast-paced development, it paves the way for improved usage of technology. Disability is one of the major issues that humanity faces, people with certain hearing and sight disabilities may not be able to use modern day computers with ease. Therefore the concept of hand gesture recognition was implemented. Built using the latest IoT Hardware devices, the concept of hand gesture recognition is implemented. Most of the standard available hand gesture recognition algorithms utilize image recognition and processing and may therefore increase in development complexity. To overcome this problem, we have come up with an application of hand gesture recognition using Arduino and machine learning implemented using support vector machines (SVM) for hand gesture classification with proper gesture recognition. The hand movements are captured using the MPU-6050, an accelerometer, and a gyroscope based Arduino IoT device that provides six-axis readings based on the movement performed and sent wirelessly to the computer via HC-06 Slave Bluetooth module. Once data is received by the device, ML is implemented using SVM and python’s open-source package PyGARL for gesture recognition. It is predicted that in 2020 alone, 4 Billion Bluetooth enabled devices will be sold. Hence making the hand gesture recognition device capable of connecting to any device near it and readily available to any user

Deep Learning based Framework for Retinal Vasculature Segmentation

Shambhavi Shikha Tiwari, Rajat Pandey, Akash Dholaria, Gauri Nigam, Rahee Walambe, Ranjana Agrawal and Ketan Kotecha

Symbiosis Institute of Technology, Pune

Retinal vessel segmentation aims to separate the structure of blood vessels from the fundus image background. This retinal vasculature is then used for detection of numerous diseases like Retinopathy of Prematurity (ROP), Glaucoma, Diabetic Retinopathy, Coronary Heart Disease, etc. Deep learning-based semantic segmentation techniques are considered a breakthrough in the field of medical diagnosis using artificial intelligence. Various methods for segmentation with convolutional neural networks have been developed which have become indispensable in tackling more advanced challenges with image segmentation. The limitation is that they all require huge quantities of labelled data which is difficult to collect. To overcome this, U-Net architecture is widely used for segmentation of medical images as it segments the pixels individually and can be trained with a small number of images. In this work, we have implemented U-Net architecture and evaluated it on two public datasets: ‘HRF’ and ‘DRIVE’. An accuracy of 96.64% was obtained on the DRIVE dataset and 94.28% on HRF dataset. To check the model robustness, we tested the model trained on the augmented DRIVE dataset on the HRF dataset and vice versa. The model trained on the augmented HRF training set achieves an accuracy of 95.04% when tested on the DRIVE dataset. Similarly, the model trained on the augmented DRIVE training set achieves an accuracy of 92.17% when tested on the HRF dataset. A progressive web application is also developed as part of this work, since there is no specific easy to use interface to perform segmentation of retinal fundus mentioned in the literature, to the best of our knowledge. This application accepts retinal fundus image as input and performs segmentation using trained U-Net model, to provide an output image of the blood vessels which will assist the ophthalmologists in screening retinopathy.

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A Novel Unified Scheme for Missing Image Data Suggestion Based on

Collaborative Generative Adversarial Network

Angeline R and Vani R

SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

An immense number of the applications are available that necessitate numerous system input data to bring about the wanted or desired results or system outputs. In this case, in case of any data that is lost or absent it announces a huge amount of favourism or unfairness in the spectrum of the output produced from within the method .Here we are presenting one algorithm that aids in creating more precise anatomically credible imageries of high dimensions according to acquired medical brain scans having a large gap between the spacing of two corresponding inner image slices. Even in spite of the fact that vast databases containing anatomical images which store a copious amount of data, anatomical procurement parameters produce a result in the form of scattered scans that tend to lose a large part of the anatomical image. The main ambition of this system is to be able to apply previously developed algorithms that were developed for fine resolution scans used for research purposes, to be applied on poorly sampled images. The algorithm alters the problem of anatomical image imputation to an image to image illustration translation task over multiple domains for the purpose that the generator part and the discriminator part of the network is able to recover the lost data from the remaining pure and unsoiled data accumulation. In this envisioned form of the system in place of producing common and general results, the generator part of the network trains itself to learn to generate a counterfeit sample result that is specifically parameterized along with particular conditions.

Application of Social Big Data in Crime Data Mining

Nahid Jabeen and Parul Agarwal

Jamia Hamdard, New Delhi

The increased fame of the internet and technology has motivated criminals a lot to commit their evil actions with the help of its various facilities such as social media, e-commerce, and so on. Nowadays, almost everything exists on the internet either to store or to access personal as well as professional data. This helpful nature of the internet and technology has proved a boon for criminals to find their vulnerable targets. It also provides many options for investigators and police officials to track and catch the criminals. The regular use of the internet has led to a huge amount of big data that made investigators and police officials hard to analyze and predict crime and criminals. But a combination of crime analysis and computer science or information technology can make it easier and user-friendly for investigators and police officials to fetch useful data from the social big data. By applying data mining approaches, we can help the investigative department to predict and prevent crime based on past crime data. The main focus of this paper is to perform an in-depth analysis of some cybercrimes that frequently occurred in the Union Territories of India per year from 2014 to 2018. Two open-source data mining tools namely RapidMiner and Weka are used with the application of the k-means clustering technique. The results of the two tools are compared and it is found that though Weka outperforms RapidMiner in execution time, but it lacks in Accuracy and visualization. But, in general, both help in finding an application for investigation and the police department in predicting patterns of crime occurrences.

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A Low-Cost Embedded Computer Vision System for the Classification of

Recyclable Objects

Karl Myers and Emanuele Lindo Secco

Robotic Lab, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, England

Due to rapid urbanization, increasing population and industrialization, there has been a sharp rise in solid waste pollution across the globe. Here we present a novel solution to this inefficiency, by the use of embedded Computer Vision (CV) in the Material Recovery Facilities (MRF). The proposed architecture employs software (i.e. Tensorflow and OpenCV) and hardware (i.e. Raspberry Pi) as an embedded platform in order to classify daily life objects according to their visual aspect. The CV system is trained using modules contained within the TensorFlow API with two datasets, namely the TrashNet and a combination of the TrashNet and a set of web images. This solution allows greater accuracy, with a baseline performance of 90% which drops to 70% when deployed on the embedded platform, due to the quality of the images taken by an integrated camera for the real-time classification. The speed results are also promising with a baseline speed of 10 FPS at simulation level, which drops to 1. 4fps when running on the platform. Such a system is cheap at less than £ 100, it is perfectly adequate to be used to identify recyclables in the MRF for sorting.

IoT Based RGB LED Information Display System

T Kavya Sree, V Swetha, M Sugadev and T. Ravi

Sathyabama Institute of Science and Technology, Tamilnadu

The Internet of Things (IoT) is an incompatible technology used in various applications. IoT is an interconnected network. Using IoT we can transfer data without human-to-human interaction. In the present era, in every field, we are using IoT. Normally for the dot matrix display, we are using a single-color LED light or RGB Leds. It consumes more power, occupies more space and heavyweight and not so flexible to program from user perspective. Using RGB LED’s we can produce different colors by adjusting the intensity of light using the PWM (Pulse Width Modulation) signal. In this paper, the design, implementation and cost optimization of an IoT controlled RGB display system. In this work, we are using the W60-75 RGB driver board to program the LEDs for the display system using IoT connectivity using Bluetooth and Wi-Fi protocols. This driver board comprising of built in modules of Wi-Fi modem, U disk port, and supports GUI based program tool. So instead of a complex programming procedure, it can also be operated by a simpler Mobile app called LED-Art for android operating system. This display system can be used to exhibit any information in transportation, colleges and in many other places.

Personal Assistant for Career Coaching

Arbaaz Khan, Vinit Masrani, Anoop Ojha and Safa Hamdare

St. Francis Institute of Technology, India

There has been an increasing hype and usage of chat bots now a days. If chat bots are used as career assistants who can guide an individual to the right track then this will motivate people to learn and acquire new skills. Currently there is no standard and proper resource which can prepare an individual pertaining to industry requirements. So, this project will help develop standard platform where users can get all the career related queries solved and also resources needed for industry. System is built with the aim to leverage the latest technologies to help job seekers, career aspirant and tech enthusiastic to realize their dream.

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A Software Reusability Paradigm for Assessing Software-as-a-Service for

Cloud Computing

Deepika and Om Prakash Sangwan

Guru Jambheshwar University of Science and Technology, Hisar, India

Cloud computing is a type of computing in which there is on demand availability of IT or computer system resources over the network as an online service. Virtualization is the main driving technology of cloud computing. With the increase in utilization of infrastructure, it speeds up IT operations and reduces cost. SaaS is generally operated and it gives numerous advantages to assist end users. To accomplish these advantages, it is important to calculate the reusability of SaaS and there is requirement to develop a reusability paradigm for evaluating SaaS for cloud computing. In this paper, we suggested an inclusive paradigm for assessing reusability of SaaS. We first describe key characteristics of SaaS. And then, we defined reusability attributes from the key character-istics, and derived metrics for the reusability attributes. Correlation measure is used for validating the reusability model of SaaS. This proposed paradigm can be used for evaluation and quality management of SaaS by service providers.

Determination of Breakdown Voltage for Transformer Oil Testing using

ANN

V. Srividhya1a, Jada Satish Babu1b, K.Sujatha1c, J. Veerendrakumar1c, M.

Aruna2a, Shaik Shafiya2b, SaiKrishna2c and M. Anand2d

1aDept. of EEE, Meenakshi College of Engineering, Chennai, India.

1bDept. of EEE., REVA University, Bangalore.

1cDept. of EEE, Dr.MGR Educational and Research Institute, Chennai, India.

2aDept. of EEE, Meenakshi College of Engineering, Chennai, India.

2bDept. of ECE, Nalla Narasimha Reddy education society, India.

2cDept. of ECE, Sree Rama Institute of Technology and Science, India.

2dDept. of EEE, Dr.MGR Educational and Research Institute, Chennai, India. Power transformer is an important apparatus in power system. Insulation used in transformer is of solid insulation system and liquid dielectrics. It is important to investigate the cause of the insulation degradation with respect to aging affects. The standalone parameters of transformer oil are analyzed for various Breakdown voltages. The absence of a well-defined scheme to correlate the various parameters of transformer oil reinforces the need this transformer oil testing studies. In this work, a change in the Break down voltage of insulating oil was observed and its interrelationship was determined using Feed forward Neural Network (FFNN) trained with Quasi Newton and Conjugate gradient learning methods. An expert system is created using FFNN implemented using MATLAB software to accurately determine the sensitivity and also the fault in system due to aging of transformer oil.

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Delivering Newspapers Using Fixed Wing Unmanned Aerial Vehicles

Varun Agarwal and Rajiv Ranjan Tewari

University of Allahabad, India

The advent of Unmanned Aerial Vehicles (UAVs) or Drones, as they are popularly known as, has opened a plethora of areas where they can be used and efficiently deployed to perform tasks that are otherwise redundant to humans. Companies across the world are contemplating the applications of UAVs in the delivery of items. We, in this paper, plan to incorporate a class of UAVs called Fixed Wing UAVs for the delivery of newspapers. Our aim is to determine the exact location where a newspaper parcel needs to be dropped from a UAV so that it reaches its intended destination. Forces of gravity and air drag have been considered in our calculations. Based on the permissible flying ranges, the weight of the newspaper parcel and the drag experienced, our equations calculate the point of parcel drop.

Code Buddy: A Machine Learning-Based Automatic Source Code Quality

Reviewing System

Nidhi Patel1, Aneri Mehta1, Priteshkumar Prajapati1 and Jigar Biskitwala2

1Charotar University of Science and Technology, Gujarat

2Capgemini, India

The growing complexity of software and associated code makes it difficult for the software developers to produce a high-quality code in a timely fashion. However, this process of assessing code quality can be automated with help of software code metrics, which is a quantitative measure of code properties. The software metrics consists of several attributes, which describe the source code this includes: lines of code, program length, the effort required, the difficulty involved, cyclomatic complexity, volume, vocabulary, intelligence count and so on. With the help of these features, code can be classified as well-written code or badly written code. This study focuses on evaluating the performance of main classification algorithms. The research work also focuses on understanding the math and working of each of the classifier and quality of each dataset. The best model is chosen along with the appropriate dataset. In order to allow the developers to use the trained model, we created Code Buddy a SharePoint web-portal; which allows the developers either assess the code quality by sending the review request to any of the colleagues or assess the code automatically using a trained model, which will predict whether the code is well written or badly written.

Monitoring Web-based Evaluation of Online Reputation in Barcelona

Jessica Pesantez-Narvaez, Francisco-Javier Arroyo-Cañada, Ana-María Argila-Irurita, Maria-Lluïsa Solé-Moro and Montserrat Guillen

Universitat de Barcelona, Spain

In the hotel sector, online reputation and customer satisfaction help measure the quality of service based on the opinions of the survey participants. This research takes information provided by TripAdvisor from a sample of 247 hotels in Barcelona in order to obtain users’ reaction to each establishment. A robust compositional regression is modelled to diagnose the score given to each hotel as a result of the customers’ profiles. Additionally, a principal component analysis is proposed to visualize customers’ behavioural patterns of scoring. The results let us detect a particular behaviour for the business travellers’ scale seen alongside other groups of tourists who also rated hotels with the top marks at this time. Furthermore, the score given by travellers who arrived during the summer are shown as not being significant to

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achieving a final high score. The proposed model can also be used to track the stability of score over time and to identify suspicious deviations from benchmark levels.

Telugu Scene Text Detection using Dense Textbox

Srinivasa Rao Nandam, Atul Negi and Koteswara Rao Devarapalli

University of Hyderabad, India

This paper presents an end-to-end trainable Scene Text Detector and a Script Classification Network to filter Telugu text. The Scene Text Detector named Dense Textbox efficiently makes accurate predictions using Densenet as a base architecture. It introduces K-Means clustering to set aspect ratios of default boxes and predicts at vertical offsets [-0.125,-0.25,0.125,0.25] similar to Textboxes and at a resolution of 512 × 512 to produce more text candidate proposals which are filtered using non-maximum suppression. SSD Focal loss is used instead of traditional SSD loss to accurately differentiate text regions from the background. Dense Textbox outperforms other similar frameworks on the IndicSceneText dataset using no preprocessing and standard ICDAR2013 dataset using a single pass at a single resolution. The framework has no problem in detecting text containing various scripts like the images present in IndicSceneText dataset. Traditional Scene Text Detection frameworks fail to classify the type of script Ex: Telugu, English present in an Image. We introduce a Script Classification network that is used to filter images that contain Telugu script from a non-Telugu script based on CRNN network, which uses both convolutional and recurrent networks to classify the type of script present. Classifying and filtering text regions helps to send the text regions to appropriate Scene Text Recognition modules. The network identifies the type of script with 92.4% accuracy.

Detection of Cardiac Stenosis using Radial Basis Function Network

G Indira Priyadarshini1, K.Sujatha1, B. Deepa Lakshmi1 and C. Kamatchi2

1ECE Dept., Dr. MGR Educational and Research Institute, Chennai.

1EEE Dept., Dr. MGR Educational and Research Institute,Chennai,

1ECE Dept., Ramco Institute of Technology, Rajapalayam.

2Dept of Biotechnology, The Oxford College of Science, Bangalore.

Worldwide, the cardiac stenosis is the number one cause of death among human community. The countries which are below the poverty line and having mediocre income experience nearly 80% of deaths due to cardiovascular disease. If the present status continues, approximately, about 20 million people will die due to coronary artery block by 2030. Effective decision making is the only solution the physicians can offer to detect the occurrence of heart disease so that the patients can be relaxed and can undergo treatment at appropriate time. The heart attack mainly occurs due to decreased blood and oxygen supply to the heart due to deposition of cholesterol on the sidewalls of the arteries causing heart disease. Conversely, there is a shortage in effective scrutiny tools to identify the concealed relationship in the data pattern. This proposed technique intends to provide a study of existing techniques to form the knowledge base which will guide the cardiologists to take an effective decision. The objective of this scheme is to diagnose the presence of various levels of block in coronary artery using the features extracted from the Computed Tomography (CT) Angiogram. Seven features with Radial Basis Function Network were used to measure the percentage of blocks in the coronary artery. This investigation proved that the data mining approaches like RBFN was used to measure the blocks in the coronary artery.

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Impacts of Environmental Pollution on the Growth and Conception of

Biological Populations Involving Incomplete I-function

D.L. Suthar1, S.D. Purohit2, A. M. Khan3 and Sushma Dave3

1Wollo University, Ethiopia,

2Rajasthan Technical University, Kota

3JIET Jodhpur, India

The paper concerned with the analysis of effects on environmental pollution and occurrence in biological populations by presenting an incomplete I-function mathematical model. The results demonstrated in this paper are common in mathematical sciences and show a variety of cases of interest in relevant parameter conditions.

An Improved Machine Learning Model for IoT-based Crop Management

System

Harish Sharma1, Ajay Saini2, Ankit Kumar3 and Manish Bhardwaj4

1Rajasthan Technical University, Kota,

2Arya Institute of Engineering and Technology, Jaipur

3Swami Keshvanand Institute of Technology, Management & Gramothan, Ramnagaria, Jagatpura, Jaipur, Rajasthan, India

4Poornima Institute of Engineering & Technology, Jaipur, India

Smart Farm is a management system that influences farmers in the highest demand for agriculture, which predicts the future of the field instead of the fruit of the field by learning the appropriate machine in your area, which is what the farmers want to produce, the year they want to know. This paper is aimed at the development of an IOT based smart agriculture system which can help and provide all information to farmers from their fields on mobile and web server, this system helpful for farmers to increases quality product and preserve the corps. The purpose of the article is to analyze the environmental parameters such as agricultural production zones, annual production rates and crop location to produce, which influences crop yields and establish relationships among these parameters. Typically, the farmers are still using traditional methods for monitoring their crop and agriculture field. To meet the end of the growing world needs, the farmers have to use new techniques of monitoring and tracking their crops which in turn help them by improvement in yield, reduction in farming cost, reduction in destruction to the environment and increase in the quality of the produce. Existing systems and methods of monitoring crops use wireless sensor network to collect data from the different sensors deployed at various nodes and send it through the wireless protocol. In this study, regression analysis (R.A.) is used to stress environmental factors and crop yields. In this search, the random forest classification method for the deduction and precision model used is 87.04%, therefore, improve the accuracy of the prediction of the hybrid model, which is a combination of linear regression and random matching, and 93.72% of the efficiency model.

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Accuracy Evaluation of Plant Leaf Disease Detection and Classification

using GLCM and Multiclass SVM Classifier

K Rajiv1, N. Rajasekhar1, K. Prasanna Lakshmi1, Dammavalam Srinivasa Rao2 and P Sabitha Reddy3

1Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad 2VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad

3St martin's engineering college Hyderabad One of the significant segments of Indian Economy is Agriculture. Occupation to almost 50% of the nation’s labour force is on condition that by Indian cultivation segment. India is known to be the world is biggest maker of pulses, rice, wheat, spices and spice crops. Agriculturalist is financial development rest on the excellence of the foodstuffs that they harvest, which trusts on the plant is development and the harvest they get. Consequently, in ground of farming, discovery of disease in plants shows a contributory character. Plants are extremely disposed to diseases that mark the growth of the plant which in chance marks the natural balance of the agriculturalist. The yield of crop drops due to contagions instigated by numerous types of illnesses on parts of the houseplant. Leaf illnesses are principally instigated by fungi, bacteria and virus etc. Verdict of the illness would be completed precisely and suitable activities should be occupied at the suitable period. Image Processing techniques are more significant in detection and classification of plant leaf disease, machine learning models and Principal Component Analysis, Probabilistic Neural Networks, Fuzzy logic etc. Proposed work designates in what way to notice plant leaf illnesses. The proposed scheme will deliver a profligate, natural, accurate and actual reasonable technique in identifying and categorizing plant leaf diseases. The proposal method is intended to support in the identifying and categorizing plant leaf illnesses using Multiclass SVM classification method. First, input image acquisition, Second, pre-processing of images Third, Segmentation for discovering the affected region from the leaf images by utilizing-Means clustering algorithm and then structures like (color, shape and texture) are mined for classification. Finally, classification technique is utilized in identifying the category of plant leaf disease. The projected method successfully identifies and also classifies the plant leaf illness with more correctness/ accuracy.

Information Technology for the Synthesis of Optimal Spatial

Configurations with Visualization of the Decision-Making Process

Sergiy Yakovlev1, Oleksii Kartashov1, Kyryl Korobchynskyi1, Oksana Pichugina1 and Iryna Yakovleva2

1National Aerospace University “Kharkiv Aviation Institute”, Ukraine 2National University of Urban Economy, Ukraine

The paper describes models and methods for solving the problem of optimal synthesis of spatial configurations of geometric objects. Special attention is paid to the issue of using modern information technologies, which allow automatically designing models of optimization problems and their subsequent solving by specialized software packages. It is proposed tools for visualizing geometric information throughout the solution process. To a decision-maker, the presented technology allows changing the current configuration interactively. This instrument is of particular importance since most of the problems under consideration are NP-complete, and there are no methods for treating them guaranteed finding the optimal global solution.

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Elastic Optical Networks survivability based on Spectrum Utilization and

ILP model with increasing Traffic

Suraj Kumar Naik Siksha 'o' Anusandhan University, India

Communication always demands growth in technologies to fulfill the requirement of bandwidth in a network. Requirements like multimedia, cloud services, high use of internets which are exponentially increasing with respect to time. Due to this high requirement of bandwidth, it is always a challenge to maintain the survivability of the existing optical networks for a defined period. The existing network can handle the traffic within its certain limits on spectrum efficiency, and data rate. These two parameters are most important to predict the survivability of the network for a long period to handle the traffic. Recent technologies are to provide better quality of service with flexible spectrum utilization and adaptive data rate in a network. The network with flexi grid in spectrum and the adaptive networking properties called elastic optical network (EONs) can improve spectrum efficiency. The paper focuses on parameters EONs by considering the spectrum utilizations and the presence of guard bands using Integer Linear Program (ILP). The ILP returns the utilized slots with and without guard band at increased request demand in the networks. Finally the methodology to calculate the spectrum utilization of the elastic networks discuss the survivability of the elastic optical network with guardband. The value of spectrum utilization factor with respect to the scaled traffic demand allows predicting the survivability of the existing networks.

Bluetooth Controlled Arduino Robot

Saif Rafi and Indra Murugesh Galgotias University, India

The paper is designed to develop a robotic vehicle using Arduino for remote operation, monitoring purpose, surveillance purpose, industrial purpose. The robot can transmit real-time information with the help of the Arduino board connected to a computer or any smart device. The Arduino Uno is a microcontroller board based on the ATmega328. These robots are reprogrammable and can be used in multiple applications. Robots can work 24 hours without any rest making it more productive and efficient. The whole system is controlled by the microcontroller in the Arduino Uno in which the Arduino program is uploaded to control the motion of the robot.

Residual Vibration Suppression of Non-Deformable Object for Robot

Assisted Assembly Operation Using Vision Sensor

Chetan Jalendra, Bijay Kumar Rout and Amol Marathe Birla Institute of Technology and Science, Pilani Campus, Pilani

A vision-based vibration suppression strategy of a Non-Deformable Object held by an industrial robot during robotic assembly. The vibration is induced in this object during the rapid motion of the robot. In this case, an external controller is designed as an outer layer controller which works simultaneously with the robot internal controller without any interruption to suppress the residual vibration in the object. The proposed outer layer controller is a closed-loop feedback controller and designed and simulated using the Python IDLE environment and applicable for the robot that does not have control over acceleration. Experimental implementation has been carried out on an ABB 1410 robot to indicate the robustness of the proposed controller. The designed controller suppresses vibration in less than 3 seconds and the stability time is reduced by 97.5% for in a Peg-in-hole assembly task.

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Open MV - Micro Python based DIY Low Cost Service Robot in

Quarantine Facility of COVID-19 Patients

S Yogesh, B Prasanna, S Parthasarathi and M A Ganesh

Thiagarajar College of Engineering, Madurai, Tamil Nadu

COVID 19 is a pandemic which is affecting the entire world. Numerous efforts are being taken by the governments to maintain social distancing. But in quarantine facilities it is difficult to maintain these norms. Nowadays, service robots are being used in quarantine facilities to avoid spread of the virus. The robots used are generally expensive and manufacturer oriented. In this paper we propose an Open MV - Micro Python based DIY low cost service robot with an algorithm to be utilized in rural quarantine facilities. This facilitates effortless setup of service robots to assist healthcare workers in treating the affected patients. The paper also compares the proposed robot with the existing robots. This can drastically reduce the virus attack chain.

An Improved Inception Layer Based Convolutional Neural Network for

Identifying Rice Leaf Diseases

Baranidharan Balakrishnan1, Vinoth Kumar C N S1 and Vasim Babu M2

1SRM Institute of Science and Technology, India 2KKR & KSR Institute of Technology and Science, India

Technological intervention in agriculture is essential for its thriving. Particularly, in the crops like paddy, delayed identification of disease causes major economic losses. Early identification of diseases will help the farmers to take the proper course of action and save their crops. In this paper, an improved Inception CNN (I-CNN) model is proposed for early identification of rice leaf disease. Though many CNN models are existing, none of the existing models are sufficient enough to identify rice leaf diseases in its early stage. In many cases, it is found out that early identification of diseases will help take appropriate action and solve it. For comparison, the pre-trained models like AlexNet, VGG16 are compared with the proposed I-CNN model. All the models are tested and compared by varying the learning rate of 0.01, 0.001, 0.0001, and using different optimizers such as SGD and Adam. The proposed I-CNN achieved the highest accuracy of 81.25% whereas the best accuracies of AlexNet and VGG16 are 72.5% and 62.5% respectively.

Educator's perspective towards the implementation of Technology-Enabled

Education in schools

Gopal Datt1 and Naveen Tewari2 1Uttarakhand Open University, Haldwani

2Graphic Era Hill University, Bhimtal

This study gives an insight view of applications of technology-enabled education in schools of the Uttarakhand region. As we know the Uttarakhand is a hill state where Internet connectivity is a big issue in hilly areas of the region. The government of India has conjointly launched its National Mission on Education through info and Communication Technology (NMEICT) in 2009 to produce the chance for all the lecturers, learners, and consultants within the country to pool their collective knowledge for the good thing about the learner. The policymakers are keen to use the technological resources in serving to its mission on technology-enabled education, to form the standard education accessible to any or all meriting learners. We have conducted an extensive survey to identify the awareness, importance, and hurdles to applying

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the applications of technology-enabled education in the region. We have received 148 total responses about 29 survey questions, where some of them are of type qualitative and others are of quantitative by nature. The opinion of the respondents is analyzed and presented in the paper. The study further states to various aspects and phases of implementation and hurdles of technology-enabled education (TEE) in schools.

Sensorless Speed Control of Induction Motor Using Modern Predictive

Control

N P G Bhavani

Meenakshi College of Engineering, India

This paper uses model predictive control that is Superior to traditional engine transmission to handle sensorless speed control of induction motor driven by a single stage sun. The proposed system, solar PhotoVoltaic (PV) array connected with Voltage Source Inverter (VSI) and predictive control system, MRAS. A MPPT algorithm based on perturbation and observation is used for harnessing the full power from a PV series. System predictive control strategy is a predictive control approach to standard optimisation. For less dependency on model parameters, MPC can achieve sufficient control efficiency and has been widely used in process control systems. MRAS speed estimation method was implemented to achieve sensorless operation to boost the device reliability and cost reduction of Hardware. In MATLAB / Simulink environment the desired configuration is modelled and simulated.

Software Log Anomaly Detection Method using HTM Algorithm

Rin Hirakawa1, Keitaro Tominaga2 and Yoshihisa Nakatoh1 1Kyushu Institute of Technology, Japan

2Panasonic System Design Co., Ltd., Japan

In software development, log data plays an important role in understanding the behavior of the system at runtime and also serves as a clue to identify the cause of the defects. In the log messages generated by systems such as An-droid, a wide variety of components outputs are Mixed, and makes it very difficult to identify the problem from the vast amount of log data. The ultimate goal of this research is to develop a GUI tool to visualize anomalies in log messages and reduce the burden on the log analyst. In this paper, we propose a method for learning time-series patterns of logs based on features created from structured log messages. The proposed model is evaluated using the open dataset HDFS, and confirmed its effectiveness through detection score comparison with the baseline methods. As a result, it was shown that proposed method can detect anomalies in the accuracy comparable to baseline methods customized for log data.

A Study on Intelligent Control in PV System Recycling Industry

Gopalamma Aravelli and Srinu Naik Ramavathu Andhra University, Visahkhapatnam, A.P, India

Solar is one of the accessible green energy sources, but to make it green, we have to look after the end life management of the material used in the process. It is a peremptory aspect for the Industries and government to work together for a green energy waste management plan. Increased Volume of PV panels might Create Environmental Issues, and also, there exist unprecedented offers to chase new economic Opportunities. The proper way of recycling and reuse of material can unlock significant challenges behind. Three R's play a major role in any production to end the circular economic cycle they are Reduce, Reuse, Recycle. The priority is to reduce and next for reuse and then last for recycling. For the Recycling process, Infrastructure is needed. Waste treatment plants for automobile industries, composite materials

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like steel, glass, and metal, plastic manufacturing/Recycling units are having similar machinery and control logic and electrical installations. Fulfilling the objectives involved, such as low cost for recycling and a high recycle rate with maximum efficiency and energy savings. Here we proposed energy flow and its control at each level in the PV recycling Industry. Any plant associated with conveyors, hopper, and crusher systems to transport and to handle. Here we executed the conveyor with interlocks and permissive in codesys software and the same performed in Siemens S71200 PLC and drive Unit (G120).

An Automatic Digital Modulation Classifier Using Higher-Order Statistics

for Software Defined Radios

Nikhil Marriwala and Manisha Ghunsar University Institute of Engineering and Technology, Kurukshetra University,

Kurukshetra

Automatic Modulation Recognition (AMR) plays a vital role in various applications such as software-defined radios, cognitive radio receivers, and surveillance systems. This paper is based on the recognition and classification of digital modulation schemes. The designed system is capable of recognizing nine digital modulation schemes, namely, 2FSK, 4FSK, 8FSK, 2PSK, 4PSK, 8PSK, 16QAM, 64QAM, and 256 QAM in Additive White Gaussian Noise (AWGN) environment. The system includes two steps: feature extraction and classification. In feature extraction step has used seven higher-order cumulants, namely, C40, C42, C44 C51, C53, C62, C80 as features for statistical analysis of signals. In the classification, the step has used the principal component analysis technique on the feature set for compression of data and for classification of different modulation schemes. The simulation results show that the presented system has 100% classification accuracy at 16 dB SNR.

Low Profile Wide Band Micro Strip Antenna for 5G Communication

Pankaj Jha1, Ram Lal Yadava2 and Seema Nayak1

1IIMT College of Engineering, Greater Noida, India 2Galgotia College of Engineering & Technology, India

In this paper, low profile micro strip antenna has been presented for 5G applications. The proposed antenna is designed using partial ground architecture. The proposed antenna maintains the return loss less than -10 dB for a wide range of frequency from 2.9 GHz to 10 GHz. The gain of antenna is changing from 2.2 to 4.4 dB and radiation efficiency is having maximum value of 90%.

Hybrid Cryptography Algorithm for Secure Data Communication in

WSNs: DECRSA

Amine Kardi1 and Rachid Zagrouba2 1Faculty of Mathematical, Physical and Natural Sciences of Tunis, Tunisia

2Imam Abdulrahman Bin Faisal University, Saudi Arabia

In this paper, we show the effect of the Jamming and Hello flood attacks in Wireless Sensor Networks (WSNs) through simulations conducted under the NS3 simulator. We discuss the best known security mechanisms and we propose a dynamic hybrid cryptosystem based on the two famous cryptographic schemes RSA and ECC. Our proposal well adapted to the specific characteristics of the sensors, benefits from the advantages of ECC and improves it by the RSA system to ensure a high security level in WSNs. This hybrid algorithm overcomes the weakness of existing solutions identified in this work and offers the possibility of adjusting the security level according to the needs of the application.

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Hybrid Approach to Combinatorial and Logic Graph Problems

Daria Zaruba, Vladimir Kureichik and Vladimir Junior Kureichik

Southern Federal University, Russia

The article is concerned with one of the possible hybrid approaches to combinatorial and logic graph problems. They belong to the class of NP-hard optimizations problems. The authors have suggested a hybrid approach to solve these issues more effectively. The search process divided into several levels, which constitutes a distinctive feature of this approach. At the first level, the graph model is com-pressed by the method of fractal aggregation. Then, at the second level, the genetic algorithm is performed, and the graph model is decompressed at the third level. In order to realize this approach in practice, it has been developed the three-level hybrid algorithm which can obtain quasi-optimal solutions in polynomial time and avoid local optimum areas. In this article it is considered a particular combinatorial and logic problem – a placement of graph vertices in the lattice. A soft-ware application has been developed to carry out a series of computational experiments on the basis of PECO test circuits. Conducted tests have shown the ad-vantage of the suggested hybrid approach in comparison with already known optimization methods.

Malware attacks on Electronic Health Records

Shymalagowri Selvaganapathy and Sudha Sadasivam G

PSG College of Technology, Coimbatore, Tamil Nadu

Technological advancements along with the surge of high speed Internet has enabled healthcare organizations (HCO) to provide enhanced medical treatment and better patient support. Equivalently, cybercriminals are lured by the sensitive patient data available which urges them to launch attacks to their benefits. Artificial Intelligence systems involving machine learning and deep learning techniques are progressively utilized for medical diagnosis and predictions. Attackers try to disrupt the working of the learning techniques to cause misclassification or launch privacy attacks. Attackers daunt the health sector to seek Personally Identifiable Information (PII) causing Health Insurance Portability and Accountability Act (HIPAA) violation. These attacks can have severe effects in a critical clinical environment. This article tries to detail on the possibility of adversarial attacks on medical reports by considering the Portable Document Format (PDF) file format. Electronic Health Records (EHR) and DICOM reports supporting PDF file format are under constant threat to malware attacks. The results demonstrate the validity of the suggested hypothesis and provides security guidelines for health sector to thwart such attacks in real-time.

Energy Efficient Algorithms Used In Datacenters : A Survey

Juliot Sophia M and Mohamed Fathimal P.

SRM Institute of Science & Technology, Chennai, Tamil Nadu

The count of datacenters is drastically increasing as the need for cloud computing is increasing day by day. Datacenters are the major electrical consumers in the world [1]. Successful cloud service providers (CSP) such as Google, Amazon, Microsoft are having huge number of datacenters worldwide with hundreds of servers (PMs) to satisfy their customers’ requirements [3][8]. Servers along with their network and cooling equipment require huge amount of energy to operate. A study has shown that data centers will use around 3–13% of global electricity in 2030 compared to 1% in 2010[9] .Data centers not only consume lot of energy but also release lot of Greenhouse gas(GHG). Energy consumption and carbon-di-oxide emission of datacenters are the major concerns of cloud service providers (CSPs).The benefits of cloud datacenters are given to us by the cost of global warming since the servers used in datacenters dissipate lot of heat [2]. Implementation of holistic approaches such as

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advanced frameworks , power efficient electronic devices and well- designed cooling and heating equipment are not suffice to address the problem of energy consumption. Effective use of Servers in datacenters will help the CSPs to face the energy efficiency challenge to a greater extent. Allocating services to power efficient PMs and reducing the count of running PMs are the best practices followed in datacenters. The purpose of this survey is to study different kinds of algorithms used in datacenters in recent years to achieve energy efficiency target and to minimize carbon-di-oxide emissions.

A Perspective of Security Features in Amazon Web Services

Kota Harsha Surya Abhishek1, Shyam Mohan J S1 and Challa Nagendra Panini2

1Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya, Tamil Nadu, India

2Shri Vishnu Engineering College for Women, Kovvada, Andhra Pradesh

In the recent times, increase in ICT tools has made significant progress in various sectors. As a result, data storage has become one of the most challenging tasks. Cloud Computing has resolved this problem by providing on-demand computing power like Virtual Servers, storage space, function as a service and many more. Cloud computing indisputably provides everlasting benefits and is cost effective model. However, the major drawback in Cloud Computing is Security. In this paper, we will have a detailed view of shared responsibility model, global infrastructure and compliance program, network security features. These will be the base for all the services provided by the Amazon Web Services (AWS). We will also have in-depth security features in Elastic services and Networking services like Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Block Store (Amazon EBS), Amazon Elastic Load Balancing (Amazon ELB), Amazon Virtual Private Cloud (Amazon VPC), Amazon Route 53, AWS Direct Connect. All these services are the main base for any Well-Architecture solution in Amazon Web Services (AWS) Cloud.

Question Answering System Using Knowledge Graph Generation and

Knowledge Base Enrichment with Relation Extraction

K. Sathees Kumar and S. Chitrakala

Anna University, Chennai, India

Question answering system is one of the predominant application that used to get the answer for the input natural language question. It reduces the search time to find the answer for particular question. In order to answer a natural language question, question understanding plays a vital role. Understanding a question semantically, leads to get the intention of the input question accurately. In order to understand the question, semantic query graph will be constructed to analyze the question phrases in a structured way by extracting entity and relation phrases. If there is no relevant relation phrases in knowledge base then query graph cannot be constructed. In order to overcome this unavailability of relation phrases, using n-gram based attention model and joint learning, relation phrases will be extracted. Using encoder-decoder model, extracted relation phrases along with that corresponding entity and predicate phrases will be converted as triples with unique IDs. Finally, using top-k sub graph matching algorithm, the answer with the top score will be selected as the final answer. In this proposed Question Answering system with Relation Enrichment (QARE) using two datasets such as QALD5 and web questions benchmark, comparative analysis had been carried out with two existing systems. Experimental results show that proposed system achieves better performance than existing systems.

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A Study on Design Optimization of Spur Gear Set

Jawaz Alam, Srusti Priyadarshini, Sumanta Panda and Padmanav Dash

Veer Surendra Sai University of Technology, Burla, Odisha

In this article a profile modification approach is adopted to design a spur gear set for minimization of contact stress and optimization of weight. An altering tooth sum method is used to reduce the hertizian contact stress along the path of contact. Four case studies are performed for a spur gear set with specific centre distance and tooth sum of 90 (±5). A Multi-objective optimization problem is formulated with contact stresses along path of contact and weight of gear set as design objectives. This nonlinear constrained optimization problem has been addressed by mean of the Particle Swarm Optimization algorithm. Six design variables related to gear geometry and material property are used in this optimization and all the constraints are satisfied at the optimal solution. Gear and pinion surface temperatures are below flash temperature, indicating protection against scoring wear. A higher value of AGMA scoring index is achieved in this optimum design approach. Specifically, a lighter gear with less contact stress and adequate scoring resistance is reported in this study. Promising results in terms of objective function values and computational time (CPU time) are observed .Furthermore, a CAD model is developed by means of optimized design parameters so as to check the geometric interference and practical feasibility of the design.

Text-Document Orientation Detection Using Convolutional Neural

Networks

Shivam Aggarwal, Safal Singh Gaur and Dr Manju

Jaypee Institute of Information Technology, Noida, India

Identifying the orientation of scanned text documents has been a key problem in today’s world where every department of any cooperation is surrounded by documents in one or another way. In this paper, our emphasis is on the more challenging task of identifying and correcting the disorientation of general text documents back to normal orientation. Our work aims to solve the real-world problem of orientation detection of documents in PDF forms which can be later used in further document processing techniques. To do this, the convolutional neural network (CNN) is used which can learn salient features to predict the standard orientation of the images. Rather than earlier research works which act mostly between the horizontal and vertical orientation of non-text documents only, our model is more robust and explainable as it works at page level with text documents. Also, we have accelerated to different level with proper explanation and interpretability.

Air Quality Monitoring System using Machine Learning and IoT

Varshitha Chandra B. R., Pooja G. Nair, Risha Irshad Khan and Mahalakshmi B. S.

B. M. S. College of Engineering, India

Monitoring air pollution is of increasing concern today. People are suffering from health problems as a result of prolonged exposure to polluted environments. This project aims to develop an Air Quality Monitoring System using Machine Learning with IoT (Internet of Things), an internet server network of physical nodes. This system consists of three sections: an air pollution detection model developed in python and built using machine learning algorithms, Random Forest and Support Vector Machine, a low-cost air monitoring device comprising of a hardware unit that detects different pollutants like CO, NOx, PM2.5 and an IoT Cloud, ThingSpeak, acting as a middleman for the captured data between the hardware component and the algorithm for air pollution classification. The final output displays the

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predicted Air Quality Index (AQI) and provides a comparison between the two algorithms used, Random Forest and Support Vector Machine, in terms of accuracy and various other statistical data. The accuracy depicted by Random Forest exceeds 95% and that of Support Vector Machine is 85%.

Optimization of Regularization and Early Stopping to Reduce Overfitting

in Recognition of Handwritten Characters

Naman Jain

Symbiosis University of Applied Sciences, India

Character recognition of handwritten texts is one of the most crucial aspects of any pattern recognition algorithm. The application of digit recognition is very vast, some examples but not limited to, include postal mail sorting, digitizing any hand filled form, bank check processing, signature verification, etc. For the purpose of this research, logistic regression was used to train a model to accurately identify the input digits/alphabets. Also, to improve accuracy of the model, parameter optimization was done on two major factors of logistic regression. The two parameters, the regularization parameter (lambda) and the number of iterations (during training) were closely studied and were optimized to eliminate any chance of Overfitting. Experimental results show that an optimized set of parameters would provide maximum accuracy on the test set and on the Training set if Regularization and Early Stopping were to be applied in a joint manner. The simulation was done on MATLAB using a Gradient Descent based algorithm to minimize the Cost Function. Gradient Descent was chosen as it is guaranteed to find the global minimum of a convex surface, however it can incur a high computational cost. I concluded from the simulation results that machine learning algorithms provide near to 100% accuracy in the training set, but face difficulties and significantly reduced accuracy on the Cross-Validation Set.

Comparison of Various Classifiers for Indian Sign Language Recognition

using State of the Art Features

Pradip Patel1 and Narendra Patel2

1Gujarat Technological University, India

2BVM Engineering College, India

Indian Sign Language (ISL) is used by deaf people in India for their internal communication. Sign Language Recognition (SLR) system recognizes gestures of sign language and converts them into text and voice thus enabling deaf people to communicate with others. In this paper we have presented an automatic system for ISL hand gesture recognition. Modern features like Histogram of Oriented Gradient (HOG), Speeded Up Robust Factor (SURF), Fourier Descriptors, Hu Moments and Zernike Moments have been extracted from training images of our own created dataset. Using these features, most commonly used classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbours (KNN) and Linear Discriminant Analysis (LDA) have been trained and evaluated. Comparative performance analysis of these classifiers along with experimental results is the main topic of discussion included in this paper. Moreover a user friendly GUI of the system allows user to select any combination of features and classifiers to perform gesture recognition from both stored file and live camera. During experiment it is found that the system is able to recognize gestures with a recognition rate up to 98.70%.

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Logical Inference in Predicate Calculus with the Definition of Previous

Statements

Vasily Meltsov1, Nataly Zhukova2 and Dmitry Strabykin1

1St. Petersburg State Electrotechnical University “LETI”; Vyatka State University, Russia

2St. Petersburg State Electrotechnical University “LETI”; SPIIRAS, Russia

At the present stage of computer engineering and information technology development, one of the most actual problems is the creation of high-performance artificial intelligence systems. Along with the successful use of neural networks and various machine learning algorithms, the theory and methods of logical inference play an important role. These mechanisms are indispensable in the design of such knowledge processing systems as expert systems, decision support systems, enterprise management systems, software verification systems, medical and technical diagnostics systems, battle control systems, etc. One of the promising applications of inference is logical prediction. The authors propose an original method for the inference of conclusions in predicate calculus with the definition of previous statements. This approach allows us to reduce the problem of forecasting the situation development, including the transition of the situation to a desired phase, to the task of deductive inference. The method is based on an iterative repetition of the partial and complete "disjunct division" procedure. The article gives a meaningful and formal definition of the problem of the logical inference of conclusions in predicate calculus with the definition of previous statements. Features of all method`s stages implementation are illustrated by examples. The main advantage of the proposed method is the parallel execution of disjunct division operations in the inference procedure. This will significantly increase the performance of the inference process.

Congestion Management Considering Demand Response Programs using

Multi-objective Grasshopper Optimization Algorithm

Jimmy Lalmuanpuia, Ksh Robert Singh and Sadhan Gope

Mizoram University, Aizawl, India The increase in consumption of electrical energy has become one of the major challenges in power system operation and control. In the mean times, transmission congestion is one of the major concerns for power system operator and planner. Thus, this paper presents a multi-objective grasshopper optimization technique for congestion line management considering the demand response programs. The objective of this congestion management problem is to reduce the emission and also the total operation cost of the system. The above mentioned optimization technique is tested on IEEE 30-bus test system with varying load conditions. It is found that demand response programs can solve the congestion management problem significantly. Results also show that multi-objective grasshopper optimization technique performs better as compare to other optimization techniques discussed in this work.

Effectiveness of Software Metrics on Reliability for Safety Critical Real-

Time Software

Shobha S. Prabhu1 and H.L. Shashirekha2

1Gas Turbine Research Establishment, DRDO, India

2Mangalore University, India

Safety critical software is a key component of any critical system and whenever there is a

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failure in this software, the system malfunctions with effect on safety of life or mission. Reliability is one of the quality factors and performance evaluator for this critical software. High reliability is expected of such software in its design, development and maintenance in order to increase the quality of the software for the system. Reliability metrics are derived for this kind of software during the planning phase and enhancement of reliability metrics is achieved during the design & development phase. When the software is being designed, the reliability metrics are taken as the factors of foundation on which the software is built. In this paper, the software metrics which form the basis for proving their effectiveness on reliability for airborne engine control safety critical software are described. Development process based on these metrics not only influenced the reliability enhancement of the critical software but also improved the performance and efficiency of the embedded real-time system.

A Hardware Accelerator Implementation of Multilayer Perceptron

Thasnimol V S and Michael George

Rajiv Gandhi Institute of Technology (RIT), Kottayam, India In recent years, there has been a surge in the use of machine learning models for various important applications in wide variety of fields. Neural networks play a huge role in this area. The hardware acceleration of neural network models has been a recent area of active research. In Hardware acceleration-based implementation of computing tasks, the throughput of the system is increased by decreasing latency. This work focuses on developing a digital system implementation of the most common structure for a feed forward neural network (FFNN) known as multilayer perceptron (MLP). In pursuit of this aim, the proposed architecture designed by considering the performance, accuracy and resource usage and also the remarkable benefits of the design to accelerate the feed forward neural network computation.

Building a Classifier of Behavior Patterns for Semantic Search based on

Cuckoo Search Meta-heuristics

Elmar Kuliev, Ilona Kursitys, Victoria Bova and Dmitry Leschanov Southern Federal University, Russia

The paper deals with the problems of extracting and classifying the thematic categories representing user preferences to increase the pertinence of semantic search in the knowledge-based information systems. The authors propose building the classifier of behavioral user profiles by analysing the information requirements, implicit preferences, and knowledge resources interaction. The method is based on the probabilistic algorithm for EM- clustering of user characteristics and distributed knowledge resources to generate the structure of the classifier input parameters. Optimization is implemented by the mechanism of selecting informative features based on revealing the user latent interests and the resource ability to satisfy the users. To reduce the search space, the paper proposes a bio inspired algorithm based on the cuckoo search. The algorithm is compared with the Fuzzy C-means algorithm on the UCI Machine Learning Repository. The results show the effectiveness and prospects of the proposed method in the semantic search systems.

Impact of Quasi-variable Nodes on Numerical Integration of Parameter-

Dependent Functions: A Maple Suite

Navnit Jha

South Asian University, India We shall extend the classical integral approximation techniques to nonuniform nodes, and observe the impact on solution errors, order, and accuracies. The proposed algorithms can be regarded as a generalization to some commonly applied integration formula, such as Simpson’s rule. A detailed computation on bounds of error using a quasi-variable node exhibit

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a better resolution and equivalent truncation error as obtained on a uniformly distributed node. Numerical simulations are presented to justify the convergence rate of the proposed scheme.

Text Clustering Techniques for Voice of Customer Analysis

Zaheeruddin Ahmed1, Harvir Singh2 and Sonu Mittal2

1Manipal Academy of Higher Education, United Arab Emirates

2Jaipur National University, Jaipur, India

A comprehensive analysis of customers is very vital for any organization for customer enlightenment. However, conventional methods are not effective in analyzing customers concerns particularly when it is online. Data generated online is extremely complicated because of its unstructured behavior, the data set in this format is often complex and very difficult to evaluate its overall model. Further, the application of conventional methods remains monotonous as many emerging techniques are proposed by the researchers, but the most effective approach will remain thought-provoking. In this paper, we intro-duce effective methods to analyze customer concerns with a computational approach that perfectly apply text clustering techniques. An effective clustering technique can be applied to a large set of unstructured data since online data of-ten contain huge amount of noise, we will discuss different text clustering techniques to evaluate and analyze the method of text extraction from customer statements mainly on web and social media for customer analysis.

Analyzing the Impact of Software Requirements Measures on Reliability

Through Fuzzy Logic

Syed Wajahat Abbas Rizvi Amity University Uttar Pradesh

In today’s scenario the information technology is impacting every domain of our lives. The need of reliability as a quality attribute has been gaining its importance. At the same time, it is a well-accepted fact that if the software requirements are handled carefully then the final software will have a high probability of reliability. With this spirit, researcher has identified a set of requirement stage measures and analyze their impact on the reliability of the software. This study has shown quantitatively that the metrics based on requirements phase impact on the reliability of the final software. The paper also presents a comprehensive sensitivity analysis that highlights how various requirements phase measures influence the soft-ware reliability positively or negatively.

Social Network Opinion Mining and Sentiment Analysis: Classification

Approaches, Trends, Applications and Issues

Amit Pimpalkar and R. Jeberson Retna Raj

Sathyabama Institute of Science and Technology, Chennai, India

Sentiment Analysis (SA) or Opinion Mining (OM) probes the content stated about a topic of interest and classifies them as +ve, -ve or neutral hold on the person's opinion, emotion, judgment articulated in it. Nowadays people’s opinions and reviews on blogs, forums, review websites, E-commerce sites are most vital information in the process decision-making for both organization and users. SA is an information revelation and knowledge discovery technique inherited from data mining. The manual investigation of such immense number of reviews is actually unthinkable, so computerized frameworks are expected to do analysis and accordingly classify of information to take related decisions. In general, SA attempts to make some decisions on the base of an author standpoint that may be his or her judgment, mood or

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evaluation. In this study, we have described the essential concepts, various methods, trends, applications and key issues of OMSA in social networks. We tried to analyze SA literatures from various perspectives and presented those techniques and methods in a systematic manner. With brief descriptions of the algorithms, different SA taxonomical techniques classified along with their advantages and limitations. The critical opportunities that lie ahead for SA are defined and addressed on the basis of those sources. We attempted to present the recent applications and challenging issues in Sentiment Identification and presents evaluation metrics for SA.

Dynamic Analysis of a Novel Modular Robot

Omkar Dixit and Vishal Dhende

Walchand College of Engineering, Sangli, India

A vast amount of research is being carried out in the field of modular robotics. The research is mainly focused on improving locomotion and docking capabilities. This paper presents dynamic analysis of a novel modular robot. The modular robot has nested configuration and thus can be assembled in various forms and can be adapted for various environments. Due to the availability of both unconventional and wheeled locomotion, dynamic analysis is carried out for each configuration separately. Euler-Lagrangian approach is used for analysis of unconventional locomotion. A MATLAB program is created to solve the equations of dynamics. While robot locomotion analysis is done to determine torque requirement for wheeled locomotion. Validation of analysis is done with the help of Virtual Robotic Experimentation Platform (VREP) – a robotic simulator.

A Comparative Analysis on Edge Preserving Approaches for Image

Filtering

Niveditta Thakur, Nafis Uddin Khan and Sunildatt Sharma

Jaypee University of Information Technology, Solan, India

Noise removal while sustaining the integrity of important details is a pedantic problem in image enhancement and reclamation. The vital stage to improve image quality which is needed for quantitative imaging study is filtering. Edge preserving filtering techniques are used extensively in computer vision to serve de noising efficiently in prominent research areas of enhancing the quality of low-level vision images, medical images, industrial images, geological images, and so on. This review paper is presenting the popular edge preserving approaches for enhancement of speckle contaminated images and to enhance peak signal to noise ratio (PSNR) of images. Various IQA techniques for proving the worth of these approaches are discussed. Main challenges in these filtering approach are also covered along with how those challenges are inscribed by researchers. This review contributes to research problems to other fellow researchers who are keen to work in this area.

TETRA Enhancement Based on Adaptive Modulation

Ali Abbood and Alharith Khafaji University of Babylon, Iraq

Every day, millions of people place their trust in products based on Terrestrial Trunked Radio (TETRA). TETRA is a Private Mobile Radio (PMR) criteria purposed for meeting the special requirement for voice communication which is reliable and secure communication links. Due to the data-rate's narrowband and on-demand applications which required big data rate, fast communication and secure in different environments. So new TETRA Release introduces in this work to support novel high data rate applications which require wide band such as video streaming and adapting with cases of congestion or density of communication when special

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cases of application occur which they effect on link cause bottlenecks and stop services like denial of service (DoS). An adaptive TETRA system has been suggested in the selection of the advanced network based on the input of the modulation objects. As well as, based on these modulations (π/4 DQPSK, 4 QAM, 16 QAM, 64 QAM) which they give us more data rate from the single module modulation based system. All parts and techniques of the proposed work are implemented in ‘OMNET++’ tool of simulation and programming language of visual C# with visual studio. Finally, we show that our proposed work gives us more adaptation way when we use multiple modulation comparing with single modulation.

Progress in the Mathematical Modelling of the Emotional States

Siddhartha Arjaria and Shruti Tripathi Rajkiya Engineering College, Banda

In the recent years, several attempts have been made to understand and construct a model that establishes the relationship between emotional states and cognitive behavior, in order to apply these studies to a robot for various purposes. These purposes include aid in the treatment of mentally ill patients or construction of elderly companion robots or make an independent decision-making machine, in general. From the purposes, it is clear that the construction of a credible model is vital for the advancement in the field of cognition and emotion. This paper is such an attempt to resolve the progress made by the attempts to model the emotional states and include them into an artificially intelligent agent and presents the analysis of the progress made by the representation of the emotional states in the form of models such as valency models, algebraic models and fuzzy logic models. This work will present the traditional approaches as well as comparatively modern ones and contribute to guide future research in this area.

Design and Analysis of Mechanical Properties of Simple Cloud-based

Assembly Line Robots

S Srrinivas and Hari Krishnan R SASTRA Deemed to be University, India

Industrial robots have been extensively used in factory shop floors to do repetitive, tedious and dangerous operations such as assembly, painting, packing and welding. To further widen the functional capabilities, the existing pre-programmed robots are integrated with networking concepts to cultivate the networked robotics culture. This study provides an insight on design of a simple automated robotic assembly line, which serves the purpose of screwing bolts to the workpiece incoming through the assembly line. This finds its application in an assembly line of any production industry, where difficulty is faced by line workers in repeatedly joining parts using bolts and nuts. The assembly line is designed to consist of two robots - A 4 DoF SCARA Robot and a 5 DoF Pick and Place Robot. The kinematic model and study for both the robots were per-formed and 3D CAD models of both the robots were made and assembled. Analysis of various mechanical properties of both the robots such as stress, strain, displacement and buckling load are performed to ensure nominal operations. The assembly line is made to virtually show how the robots will work in tandem to achieve the intended purpose.

Impact of Dynamic Metrics on Maintainability of System using Fuzzy

Logic Approach

Manju and Pradeep Kumar Bhatia Guru Jambheshwar University of Science and Technology, Hisar

In software development life cycle, software maintainability is the most important and crucial

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phase because it requires more effort as compared to other phases. To minimize the cost of software maintenance, it is necessary to predict software maintainability during the early phases of software development life cycle. Most of the metrics available in literature to calculate maintainability factor are static i.e. based on design patterns. In this paper, a fuzzy model is proposed to measure Maintainability of system. Firstly static metrics are used as input to evaluate maintainability at compile time and after that dynamic metrics are used as input to compute maintainability at run time. Static metrics values are collected using CodeMR tool while dynamic event tracing is done by AspectJ an implementation of aspect oriented programming on java platform. Further, purposed model is applied on 15 java classes to calculate maintainability at compile time and run time based on static and dynamic metrics. Lastly, proposed model is validated using AHP (Analytical Hierarchy Processing) technique and it is concluded that dynamic metrics are better predictor of Maintainability factor.

Feature Based AD Assessment using ML

Siddheshwari Dutt Mishra and Maitreyee Dutta NITTTR, Chandigarh, India

Neuroimaging has revolutionized the world of neuroscience. More specifically, the role of functional imaging in the diagnosis of metabolic diseases like Alzheimer's is very very significant. It's an era of smart intelligence wherein the machine efficiently and smartly visualizes the given input and produces a valuable output. Thus, the objective of this paper is to classify brain images into two broad classes namely, Alzheimer's and Normal Control using machine learning classifiers. Furthermore, the study focuses on comparing the accuracy of classifiers across medical imaging modalities. The dataset consists of MRI, PET and DTI scans of subset of participants from publically available ADNI dataset. We summarize our results with the findings of most suitable classifier for each modality.

Notification System For Text Detection On Document Images Using

Graphical User Interface (GUI)

Wan Azani Wan Mustafa, Mohd Aminudin Jamlos, Wan Khairunizam Wan Ahmad and Mohamad Nur Khairul Hafizi Rohani

University Malaysia Perlis, Malaysia

Degraded document images usually carry numerous amounts of information. Thus, it must be retrieved by using a binarization approach. Many causes can lead document images towards the degradation process, such as the quality of the inferior materials of document images, environmental factors, and carelessness in document images handling. Binarization is a process of converting a pixel image to a binary image, which consists of black and white pixels. The binarization process usually starts with converting a color image to a grayscale image. This transformation can be done by using an average of a pixel's Red, Green, and Blue components together to get its grayscale value. This proposed system consists of two parts, which are mainly performed in the MATLAB Graphical User Interface (GUI). In the preliminary section of the GUI, the binarization strategy of amendment Fuzzy C-Means and Deghost Method will be utilized to a purchaser selected document picture. The binarization method will ride Image Quality Analysis (IQA, for example, PSNR, Accuracy, and F-measure to figure out the viability of the technique. The three parameters of the IQA will at that factor be proven in a section on the GUI. The 2nd piece of the framework is for the versatile be aware framework to ship SMS to a phone phone. All collectively for the patron to accumulate the photograph examination results, there should be a warning framework on the cellphone phone. By utilising the GSM module coordinated with Arduino Uno, the word in regards to photograph examination will be despatched to the telephone phone.

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A Tripod Type Walking Assistance for the Stroke Patient

Kishore Pandiyan1, Anjan Kumar Dash1, Akhil Pragallapati1, Dheerkesh Mugunthan1, Aniirudh Ramesh1 and Dilip Kumar K2

1SASTRA Deemed to be University, India

2Sanjay Physiotherapy Centre, India

This paper presents, a tripod type balancing support which assists the stroke patients during standing and walking. Stroke is the blockage or the damage of veins which supplies blood to the brain. It results in difficulty in speaking and understanding, loss of one-sided mobility. There are various walking aids like canes, crutches, and walkers are available in the market. But these walking aids fail to provide proper stability for the stroke survivors for their basic mobility. And also they always depend on the assistive person for their basic mobility. This leads to an unhappy situation for the patient as well as to the family. Commercially available exoskeleton for stroke patients is very costly and such that normal person cannot afford that much higher amount. This work aims to provide the necessary stability during walking at a low cost for the stroke patient. Hence, a tripod type of balancing support is fabricated and tested with the stroke patient. From the experiment, it is seen that tripod walking support provides the necessary assistance required for the stroke patient and motivates the patient positively towards walking.

COVID-19 Risk Management

Jannaikode Yashwanth Kumar1 and Nenavath Srinivas Naik2

1CVR College of Engineering, India, 2IIIT Naya Raipur, India

The sudden outbreak of coronavirus has left people unanswered till date. COVID-19 virus cases are drastically increasing day by day. The only way to protect ourselves from COVID-19 is by maintaining social distancing and taking preventive measures. We propose a Mobile application-based solution for risk management of COVID-19. The objective of the proposed model is to help people with the status of cases in their region. The proposed model notifies the user regularly about the COVID-19 cases in nearby locations which helps him/her to be cautious. We use android studio and Google Firebase to develop the mobile application. COVID-19 information and state-wise cases are updated in the database regularly and users are notified. The results are good in terms of response time and delivery of static alerts.

Speech Emotion Recognition Using Machine Learning Techniques

Manjusha Nair, Sreeja Sasidharan Rajeswari and G Gopakumar

Rajalakshmi Institute of Technology, India

Speech Emotion Recognition system is a discipline which helps machines to hear our emotions from end-to-end. It automatically recognizes the human emotions and perceptual states from speech. This work presents a detailed study and analysis of different machine learning algorithms on a Speech Emotion Recognition System (SER). In prior studies, single database was experimented with the sequential classifiers to obtain good accuracy. But studies have proved that the strength of SER system can be further improved by integrating different deep learning classifiers and by combining the databases. Model generalization is difficult with a language dependent and a speaker dependent database. In this study, in order to generalize the model and enhance the robustness of SER system, three databases namely Berlin, SAVEE and TESS were combined and used. Different machine learning paradigms like SVM, Decision Tree, Random Forest and deep learning models like RNN/LSTM, BLSTM (Bi-directional Lstm), CNN/LSTM have been used to demonstrate the classification. The experimentation result shows that the integration of CNN and LSTM gives more accuracy

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(94%), when compared with other classifiers. The model performs well in all emotional speech databases used.

Model Based Data Collection Systems on Fog Platforms

Natalia Zhukova1, Alexander Vodyaho2, Saddam Abbas2 and Elena Evnevich1

1Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Russia

2Saint- Petersburg Electrotechnical University (LETI), Russia

The modern stage of development of technology is characterized by an increase in the complexity of created anthropogenic systems, a constant expansion of the scope of application of information technologies and an increase in the intellectual level of created anthropogenic systems and the emergence of new paradigms for building information-oriented systems such as cyber-physical systems, the Internet of things, cloud and fog systems. Modern information-oriented systems often have a dynamic structure and implement complex behavior. Data collection in such systems is a non-trivial task. The paper proposes a model approach to building information collection systems in multilevel cyber-physical systems which are realized on the fog computing platforms. Models are proposed to be built in terms of knowledge.

Data Routing With Load Balancing Using Ant Colony Optimization Algorithm

Manjula S, Manikandan P, Philip Mathew G and Prabanchan V Rajalakshmi Institute of Technology, India

Wireless Sensor Networks (WSNs) are growing field in recent days. For maximizing life time of network, the routing is very important part to be considered. The routing becomes more complex due to number of nodes in the network increases. Sensor nodes in WSNs are constrained in processing power and batteries. These constraints have been enhanced by our proposed method to solve the routing problem i.e., data balancing or load balancing reduces the data path congestion and decreases the energy consumption of every node. However, In case of internet, load balancing is not sufficient to avoid congestion. There is in need of algorithm to provide optimum solution. Ant colony optimization (ACO) technique is used to find the shortest path and it is easier to combine other techniques with it as like data balancing using ACO. The proposed method combines ACO and load balancing technique to find an optimal approach for an efficient data routing. NS2 simulator is used to implement proposed algorithm. From the analysis of results, the proposed one produces better results than other existing algorithms.

Show Based Logical Profound Learning Demonstrates Utilizing ECM

Fuzzy Deduction Rules in DDoS Assaults for WLAN 802.11

Sudaroli Vijayakumar1 and Ganapathy Sannasi2 1PES University, India, 2VIT University, India

One wording that's making turns and turns in each division is “Data”. Colossal information generation contributed to innovation headways and brilliantly choice making. Each organization depends totally on the information for making pivotal choices around their trade. If information is stolen, one can make an organization or a person to move concurring to their tunes. In this way, securing the computing foundation for an organization picks up part of significance. A few levels of security measures exist for organizations, be that as it may compromise in shapes of interruption is still winning. If the interfacing environment is remote, independent of its layered approach to security the levels of interruption assaults is tall. Each organization requests an interruption framework that can totally secure their computing

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environment. Such sort of interruption discovery framework is conceivable on the off chance that the framework can learn like human and make choices like people. Hence, the utilization of profound and machine learning approaches can enormously help one to construct a stronger interruption discovery framework. This paper presents a novel system LASSO-ECM utilizing DEFNIS for anticipating the DoS assaults more precisely. The approach is confirmed utilizing the AWID (Aegan Interruption Location Dataset) with expectation precision of 99% serving to be a productive prescient show for recognizing DoS assaults in WLAN.

Handwritten Devanagari Character Recognition using CNN with Transfer

Learning

Akhil Ranjan Garg and Gaurav Singh Bhati

MBM Engineering College, J.N.V. University, Jodhpur, India

In this paper, we review the use of CNNs, along with the transfer learning for handwritten Devanagari Character recognition. We compare the performance of VGG16 and DenseNet121 with transfer learning for recognition of handwritten Devanagari character dataset. The results of models trained in different conditions and compared with other methods are presented. Our study shows that DenseNet121 with deep fine-tuning method outperformed the other pre-trained models and other supplemental learning strategies. The learning accuracy further improved with some tweaking of hyper-parameters like batch size, learning rate, etc.

Estimation of Road Damage for Earthquake Evacuation Guidance System

Linked with SNS

Yujiro Mihara, Rin Hirakawa, Hideaki Kawano, Kenichi Nakashi and Yoshihisa Nakatoh

Kyushu Institute of Technology, Japan

Earthquake-induced damage is serious in Japan. One of the causes of the dam-age by the earthquake is considered to be the delay of escape due to the obsta-cle to the passage. It is important to evacuate quickly when the earthquake dis-aster. However, in the event of an earthquake, there is a possibility that the passage will be blocked by obstacles. It can be said that the evacuation route is not appropriate for them. In this study, we propose an evacuation guidance system that obtains route information from SNS. It is make it possible to evacuate users with a safe and shortest route. However, it is necessary to estimate the damage situation of the passage. Therefore, we defined eight types of failures and classified them using SVM, a machine learning method. In this case, in order to suppress the calculation cost, transfer learning was performed using VGG16. As a result, classification was possible with 78% accuracy.

IoT Based Home Vertical Farming

Abhay VS, Fahida VH, Reshma TR, Sajan CK, Siddharth Shelly Mar Athanasius College of Engineering Kothamangalam, India

Worldwide, around three million hectares of agricultural land are lost each year as a result of soil degradation and conversion for various development purposes, which in turn reduced the crop yield. Vertical farming is a type of farming method which cultivates crops using vertical structures and controlled environment which maximize the production and efficiency from minimum area. The proposed system is cultivating in a carefully controlled environment where saplings will be planted on nutrient media on chambers illuminated with LED lights. This study will also integrate the vertical farming structure with the Internet of Things. Hence, automate the farm activities with less human intervention and eradicate all the issues that may

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arise in between and control the farming activities from remote places using the mobile application. Crop Yield forecasting features use Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to improve the yield.

Improved Video Compression using Variable Emission Step ConvGRU

based Architecture

Sangeeta and Preeti Gulia Department of Computer Science & Applications, Maharshi Dayanand

University, Rohtak, India

The Video content over the internet is growing rapidly. There arises need of more powerful and proficient video compression techniques to handle beating stress of voluminous video over the limited bandwidth. Traditional video compression mechanisms are hand designed and their architecture is amalgamation of different modules designed in such a way that different modules are optimized individually instead of achieving end to end optimization of whole network. The positive upshots of deep learning in image compression emerged a breakthrough for video compression as well. ConvGRU, a Convolutional recurrent neural network comprises of productive edges of both RNN and CNN. The proposed architecture comprising of ConvGRU as basic building blocks implemented in both fixed and variable bit rate models. The experimental results demonstrated that randomized emission step ConvGRU based architecture gives better performance and provides a base framework for further optimization enhancements.

Analysis of Lightweight Cryptography Algorithms for IoT Communication

Navdeep Lata and Raman Kumar

I.K. Gujral Punjab Technical University, Kapurtala

Ubiquitous sensing enabled networks have transformed the living chores into a modern living standard in which each move is automated. The dependency of humans on IoT is increasing every day, so the IoT networks must be efficient and secure. Secure communication in the Internet of Things (IoT) networks are an active research issue. Cryptography is one of the solutions for secure data transmission. In IoT networks, designing a cryptography algorithm is a challenging task due to resource-constrained devices. So, when we talk about cryptography in IoT, this term transforms into "lightweight cryptography". This paper presents a survey of lightweight cryptography algorithms in the form of a stream cipher, block cipher, symmetric encryption, and asymmetric encryption.

Comparative Design Analysis of Optimized Learning Rate for

Convolutional Neural Network

Rashmi Gandhi, Dr. Udayan Ghose and Manushree Gupta

GGS IP University, Delhi, India

Learning Rate is the most critical hyper-parameter to tune while training neural networks. It determines the amount of weight that should be adjusted for loss gradient. Therefore, choosing an optimal learning rate is a challenging task for neural networks. In this paper, various learning rate schedules like constant learning rates, step decay and exponential decay algorithm are implemented. Exponential decay comes out to be the best learning method among the constant, step and exponential decays. Further, best learning rate is used to optimize Adam, RMSProp and SGDM algorithm. These are implemented on the CIFAR-10 data set. Various combinations of different parameters of exponential decay such as decay

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steps, decay factor, initial learning rates are used to train the model, and their optimal learning rate and losses are observed. The parameters have minimum loss considered the best one. The comparative analysis of the model is performed based on accuracy, loss function and a number of iterations. The experimental results show that Adam with exponential decay based learning method achieving higher accuracy at batch size 128, decay steps 400, and dropout rate 0.3 with minimum loss 0.5. So, it is proven the best optimization method for training convolutional neural networks.

Reliability Evaluation of Distribution System based on Interval type-2

Fuzzy System

Galiveeti Hemakumar Reddy1, Akanksha Kedia2, Shruti Ramakrishna Gunaga1, Raju More3, Sadhan Gope4, Arup Kumar Goswami2 and Nalin B

Dev Choudhury2

1MVJ College of Engineering, Bangalore, India 2National Institute of Technology Silchar, India

3Maulana Azad National Institute of Technology Bhopal, India 4Mizoram University Aizawl, India

The accurate estimation of distribution system reliability depends on requisite equipment failure data availability. Equipment failure rate is not deterministic because of lack of data availability and uncertainty of system failures. Failure rate is considered as a fuzzy number to incorporate the uncertainty of failures. In this paper, interval type-2 fuzzy systems (IT2FS) is proposed to handle the two fold uncertainty of equipment failure i.e. objective and subjective uncertainty. Monte Carlo simulation is employed to estimate equipment failure probability and sampling method is used to determine the failure possibility. The membership functions for equipment failure rate are approximated using this failure possibility. Fuzzy Importance Index (FII) is used to rank the impact of a component failure on reliability. The proposed assessment method is tested and validated on RBTS bus 2 reliability test system.

Atmospheric Temperature Prediction Using Ensemble Deep Learning

Technique

Ashapurna Marndi and Gopal Krishna Patra CSIR Fourth Paradigm Institute, India

For the research of climatology, temperature is the indispensable parameter that plays significant role in measuring climate changes. Climate change is required to understand the variability in climate and its impact on various activity such as agriculture, solar energy production, travel, climate conditions in extreme cold or hot places, etc. Thus, time and again researchers have tried to find methods to predict temperature with increasing accuracy. Hitherto many scientists used to perform on high powered HPC systems using complex and dynamic climatology models to compute temperature at future timestamps. However, in such models, the accuracy plays game adversely with prediction lead time such that sometimes with increasing lead time as need of application, the model fails to result in acceptable outcomes. With advancement of Artificial Intelligence, now a days, Deep Learning technique and especially Long Short Time Memory (LSTM) is providing better solution with higher efficiency for timeseries prediction problems. We have enhanced the base LSTM and introduced certain changes to solve the prediction of timeseries data, specially temperature in alignment with scientific application. The experimental outcome of our proposed technique convincingly justifies the logic behind the enhanced techniques and also it has been also compared with exiting approaches to be found unmatched.

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Text Recognition Using Convolution Neural Network for Visually Impaired

People

Sunanda Dixit1, Nidhi Munavalli2, Anuja Velaskar2 and Apurva Waingankar2 1BMS Institute of Technology and Management, Bengaluru, Karnataka

2A. P. Shah Institute of Technology, Thane, Maharashtra

In image processing and document analysis, accurate identification and recognition of the text is very important. It is difficult to recognise some alphanumeric symbols which are similar in appearance. Hence, the best Deep learning Neural Network - Convolution Neural Network is used. In continuation to this, Optical Character Recognition (OCR) is coupled with voice-over utility that reads out the text recognised and conversion to braille is done to help the blind or visually impaired people. The experimental results show high level precision for detection of machine printed documents. The model can handle different font types and sizes and provides values accuracy of 90.34%.

Tweets Reporting Abuse Classification Task: TRACT

Saichethan Reddy1, Kanishk Tyagi1, Abhay Anand Tripathi1, Ambika Pawar2 and Ketan Kotecha2

1Indian Institute of Information Technology, Bhagalpur 2Symbiosis Institute of Information Technology, Pune

In recent decades we have noticed a considerable increase in reports or confession posts of abuse victims on twitter. Most of the time victims do not report it to their guardians or the concerned authorities. Teenagers and minorities are the most affected group of abuse. Part of these victims tweets about their incident to let go of pain and suffering or as a cry for help. Identifying such reports is challenging, to address such an important task In this study, we define a new task, Tweets Reporting Abuse Classification Task (TRACT), and construct a new dataset related to the online abuse reporting. A detailed comparison with existing supervised models and detailed error analysis explores the merit of our proposed model.

Design of Automatic Answer Checker

Ekta Tyagi, Deeksha and Lokesh Chouhan National Institute of Technology, Hamirpur, India

In a world where technology has grown to be a mega thing impacting the lives of mankind to a greater extent, a common man is still having huge difficulty in adapting the available technical solutions immediately. There are so many basic tasks that are performed in the day-to-day lives of people which takes a lot of effort and time and can be automated with the help of technology but still, humans are more prone to do them manually. One such example is taking subjective examinations by students and then checking of the answer-sheets manually by the teachers. So, to ease the process of subjective answers checking, a web application has been developed that can check the subjective answers written by the students with the efficiency same as that of a manual checking by teachers. Using this software, teachers can set the question-paper and students can take their examination with getting their answers checked simultaneously. The model's development is settled with the Word2Vec skip-gram model, NLTK modules, and Flask Python framework whereas all the data has been stored in the form of relational database tables which is inserted and fetched through the SQL queries. The Word2Vec skip-gram model is used to make the vocabulary of terms and to find their corresponding word embeddings. The paper proposes the development of a model that will make time-consuming and tiresome tasks like manual answer checking as simple as possible.

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Community Detection using Fire Propagation and Boundary Vertices

Sanjay Kumar and Rahul Hanot Delhi Technological University, India

Community detection in complex networks deal with grouping related nodes together and plays a vital role to understand the functioning of the system in real-life situations. Community detection is classified as an NP-hard problem. Various algorithms are currently available for it but the problem with these existing algorithms is either they have high in time complexity or they have not able to partition the network perfectly. In this paper, we propose a novel community detection algorithm that works in two phases. In the first phase, we apply fire propagation technique in which choosing an arbitrary vertex as the core vertex and connecting an adjacent vertex to it and shapes a community this is similar to how fire spreads in real-life situations. In the second phase, we use the result of the first phase of an overlapped community and detect all boundary vertices which are belongings to more than one communities and assign them to the single community based on the weight that each core vertex assign to that particular boundary vertex using Dijkstra distance and the count of the adjacent vertex that belong that community. The proposed algorithm performs well as compared to label propagation and walktrap algorithm in terms of modularity score using various synthetic and real-world datasets.

Towards Grammatical Evolution Based Automated Design of Differential

Evolution Algorithm

Indu M T and Shunmuga Velayutham C Amrita School of Engineering, India

Differential Evolution (DE) is a robust evolutionary algorithm that has been applied for various real world optimization problems. However the performance of DE depends on the optimal choice of variation operators and control parameters. The dizzying choice of heuristics for choosing mutation strategies, crossover operator and control parameters make DE design a challenging task for a practitioner. We present a meta-evolutionary approach with Grammatical Evolution (GE) to evolve effective parameter configurations for classical differential evolution algorithm. It has been observed that the GE evolved DE configurations performed competitively on the chosen six standard benchmark functions. This work is a preliminary step towards automating DE algorithm designs, which has the potential to relieve a user from the painful task of trial-and-error manual designs.

Video Surveillance System with Auto Informing Feature

Ekta Tyagi, Deeksha, Lokesh Chouhan, Vikas Sahu, Chandranshu Malhotra, Shubham Poddar and Jayash Verma

National Institute of Technology, Hamirpur, India

The present document represents a thorough study of the making of an efficient surveillance system along with a feature of automatically informing the owner about the suspicious movement. In this moving world normally people are suffering from the availability of time, so if any crime has happened at the site, it will take many days of searching for finding the actual presence of criminals. And thus a good chance for those burglars to flee away in order to protect themselves. For making the task possible chose Python as the weapon for this battle and used different efficient techniques like COCO dataset for getting labeled and annotated images, LabelImg for making the annotation set of images, TensorFlow, Object Detection API for object Detection and Faster RCNN for training as faster RCNN has shown the highest accuracy for the COCO dataset so far. The owner can be informed in two ways: Either send a message to him via mail or phone or call at the time of suspicious image capturing. Here both of these cases are used: For mail, the task is done via SMTP and for phone calls Twilio is used

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which provides us registered phone no. and can make both outbound and inbound calls. After using all the mentioned things and making the model in a way described above it was found that faster RCNN is much more accurate than the other conventional methods. The results have been very well as RCNN show 86.7% accuracy and 100% has come out with the informing module as there simply the mail will be sent to the one whose mail is given in the code and the same is for Twilio calling.

An Optimal Feature based Automatic Leaf Recognition Model using Deep

Neural Network

Aditi Ghosh1 and Parthajit Roy2 1Techno India Hooghly, India, 2The University of Burdwan, India

In this paper, we have proposed a deep neural network based pattern recognition model for automatic classification of color leaves. The study initially uses eighteen features for its basic model and thereafter made a two-step improvements. These have been achieved through dimension reductions and through optimal feature selections. The proposed models have been tested with benchmark color leaf images and the results have been critically analyzed. The performances of the proposed models have been compared with that of other six standard models.

Input Parameter Optimization with Simulated Annealing Algorithm for

Predictive HELEN-I ion Source

Vipin Shukla1, Vivek Pandya1, Mainak Bandyopadhyay2 and Arun Pandey2

1Pandit Deendayal Petroleum University, India

2Institute for Plasma Research, HBNI, Gandhinagar, India

HELicon Experiment for Negative ion source (HELEN-I) setup is created for the production of negative hydrogen ions. High-density plasma generation involves tuning several input parameters which are correlated and holds the non-linear relationship with each other so tuning input parameters such as input power (W), magnetic field (Gauss), and gas pressure (mTorr) to achieve de-sired plasma density becomes a challenging and time-consuming task for the operator. Instead of relying on analytical expressions, the proposed method uses deep learning techniques supplemented with experimental data for the selected parameters. The datasets is collected by conducting real-time experimental on hydrogen ion Helicon plasma source. This paper presents an Artificial Neural Network (ANN) based predictive Negative Hydrogen Ion Helicon Plasma source (HELEN-I). The predicted plasma density results are validated using the experimental outputs. Further, the input parameter (RF power) is optimized using simulated annealing (SA) algorithm. The model proposed in this study enables the operator to produce high-density negative hydrogen plasma with reduced human-machine interventions.

LRSS-GAN: Long Residual Paths and Short Skip Connections Generative

Adversarial Networks for Domain Adaptation and Image Inpainting

Shushant Kumar and Chandrasekaran K

National Institute of Technology Karnataka, India

We propose a new architecture Long Residual and Short Skip GAN (LRSS-GAN), which allows semantic gap reduction and a deeper flow of visual concepts for image generation

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function. Compiling a labeled image database to train modern machine learning algorithms is very costly. Another attractive alternative is to provide synthetic data where real-world annotations are automatically generated. Most of the trained models on translated images generally fail quite often, to learn features when compared with the original images. To address these failures, previous work has introduced domain transformation algorithms in unsupervised environment that extract features from the images that are not dependent on the domain. In this work, we present a novel architecture that learns in an unsupervised manner. LRSS-GAN built upon base UNet architecture uses longer residual paths between encoder and decoder branches and also incorporates short skip connections to pass feature information deeper into the model. This architecture improves the model adaptation of features from source domain images to look like they are from a target domain. Specifically for MNIST and MNIST-M dataset, it was successfully able to adapt the features learned in before the one in the latter. The mean classification accuracy obtained was 98.56%, which surpasses state-of-the-art accuracy. Also, to analyze the efficacy of the proposed architecture, it was also assessed for image in painting scenario on the CelebA dataset by masking major regions of the face, where it gave a mean loss of 0.0122 with better practical results. Experiments proved the theoretical concepts proposed, resonates with this research's aim and effectiveness in its application.

Load Equilazation Technique and Security Mechanism for Cloud

Performance

Dushyantsinh Rathod1, Ramesh Prajapati2 and Harshil Joshi3

1Alpha College of Engineering and Technology, Gujarat, India 2L. J. Institute of Technology and Engineering, India

3Devang Patel Institute of Advance Technology and Research, India Cloud enrolling provides association of sharing info aggregation most extreme and computer authority over the net and computer network. Cloud advancement has another procedure for wide scale dissipated problem solving with 5 star introduction. it's a complete of wide get-together of topographically and ceaselessly condemned heterogeneous assets for illuminating broad scale info and strategy targeted problems. The heterogeneous technique for mastermind choosing quality makes the advantage association associate basically problematic occupation. Quality association conditions a major a part of the time be a part of quality presentation, quality observation, quality inventories, quality provisioning, charge detainment, arrange of involuntary limits and association level association works out. The exibility, security and assurance of cloud organizations is improved by grasping client driven access management and character the officers plans. Late advances in cryptography and internet progresses empower U.S. to structure security game plans that provide the purchasers a lot of noteworthy detectable quality and management over their cloud-based resources and straight away some security and insurance considerations and fears connected with cloud perspective. By the day's finish, these game plans create it possible for the dares to urge cloud perspective and re-proper hardware, institution and programming whereas at the same time maintaining order over their characters and knowledge CloudSim may be a .NET-based lattice recruitment structure that provides the Runtime gear and programming condition needed to form machine framework. Within the wake of fixing system condition, we've got targeted existing amendment in accordance with within discontent in CloudSim very well, and have chosen the quality clarifications behind disappointments in it. To manage barely of the apparent wants we've got planned bolster boss plan. Just in case there ought to rise a happening of disappointment of the focal regulator, bolster boss can expel its management and stays from the framework to bite the mud. Assets area unit fast and heterogeneous in nature. That the pile of every advantage fluctuates with amendment in course of action of matrix in addition the event of disappointment of focal points is altogether more and more basic. Aside from the error of the benefits impacts the work execution mortally. This makes stack dynamical and adjustment to within frustration dynamically basic if there ought to arise an incident of preparing framework.

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Automatic Recognition of ISL Dynamic Signs with Facial Cues

Sruthi C J, Karan Soni and Lijiya A

National Institute of Technology Calicut, India

Sign language recognition systems help the hearing and vocally impaired people to communicate with the verbally speaking community. This paper proposes an Indian sign language (ISL) recognition system capable of recognizing isolated double-handed dynamic gestures involving facial expressions. The recognition system uses skin color segmentation for segmenting face and hand regions from the video frames. Histogram of oriented gradients (HOG) features are computed over the segmented frames, and Multi-class neural network is used for classification. The proposed system can recognize ten single and double-handed dynamic gestures with an accuracy of 81\%. The most important thing regarding this work is that it does not use any sophisticated sensors or even does not use any computationally intensive methods like hand tracking and still achieve a remarkable accuracy.

Enhance The Prediction of Air Pollutants Using K-Means++ Advanced

Algorithm with Parallel Computing

Chetan Shetty1, Seema Shedole2, Rosemary Binoy M2, Swetha Sree T S2, Ujwala G2, Geetha V H2 and Sowmya B J2

1HCL Technologies, India

2M S Ramaiah Institute of Technology, India

Air pollution being the bane of our existence, has led to environmental degradation affecting the lives of plants and animals and causing a lot of health disorders to humans. Air quality forecasting of concentration of main pollutants like O3, NO2, NOx and NMHC(Benzene) can reduce the effect of this dreadful pollution. Our methodology uses the K-means++ advanced algorithm, which is an enhancement of K-means++ algorithm, for smarter initialization of centroids using probability theory and weight assignment. This will do away with the drawback of bad clustering due to the original way of choosing random initial centroids and also reduces the number of iterations in the initialization algorithm and consequently the total time taken by the algorithm. The mentioned technique is then applied on real time Big Data using parallel computing techniques such as MapReduce using Apache Spark. The accuracy, number of iterations and execution time are taken as our evaluation parameters and the proposed methodology is evaluated. The final outputs show that it is indeed possible to improve the accuracy and quality of clustering. Also, parallel computation that divides the task and runs it side by side drastically reduces the whole execution time. Hence, the solution proposed is better in terms of being reliable, fast and helps predict the level of pollution so that it can help us take precautionary measures.

Passive Motion Tracking for Assisting Augmented Scenarios

Pranay Pratap Singh and Hitesh Sharma

Gujarat Technological University, India

In this paper we have performed a detailed overview and noted the observation on implementing low-cost passive motion tracking for creating the data set and processing it to be used as an asset in various Augmented, Virtual or Mixed Reality environments. We used versatile protocols like I2C and used MEMS (Micro Electro-Mechanical Systems) architecture based sensors-MPU6050. For data sorting we used Bubble and Merge Techniques in Python environment, and balanced and positively controlled the tolerances and noises using Kalman

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Filter Algorithm. Finally we implemented the generated and processed data file in a virtual space in the blender engine to give a predefined function to the asset.

Plasma Density Prediction for Helicon Negative Hydrogen Plasma Source

using Decision Tree and Random Forests Algorithm

Vipin Shukla1, Mainak Bandyopadhyay2, Vivek Pandya1 and Arun Pandey2

1Pandit Deendayal Petroleum University, Gandhinagar, Gujarat 2ITER-India, Institute for Plasma Research (IPR), Gandhinagar, India

Plasma density of an ion source is an important parameter to be measured for the characterization of an ion source. Measurement of plasma density depends on several input parameters such as input power, gas pressure, magnetic, plasma potentials and electron temperature. In the absence of a direct mathematical relation-ship between the input parameters and plasma density, measuring plasma density becomes a time-consuming task and has been done through invasive experimental methods. This paper utilizes decision tree and random forest algorithms for developing a predictive model of a negative hydrogen Helicon plasma source to estimate the plasma density from the RF input power, magnetic field (B) and gas pressure. For this purpose, a datasets having 162 samples have been applied for training, testing and validation of the proposed models. A comparative analysis of proposed models has been done through by comparing of root mean square error and the coefficient of determination (R2) between the measured and the predicted values of plasma density. The obtained results show that the random forests model has shown better results and able to predict plasma values closer to the actual experimental results. In addition, random forests algorithm has the capability to interpret the complex non-linear relationships between the influential input parameters and the output.

Efficient Fuzzy Similarity based Text Classification with SVM and Feature

Reduction

Shalini Puri Poornima College of Engineering, Jaipur, Rajasthan, India

With the generation of enormous data day by day, the need of feature reduction has tremendously increased in recent years in the field of text classifica-tion. This paper proposes Concept based Mining Model using Threshold (CMMT) and Fuzzy Similarity Based Concept Mining Model Using Feature Clustering (FSCMM–FC) models to classify English text documents into pre-defined categories using SVM. Various existing algorithms are compared to check the impact of feature reduction and accuracy concerns. The comparison of distinct thresholds in CMMT found 5 as best with maximum reduction. The re-ductions 95.72% and 90.58% are obtained in CMMT and FSCMM-FC for Ya-hoo! documents, respectively. The best reduction 96.09% is achieved for radio astronomy documents in FSCMM-FC. Furthermore, accuracy is estimated as 65% and 67% in CMMT, and 66.68% and 87.5% in FSCMM-FC for Reuters 21578 and Yahoo! test documents respectively, therefore, FSCMM-FC outper-forms CMMT in effective memory usage and classification accuracy.

Disease Prediction from Speech using Natural Language Processing and

Deep Learning Methods

Rahul Kumar, Sushant Pradhan, Tejaswi Rebaka and Jay Prakash National Institute of Technology Calicut, India

Disease prediction is an active area of research, which supports to make the best possible medical care decisions, help reduce the overhead work of a doctor and provide proper facility

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in vicinity. In this work, we predict the diseases from a set of symptoms extracted using various Natural Language Processing and Deep Learning methods. The selected models take input from the user as speech and output the most probable disease corresponding to the symptoms. In most of the existing works, structured data are used for disease prediction. However, in this work, we propose the method which takes unstructured data as input in the form of speech in layman terms. Here, unstructured data is converted into suitable structured form to give input to Deep Learning model. We enhence the deep learning model by incorporating dense layer. Here, the context dependencies are stored using LSTM Layer and the output is processed using a Dense layer as the fully connected layer. The proposed method outperforms over competitive algorithms for the chosen data set.

Classification of Human Postural Transition and Activity Recognition

Using Smartphone Sensor Data

Priyanka Kolluri, Pranaya Chilamkuri, Naga Deepa Ch. and Padmaja V.

VNR Vignana Jyothi Institute of Engg. and Technology, Hyderabad, India

The global statistics on health monitoring show that 15% of humans in the world live with some kind of disability. One of the fundamental rights of such people is existence of companion to supervise their day-to-day activities. Smartphones are beneficial for this purpose of monitoring human activity and provide means that give high accuracy while estimating activities. Sensors such as accelerometer and gyroscope available in any smartphone can be used to record basic activities like walking, sitting, standing, etc. with the ones that involve postural transitions for example sit to lie, stand to sit etc. This paper proposes a methodology in order to overcome the difficulty that lies in recognition of such activities with transitions using filtering, windowing and outlier detection. The results of the proposed methodology show that Logistic Regression classifier provides highest accuracy of 96.16%.

A Blockchain based Multi-Layer Framework for securing Healthcare Data

on Cloud

Roshan Jameel, Harleen Kaur and M Afshar Alam

Jamia Hamdard, India

The sharing of Electronic Health Records (EHRs) helps in improving the quality and accuracy of the medical diagnosis and clinical researches. But at present, there is no particular way for ensuring the safe and secure collection and storage of the healthcare data, also in some cases the data is not even available in digitized form. The current demand of Healthcare industry is patient centric healthcare management system that has the capability to trace the data transactions with minimum amount of interference of intermediaries. The Healthcare data is big and heterogeneous therefore it is preferred to be stored on Cloud Environments for hassle free availability and real time access. In the past few years, Blockchain has emerged as a technology capable of handling data that is growing unremittingly; that gives it the potential to effectively handle the Medical data and transform the Healthcare Industry. In this paper, a Blockchain-based infrastructure is proposed for securely handling the healthcare data transactions. The blockchain network works on distributed and decentralized peer-to-peer environment that does not have any central controlling authority. Blockchain provides a network based on trust and transparency that allows the maintaining of the historical transactions in immutable manner, which makes it suitable for Healthcare Domain.

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ComVisMD - Compact 2D Visualization of Multidimensional Data:

Experimenting with two different datasets

Shridhar Dandin1 and Mireille Ducasse2

1Sarala Birla University, Ranchi, INDIA

2IRISA-INSA Rennes, FRANCE

Interpreting data with many attributes is a difficult issue. A simple 2D display, projecting 2 attributes onto two dimensions, is relatively easy to interpret but provides limited help to see multidimensional correlations. We propose a tool, ComVisMD, which displays, from a dataset, five dimensions in compact 2D maps. A map contains cells, each one represents an object from the dataset. In addition to the usual horizontal and vertical projections and the use of colors, we offer holes and shapes. In order to compact the display, we partition objects according to two dimensions, grouping values of each dimension into up to 7 categories. In this paper, we present two case studies covering two different domains, a cricket player dataset and a heart disease dataset. The cricket dataset has 15 attributes and 2170 objects. We show how, using ComVisMD, correlations between variables can be found in an intuitive way. The heart disease dataset has 14 attributes and 297 objects. Blokh and Stambler, in the June 2015 issue of "Aging and Disease", state that individual attributes show little correlation with heart disease. Yet in combination the correlation improves dramatically. We show how ComVisMD helps visualize those multidimensional correlations between 4 attributes and heart disease diagnosis.

Design of Compact Size Tri-Band Stacked Patch antenna for GPS and

IRNSS Applications

Nitin Kumar Suyan1, Fateh Lal Lohar1, Yogesh Solunke2 and Chandresh Dhote3

1Engineering College Jhalawar, India

2IIITDM Kancheepuram, India

3Shri GSITS Indore, India

This paper presents the design aspects and simulation of tri-band stacked patch antenna for GPS and IRNSS applications. The antenna has two microstrip circular patch layers, and employs patches which have different radius to achieve the tri bands resonance characteristics. The conducting body of antenna have two dominant resonant modes from patch1 and patch 2 for L5 and L1 applications and one higher order resonant mode for S band application. The subtract is used Rogers RO4003C for antenna with dielectric constant 3.55 and loss tangent 0.0027. Coaxial probe feed location is selected such a way to excite two dominant modes and one higher order mode. The ground segment stations uses Tri-band i.e. L5 band (1164.45-1188.45MHz) and S-band (2483.50- 2500.00 MHz) for IRNSS application and L1-Band(1563.42-1587.42Mhz) for GPS application. The proposed tri-band patch antenna is developed for these ground stations to centre to both the IRNSS and GPS Systems. Simulated results of proposed antenna show a good performance such as a compact size of 80x80x4.73 mm3 and satisfactory 10-dB return loss bandwidths (1.1525-1.1947 MHz, 1.5573-1.5980 MHz and 2.449-2.507 MHz) are 42.2 MHz, 40.7 MHz and 66.5 MHz respectively and desirable radiation patterns with the antenna gain of nearly 3.81dB at 1.175 MHz , 5.44 dB at 1.575 MHz and 2.44 dB at 2.507 MHz The proposed antenna is designed and simulated by the help of CST Studio Suite.

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Investigation on Error Correcting Channel Codes for 5G New Radio

Sonu Lal1, Roopam Gupta1 and R K Arya2

1UIT Rajiv Gandhi Proudhyogiki Vishwavidyalaya, Bhopal 2MP Council of Science & Technology, Bhopal

In the era of digital communication various codes been used for channel coding like Convolution, Trellis, Turbo, LDPC, and polar codes for error detection and correction. Convolution and turbo codes are the earlier codes used for high energy efficiency for short length communication like machine type communication (MTC), control purpose of the broadband (MBB) & also for reliability and latency purpose (URLLC). But these codes have the limitation of working for short block length messages transmission. To solve these problems, LDPC codes are used constructed from the protograph matrix. It is helpful to generate and create a strong matrix form of long block length data transmission with a sparse matrix. LDPC code has a good error performance near to Shannon limit at high SNR with low complexity than other codes but does not perfectly satisfy the URLLC requirement in 5G NR Communications. A new candidate Polar code is introduced, is a sequential control channel used for low latency for short length message transmission. The purpose of the paper to survey the comparison between flexibility, complexity, reliability, throughput, latency, and efficiency between various channel codes. Specifically, comparison of LDPC and Polar Codes, which may lead to key channel codes for data and control in the fifth generation. 5G NR communication should stand with high flexibility, low complexity, high reliability, high throughput, and very important much low latency for mMTC and URLLC. Hence in this paper, we are comparing all the forward error-correcting codes and find optimum channel code for 5G mobile communication system.

A Deep Learning Technique for Automatic Teeth Recognition in Dental

Panoramic X-Ray Images Using Modified Palmer Notation System

Fahad Parvez Mahdi and Syoji Kobashi

University of Hyogo, Japan

Dental healthcare providers need to examine a large number of panoramic X-Ray images every day. It is quite time consuming, tedious and error-prone job. The examination quality is also directly related to the experience and the personal factors i.e. stress, fatigue etc. of the dental care providers. To assist them handling this problem, a residual network-based deep learning technique i.e. faster R-CNN technique is proposed in this study. Two kinds of residual network i.e. ResNet-50 and ResNet-101 are used as the base network of faster R-CNN separately. A modified version of Palmer notation (PN) sys-tem is proposed in this research for numbering the teeth. The modified Palmer notation (MPN) system does not use any notation like PN system. The MPN system is proposed for mainly three reasons; (i) teeth are divided into total eight categories, to keep this similarity, a new numbering system is pro-posed that has the same number of category, (ii) 8-category MPN system is less complex to implement than 32-category universal tooth numbering (UTN) system and with some preprocessing steps, MPN system can be converted into 32-category UTN system, and finally (iii) for the convenience of the dentist i.e. it is more feasible to utilize 8-category MPN system than 32-category UTN system. The method achieved 0.963 and 0.965 mean average precision (mAP) for ResNet-50 and ResNet-101, respectively. The obtained results demonstrate the effectiveness of the proposed method and satisfy the condition of clinical implementation. Therefore, the method can be considered as a useful and reliable tool to assist the dental care providers in dentistry.

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Super Resolution of Level-17 Images using Generative Adversarial

Networks

Balaji Prabhu B V1, Nikith P Salian2, Nikhil B M2 and Omkar Subbaram Jois Narasipura1

1Indian Institute of Science, India

2Manipal Institute of Technology, India

The GAN (Generative Adversarial Networks) Deep Learning models are being widely used in the field of image processing and its applications such as image generation, feature extraction, image recovery and Image Super Resolution to name a few. Image Super Resolution has a board range of applications like satellite and aerial image analysis, medical image processing, compressed image/video enhancement etc. This work implements an Image Super Resolution using Generative Adversarial Network for super resolution of level 17 low resolution geospatial images obtained from IRS (Indian Remote Sensing) imagery. The results show that, the generated super resolution image can recuperate photo-realistic textures from low resolution input pictures. The performance of the model is evaluated with qualitative measure indices such as Structural Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR). The performance metric demonstrates that, the model can generate images as close to that of the high resolution image and it also has finer details.

Analysis of the Messages from Social Network for Emergency Cases

Detection

Olga Tsarenko, Yana Bekeneva and Evgenia Novikova

Saint-Petersburg Electrotechnical University, Russia

Analysis of the publicly available data from social networks may benefit many practical applications, and analysis of text messages to reveal emergent cases is an urgent and important task. Wide spread of social media may be used in order to detect and manage rescue operations. In this paper an approach to analysis of public messages from social network for understanding the emergency scenario developing and managing rescue operation is proposed. This approach allows to filter data according to the settings chosen by researcher and visualize the result of analysis using two options: placing number of messages on the map and creating a pie diagram. The designed application was tested on data from VAST Mini-Challenge 3 2019 describing social network response on earthquake. The experiments show that the proposed approach allows detecting the most dangerous areas in the city and visualize the results.

Color Image Watermarking Technique using Principal Component in

RDWT Domain

Roop Singh1, Alaknanda Ashok2 and Mukesh Saraswat3

1Uttarakhand Technical University Dehradun, India

2GB Pant University of Agriculture and Technology, Uttarakhand, India

3Jaypee Institute of Information Technology, Noida, India

Protection of multimedia contents and false positive (fp) problem are a challenging issue for

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the researchers. The digital watermark is a scheme which resolves such issues by inserting watermark. To mitigate the same, a secure color watermarking scheme using SVD in the RDWT domain is presented. Arnold transform is performed on a watermark image to attain a scrambled watermark which enhances the security. The RDWT transform is computed over the cover image to embed the watermark into the high frequency sub-band (HH). A scrambled watermark image is directly inserted into the principal component (PC) of the cover image. The experimental results validate that the proposed scheme has better imperceptibility and robustness.

A Framework for Disaster Monitoring using Fog Computing

Raja Sree T National Institute of Technology Calicut, India

Critical crowd-sourced, Internet of Things (IoTs) data collected from various geographical sources such as sensors, mobile devices, vehicles, humans, etc. are assessed and analyzed timely for the effective disaster management. Cloud computing is a widely used technology to analyze the crowd-sourced data of a particular geographic region. The time taken to analyze these data is large, large end-end delay, and Quality of Service (QoS) degradation. Hence, fog computing is used to analyze these critical crowd-sourced data i.e., for latensive sensitive applications. This paper highlights the disaster monitoring system that analyzes the crowd-sourced data using fog computing, which avoids the latency and delay jitter for time-critical applications. The proposed framework demonstrates that it achieves very low execution time and less delay than conventional cloud computing model.

Query Auto-Completion Using Graphs

Vidya Dandagi and Dr. Nandini Sidnal

KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Belgaum, Karnataka

Search engines have a complete dependency on the query auto-completion. Query auto-completion is a process that suggests a group of words for every click dynamically. Query suggestions help in formulating the query and improve the quality of the search. Graphs are data structures that are universal and extensively used in computer science and related fields. Graph machine learning approach is growing rapidly with applications such as friendship recommendation, social network and, information retrieval. The main goal is to represent the data into a graph that is nodes and edges. A su-pervised Logistic Regression model is proposed to compute the accuracy. Node2vec algorithm learns the feature representation of nodes in a graph. It is derived by word embedding algorithm Word2vec. The evaluation metric is the semantic similarity score and the area under the receiver operating characteristic curve (ROC_AUC). Semantic Similarity between the two nodes helps in query auto-completion. Areas under curve (AUC) help in measuring the prediction accuracy and to know the outcomes of completion.

Comparative Analysis of Load Flows and Voltage Depended Load

Modeling methods of Distribution Networks

U Kamal Kumar and Varaprasad Janamala

CHRIST (Deemed To Be University), India

The growing demand for electricity in the world present scenario, the power sector is consider the challenge to fill the required growing rate of demand. In distributed networks the R/X ratio varies gradually in respect with the demand, so far the performance of the network decreases rapidly. The losses and uncertain voltage profile of those systems initiates the damage of the

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connected devices at the network. To enhance the performance it is essential to integrate the Distributed Energy Resources (DER) to the Distribution Network (DN), the conventional network has been upgraded from a passive network to an active network. Before the integration of DER, it is mandate to analyze the network with load flow technique and model the system based on the demand. In this paper the placement of DER is analyzed with the comparative results of various distributed networks and verified through different load flows and voltage depended load modeling methods. And verifies 15, 33, 69 and 85 bus distributed networks performance characteristics are evaluated for their different convergence criteria by increasing the R/X ratio, by varying the tolerance values and analyzing the voltages at break points.

Efficient Machine Learning Algorithm for Cancer Genome Classification

Nithya V, Chandra J, Siddharthan S and Mallieswari R

CHRIST (Deemed to be University), Bengaluru, India

Genome Analysis for precision medicine has a transformative impact on personal health, economics, and national productivity. Machine learning combined with genome analysis can anticipate viable outcomes in very little time for the treatment planning of a cancer patient. Precision medicine is a technique that can be applied to cancer treatment, where the treatment is given by identifying the specific DNA/RNA sequence and modified for an individual, to avoid the adverse effects of treatment. The information obtained from the human genome can be utilized drastically to improve human health, especially for cancer treatment while doing chemotherapy. Genome analysis helps the physicians to plan effective cancer treatment with accurate medication to avoid unfriendly responses to the medicine. Applying machine learning techniques is an attempt to identify the severity of the cancer level of the patient is in which stage and to help the doctors to plan the treatment effectively. So, the outcome of the analysis is to collect the right genomic information which can help to address the issues effectively for identifying the problems associated with cancer patients which will help the patient as well as the doctors to do the treatment effect for improving the lives of the cancer patient.

Attitude Control in Unmanned Aerial Vehicles using Reinforcement

Learning – A Survey

Varun Agarwal and Rajiv Ranjan Tewari

University of Allahabad, India

Unmanned Aerial Vehicles (UAVs) have a great potential in various fields. With ongoing research, the day is not far when they would be directly impacting our lives. The promise of deep learning techniques like reinforcement learning has created a window of opportunity for their use in a plethora of tasks, like the attitude control problem of UAVs. Attitude of a UAV is the angle at which it is flying relative to the ground. Attitude control is the management of orientation of a UAV with respect to the inertial frame. In this paper we have surveyed reinforcement learning algorithms to learn attitude control of UAVs and be able to take decisions in unforeseen circumstances. Reinforcement learning is the branch of deep learning where there is no human intervention in training the model. Instead the system learns over time by trial and error. Since navigation in the air presents scenarios that maybe new and unexpected for a UAV, reinforcement learning presents a viable option for their use in attitude control in them.

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Intelligent Simulation of Competitive Behavior in a Business System

Dmytro Chumachenko, Sergiy Yakovlev, Ievgen Menyailov, Ksenia Bazilevych and Halyna Padalko

National Aerospace University "Kharkiv Aviation Institute",Ukraine

As part of the study, a model has been developed which consists in the fact that society is divided into three groups that have different behavioral strategies in different situations: the strategies of “Hawk”, “Dove” and “Law-abiding”. The proposed model is implemented in the MatLAB Simulink package. With the help of the model, the dynamics of the population development using each of the strategies, the speed of "reproduction" of the sys-tem players applying each of the strategies are calculated. The system is tested for stability.

Minimizing the Subset of Features on BDHS Dataset to Improve Prediction

on Pregnancy Termination

Faisal Ahmed1, Shahana Shultana1, Afrida Yasmin2 and Junnatul Ferdouse Prome3

1Daffodil International University, Bangladesh

2Jahangirnagar University, Bangladesh

3Z. H. Sikder University of Science and Technology, Bangladesh

Predicting the pregnancy termination and controlling the child mortality rate has always been a great challenge for third world country. Our target of this research to extract out best subset of feature for predicting the pregnancy termination with improved accuracy compare to the previous research. To facilitate this noble purpose, we have carried out an extensive research that find out the most contributing attributes of pregnancy termination in Bangladesh. Using Bangladesh Demographic and Health Survey (BDHS), bivariate and multivariate analyses were performed, 2014 data which is diminished by examining features that show interesting details to find out the recent causes for pregnancy termination. However, for finding out the intended features first demographically feature selection per-formed with Weka provided visualization tools and secondly Weka provided feature ranking attribute evaluators such as Correlation, Gain Ratio, One R, Symmetrical Uncertainty, Information Gain, Relief are used. After minimizing the subset of features on pregnancy termination then we apply three traditional ma-chine learning classifiers (Naïve Byes, Bayesian Network, and Decision Stump) along with the hybrid method which shows better performance in terms of performance metrics. This research improved accuracy 10.238% for Naïve Byes, 8.2657% for Bayesian Network, 3.5853% for Decision Stump and 9.03% for Hybrid.

A Comparative Analysis of Software Development Models from

Traditional to Present-day Approaches

Poonam Narang and Pooja Mittal

Maharshi Dayanand University, Rohtak, Haryana

The primary aim of software engineering is to deliver high quality, well documented software that too within budget and schedule. A typical software development life cycle includes different phases viz. Requirement gathering and analysis, Design, Coding, Testing, Implementation, Installation, Operations and Maintenance phase. Several types of development models exist with different frameworks which gives immense need for choosing

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the right methodology to deliver a successful software.These frameworks can be generalized into three categories viz. Traditional Development Models, Agile Methodologies and DevOps. These three frameworks can be further classified into two different scenarios that are Linear and Iterative. Models like Waterfall fall under Linear category so are less flexible whereas Iterative models are much adaptable to change. In general, each upcoming model targets to minimize the discrepancies encountered in the previous model. Under this research work all these frameworks are compared in respect to different facets. Hereby, we have considered Waterfall, Prototype, Iterative, Spiral models under traditional framework and RAD, Scrum, Kanban, XP models with Agile Methodologies. A comparative Analysis of Traditional and Agile Methodologies is also made with DevOps. This research paper also includes the discussions followed by different models comparative analysis. This review research will be useful for researchers/students to gain the understanding of the modus operandi of different software development models.

Improved Image Super Resolution using Enhanced Generative Adversarial

Network a comparative study

Balaji Prabhu B V and Omakar Subbaram Jois Narasipura Indian Institute of Science, Bangalore, India

Super resolution using Generative Adversarial Networks is an approach for improving the quality of imaging system. With the advances in Deep Learning, Convolutional Neural Networks based models are becoming a favorite choice of researchers in image processing and analysis as it generates more accurate results compared to conventional methods. Recent works on image super resolution have mainly focused on minimizing the mean squared reconstruction error and able to get high signal-to-noise ratios. But, they often lack high-frequency details and are not as accurate at producing high resolution images as expected. With the aim of generating perceptually better images, this paper implements the Enhanced Generative Adversarial model and compares with Super Resolution Generative Adversarial model. The qualitative measures such as Peak-Signal to Noise-Ration and Structural Similarity indices were used to assess the quality of the super resolved images. The results obtained prove that, Enhanced GAN model is able to recover more texture.

Smart Lady E-Wearable Security System for Women Working in The

Field

Shuchi Dave1, S. D Purohit2, Ritu Agarwal3, Aman Jain1, Deepak Sajnani1 and Saksham Soni1

1Poornima College of Engineering, India 2Rajasthan Technical University, Kota, 3MNIT, Jaipur

Women's security is still a big issue in our society. Women’s in the rural Rajasthan are very hard working; they spend their day in farms and also manage household stuff. To provide safety to the woman as the ornament wore by them could be modified with our suggested design named “Borla” , this ornament is having an E-wearable security system so women’s on fields would not afraid of any emergency as the location is traceable. Technology as their Armor will protects them and provides a lot of freedom which consists of live location with a continuous server update, high definition camera along with instant capture and voice recording. It also uses sensor like environmental alcohol sensor for environmental alcohol measurement. An alert message will be generated for a specified condition which is then transmitted from the transceiver to the mobile phone .This device can be connected to the mobile phone via Bluetooth or can use the radio frequency transceiver.

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Black Hole-White Hole Algorithm for Dynamic Optimization of

Chemically Reacting Systems

Prasad Ovhal1 and Jayaraman Valadi2 1Savitri Bai Phule Pune University, India, 2Flame University, India

Black hole (BH) meta-heuristic optimization is a nature-inspired algorithm which mimics the behavior of black holes. This Black hole algorithm framework is implemented in this work to solve dynamic optimization of some benchmark problems encountered in chemically reacting systems. We have employed two different algorithm types which include a standalone Black hole algorithm and an algorithm which combines exploration-exploitation trade off in the algorithm. This tradeoff is enabled through the concept of White Hole. We have also used both piecewise linear and piecewise constant control profiles. The algorithms proposed are simple and are easy to implement. Case studies studied show that the novel algorithms are robust and compare very well with existing algorithms.

Self-Supervised Learning Approaches for Traffic Engineering in Software

Defined Networks

Deva Priya Isravel, Salaja Silas and Elijah Blessing Rajsingh

Karunya Institute of Technology and Sciences, India

Software defined networking is a mechanism that supports automation and flexibility to network management by employing the network programmability. The programmability feature of SDN enables to simplify network management and enhance the network performance. To achieve performance enhancement, it essential to have a reliable traffic classification mechanism to monitor the stream of packets and flows in the network. To enforce any traffic engineering principles, traffic classification is the basic block to building a system that offers a quality of service. So in this paper, a self-supervised learning approach to classify traffic is proposed to optimize the scheduling of traffic to obtain reduced latency and packet loss. The SDN controller applies the supervised machine learning algorithm to analyze traffic behavior and identify the normal traffic and anomaly traffic based on irregular traffic patterns. The various supervised learning approaches are experimented to find a learning mechanism that obtains better results in detecting exploits that degrade the performance and schedules traffic such that resources are allocated efficiently by avoiding congestion and packet loss.

Automated Cooperative Robot for Screwing Application

K Vigneshwaran, V R Jothi Sivam and M A Ganesh

Thiagarajar College of Engineering, India

The development of cooperative robots is reaching new heights in this decade. Cooperative robots in industries help to improve the production rate, performance, efficiency and robustness in variety of tasks. The cooperative operation by two modular robots is performed for various applications like assembly, welding and painting. The cooperative robot operation in assembly still exhibits numerous open challenges, including the effective coordination of the robots. This article explains a novel control architecture to control two modular industrial robots with open and closed architecture controllers for screwing application. In this process, screwing operation is performed with MH5LS and GP12 industrial robots with two different controllers FS100 and YRC1000. A master controller is used to instruct the robots. The trajectory of the screwing is experimentally verified.

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Evaluation of Electric Vehicle Charging Cost Using HOMER Grid

Hemani Paliwal and Vikramaditya Dave College of Technology and Engineering, India

Electric vehicles are automobiles that run with electric motor, instead of IC engine. Electric vehicles are clean alternative to traditional vehicles that are fossil fuel driven. Also electric vehicles are more efficient, quieter in operation and have better performance and control as it has less moving parts than IC engine vehicles. Therefore electric vehicles are vehicles of this new era which focuses to lessen global emission but the reduction in pollution depends upon the type of electricity used for charging these vehicles, again if it is fossil fuel generated electricity then it will be again adds on pollution and cost. Hence for charging, usage of renewable energy sources (Solar, wind or hydro) is the best alternative as it is locally available and reduces emission too. Challenges in the growth of electric vehicles are high cost, charging infrastructure and lack of electricity. In this paper HOMER grid software is used to optimize electric vehicle charging with Hybrid Renewable energy sources. Case of a hospital is considered, which is electrifying their ambulance with the goal of less consumption of oil, lowest utility bill and reduced carbon emission.

PUNER - Parsi ULMFiT for Named-Entity Recognition in Persian Texts

Fazlourrahman Balouchzahi and H. L. Shashirekha

Mangalore University, India

Named Entity Recognition (NER) is an information extraction technique to identify and classify named entities automatically in any natural language text. These entities are predefined and generic like name of location, organization, date, time, etc., or they can be very specific like the example with the resume. NER is a key component in many Natural Language Processing systems, such as question answering, information retrieval, relation extraction, etc. Applications of NER include extracting important named entities from various texts such as academic, news and medical documents, content classification for news providers, improving the search algorithms, etc. Most of the NER research works explored is for high resource languages such as English, German and Spanish. However, very less NER related work is done in low-resource languages such as Persian, Indian, and Vietnamese due to lack of or less annotated corpora available for these languages. Among the mentioned languages very few works have been reported for the Persian language NER till now. Hence, this paper presents PUNER – a Persian NER system using Transfer Learning (TL) model that makes use of Universal Language Model Fine-tuning for NER in Persian language. This is accomplished by training a language model on Persian wiki text for identifying and extracting named entities from the given Persian texts. The proposed model is compared with the conventional Machine Learning (ML) models and Deep Learning (DL) models using BiLSTM by applying five word embedding models namely, Fasttext, HPCA, Skipgram, Glove, and COBOW. All the models are evaluated on two Persian NER datasets and the results illustrate that TL model performs better than ML and DL models.

Linguistic Classification using Instance Based Learning

Priya S Nayak1, Rhythm Girdhar1, Shreekanth M Prabhu2 and K S Srinivas1

1PES University, India

2CMR Institute of Technology, India

Traditionally languages are divided into families and each language family is modeled as a tree. This modeling is however restrictive and gets influenced by the world-view of linguists.

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We would like to evolve the inter-relationship between languages by looking at inter-relationship between words and word clusters and let the structure evolve on its own. For that we use Instance based learning with the help of a variety of distance measures such as Levenshtein distance. The words can be grouped into 3 categories. Whenever a word whose source language is definitely not known it is classified by using known classes of words it is close to using chosen distance measures. Simplest distance measure compares just words the way they sound. Next, we can link words which have some structural relationship or kinship between them. We can also pair up words with semantic relationships. Finally, we may evolve a structure which may lead to a network of languages that goes beyond trees or hierarchies which are restrictive. This can show that Sanskrit as a language is central to all of Indo-European Languages or directly connected to the majority of them. To build corpus we can use existing datasets as well as use Google Translate and Thesaurus. We make use of KNN to lay out the discrete word space for languages. In this work we focus on Indian Languages to start with which includes Northern and Southern Languages as well as English.

Multi Class Support Vector Machine Based Household Object Recognition

System Using Features Supported by Point Cloud Library

Smita Gour1, Pushpa B. Patil2 and Basavaraj Malapur1

1Basaveshwar Engineering College, India 2BLDEA's VP Dr PG Halakatti College of Engineering & Technology, India

The proposed system aims to design and develop object recognition system with the help of Point Cloud Library (PCL). Object recognition problem is addressed with three stage mechanism. Initially using PCL the object image undergoes segmentation methods which makes image suitable for extracting the features. In the second stage segmented image is used to extract suitable shape based features that can separate each object type. The last step involves classification/recognition of object of the particular type using Support Vector Machine (SVM). The system reached the expected results using Point Cloud Library (PCL) and Support Vector Machine (SVM) as a classifier. The system has given 94% accuracy for 10 different household object types. 10 samples for each object type are used for training the SVM and to perform testing, complete different 5 samples from training samples are considered.

A Review of Nature-inspired Algorithm-based Multi-objective Routing

Protocols

Ruchi Kaushik1, Vijander Singh2 and Rajani Kumari3 1Amity University Rajasthan, Jaipur, 2Manipal University Jaipur, Jaipur

3JECRC University, Jaipur

Wireless ad-hoc networks have a significant role in every field like medical, electrical, civil, architecture, vehicles, law, and routing for making the optimized route through which transmit the data packet from one channel to another channel. Security plays a crucial role to make route more secure and reliable. There are some crucial features in wireless networks such as dynamic topology and open-source wireless-media, which may suffer from security issues. This paper covers a detailed review of technologies, which are useful for determining various issues of security. Nature-inspired algorithms improve security in the field of networks. This paper shows a complete review of existing security protocols along with their merits and demerits.

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Hopping Spider Monkey Optimization

Meghna Singh, Nirmala Sharma and Harish Sharma

Rajasthan Technical University, Kota

The area of swarm intelligence has been increasing extensively and new algorithms are getting formulated inspiring from the collective behavior of the social animal societies. One of such an algorithm is the spider monkey optimization (SMO) algorithm evolved by Bansal [3] et. al. in 2014. It is based on Fission-Fusion socially structured animals. It has been considered as a well-balanced algorithm and has outperformed many of the other competitive algorithms. In this paper, to advance it’s diversification and intensification capability, a new alternative of SMO algorithm namely Hopping Spider Monkey Optimization (HSMO) has been proposed on being inspired by the hopping mechanism of a grasshopper. To testify the efficacy and to derive how accurate this newly proposed variant is, it is proved over 15 standard benchmark functions. The so obtained numerical results are analyzed and contrasted with numerous state-of-art algorithms accessible in the research and hence validate the newly proposed approach.

A Coverage and Connectivity of WSN in 3D Surface Using Sailfish

Optimizer

Thi-Kien Dao1, Ngoc-Cuong Nguyen2, Shi-Jie Jiang1, Truong-Giang Ngo3 and Trong-The Nguyen1

1Fujian University of Technology, Fuzhou, China

2Cyber security and Counter high -Tech Crime, Viet Nam

3Thuyloi University, Viet Nam

Coverage and connectivity in the 3D surface of sensor nodes as the mountain is a critical problem in a wireless sensor network (WSN). This paper suggests a solution to multi-connectivity deployment WSN coverage based on combining Sailfish optimizer (SFO) with the characteristic of 3D surface topography. The target area divided into mesh grids of a size to establish multi-connectivity of every grid; the cover set constructed through the direction gradient probabilistic model, and connected graph and the joint points to graph within the grid by optimizing of SFO. A large number of simulation experiments show that the proposed method can cover the target region and guarantee the connectivity and robustness of the network.

An Automatic Emotion Analysis of Real Time Apple Mobile Tweets

Kalaivani Anbarasan1 and S Vijayalakshmi2

1Saveetha School of Engineering India

2Saveetha Institute of Medical and Technical Sciences, Chennai, India

Sentimental Analysis is one of the hottest topic on social media and used in a wider sense in social media. Sentiment analysis is all about expressing their emotions and opinions on a particular topic or on the particular product in the form of reviews and tweets. Sentiment analysis can express their emotions in the form of positive, negative or in the form of neutral. Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an

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overview of the wider public opinion behind certain topics. The applications of sentiment analysis are broad and powerful. The ability to extract insights from social data is a widely adopted by organizations across the world. Sentiment analysis gives the organizations a sneak peek into their customer’s emotions, they can be aware of any crisis that’s to come well in time and manage it accordingly. The focus of the paper is to help the customers to get customers emotional view based in real time mobile phone tweets. The real time apple mobile phone tweets are collected through twitter API. The collected datasets is applied to pre-processing strategies to remove inconsistent and redundant factors and visually represented as word cloud. The sentiment scores are obtained using nrc-sentiment dictionary and emotions are classified into basic emotions and tweets are classified as positive and negative. The performance of the results are analyzed and also compared with existing techniques. The proposed system out-performs and proved to be efficient in terms of precision and recall measures.

Flexible Bolus Insulin Intelligent Recommender System for Diabetes

Mellitus using Mutated Kalman Filtering Techniques

Nagaraj P, Muneeswaran V, Sabik Ali R, Sangeeth Kumar T, Someshwara A. L and Pranav J

Kalasalingam Academy of Research and Education, India

Diabetes mellitus is a long-standing sickness that is affected by self-control insulin for blood glucose levels. Anyway, the evaluation of the proper insulin dose doesn’t easily affect the blood sugar metabolic, which is mainly used to graph sugar levels for optimum. This study shows a Flexible Bolus Insulin Intelligent recommender system for diabetes mellitus using Mutated Kalman Filtering techniques that is used to find the positive or negative continual blood sugar observation system. The recommended advance is verified by the UVa/PADOVA is similar to 11 virtual age subjects. It has been verified by the two different types of flexible bolus that are computed and the execution of the achieved has been related to the default bolus insulin doses that can be simulated, assuming the optimum. The obtained result is determined by the proposal system quickly assembles the optimum bolus insulin dose, and it is used for modifying the bolus insulin calculator without any instability.

Detection of Parkinson’s disease from hand-drawn images using Deep

Transfer Learning

Akalpita Das1, Himanish Shekhar Das2, Arijeet Choudhury3, Anupal Neog3 and Sourav Mazumdar3

1GIMT Guwahati, India, 2Cotton University, India

3Jorhat Engineering College, India

There is no cure available for Parkinson’s disease, which imparts the importance for early detection of this disease. With early detection of this disease and with proper medication a patient can lead a better life. In this paper, our aim is to process the hand-drawn images drawn by Parkinson’s disease affected patients using deep learning architectures. Different types of images such as spiral, wave, cube, and triangle shapes have been utilized in this work. Convolutional neural networks seem to be effective in achieving better accuracy for the hand drawn images. Deep convolutional neural networks are investigated in the context of computer-aided diagnosis of Parkinson’s disease. In this paper, three approaches are considered. In first approach, all types of images are fed into various pre-trained models which are trained from the scratch. In second approach, exactly same techniques are being repeated with the exception that fine tuning have been performed using transfer learning. In this work,

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for first and second approach, we have considered variants of convolutional neural networks such as VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception and Inception-ResNet-v2 respectively. In third approach, two shallow convolutional neural networks have been proposed. All the experiments for all the three approaches mentioned above are conducted on two different datasets and the performance evaluation of each examined network is addressed for both the datasets. Experimental results show that the superior performance is achieved in case of the fine-tuned network compared to other approaches.

Adaption of Smart Devices and Virtual Reality (VR) in Secondary

Education

Ahmadh Rifai Kariapper, Pirapuraj Ponnampalam, Suhail Razeeth, Mohamed Nafrees Abdul Cader and Fathima Roshan Abdul Majeed

South Eastern University of Sri Lanka. Sri Lanka

Learning is an essential process for all individual spans from cradle to graveyard. Biology is one of the subjects where most of the students have been facing difficulties to study and memorize its content due to its complicated environment and structures. The effective education to the students is necessary and it could be achieved by the adopting of new technological concepts and application to their syllabi. Virtual reality is one of the outstanding modes to the students to learn and memorize the subject quickly. In this research, quantitative and qualitative methods were used to determine the factors of Adaption of Smart Devices and Virtual Reality (VR) in Secondary Education with a pretest-posttest group. Additionally, the reliability of the collected data was analyzed with Cronbach's Alpha test. 200 students from 10 schools were randomly selected for the pretest while 100 were selected for the posttest among these 200 students; where these 100 students allowed to use VR technology for their educational purposes and these samples were selected using a stratified random sampling technique. In conclusion, the research shows that, the performance of the students who used the VR tool in their studies was significantly increased; male students are very fond of technology and tools, thus score more marks compare with female students. At the meantime, female students are needed to be trained (to use the VR system) more in order to learn the subjects. Also, it is suggested to introduce this VR system to all the other subjects with proper training to both students and teachers not only education sector but also in various domain where the training is essential part to the employees.

Deep Learning Technique for Predicting Optimal ‘Organ At Risk’ Dose

Distribution for Brain Tumor Patients

Ashish Kumar1, Lekshmy P. C1, Niyas Puzhakkal2 and Abdul Nazeer K.A1

1National Institute of Technology Calicut, kerala

2MVR Cancer Centre & Research Institute, Kozhikode, Kerala

Radiation therapy is one of the major cure techniques for all types of cancers. During treatment planning, the planners resolve dose distributions of the target tumor and neighbouring organs based on their experience. A 3D-deep learning model is designed which predicts optimal Organ-At-Risk (OAR) dose distributions for brain tumors. In radiation treatment planning, Dose Volume Histogram (DVH) is a key mechanism for comparing different treatment plans. DVH analyses dose to various volumes of Organs-At-Risk (OAR). DVH curves can help to figure outa dose-response model for different vital organs. This work proposes the inclusion of spatial factors for each patient in the model. While most of the current approaches use a random set of DICOM images of the patient's tumor to train the model, we experiment with a U-Net based deep learning architecture on a fixed set of CT and contoured image slices of each patient grouped together for fast and precise classification of

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image pixels to get radiation dose distributions. The U-net model predicted dose with 83.07% validation accuracy and 87.5% testing accuracy trained with Mean Square Error (MSE) loss function. The validation accuracy of the model using Dice coefficient similarity is 74.56%.

An Optimal Feature Selection Approach based on IBBO for

Histopathological Image Classification

Mukesh Saraswat1, Raju Pal1, Jagdish Chand Bansal2, Roop Singh3, Himanshu Mittal1 and Avinash Pandey1

1Jaypee Institute of Information Technology, India

2South Asian University, New Delhi, India

3Uttrakhand Technical University, Dehradun, Uttrakhand, India

The automated methods for the categorization of the histopathological images is very useful in disease diagnosis and prognosis. However, due to complex image background and morphological variations these images generate very large feature vectors which make the automated classification task difficult. Therefore, in this paper, a new feature selection method based on improved biogeography-based optimization algorithm is proposed to select the prominent features. These feature are further used for the classification process. The elimination rate of the proposed method is 74.25% and 71.28% on BreaKHis and BACH histopathological image datasets respectively. A comparative analysis has been performed using different classifiers on the selected features and simulation analysis depict that the IBBO-based feature selection and classification method outperforms.

A Fractional Model to Study the Diffusion of Cytosolic Calcium

Kritika Kritika1, Ritu Agarwal1 and Sunil Dutt Purohit2 1Malaviya National Institute of Technology, Jaipur

2Rajasthan Technical University, Kota

Calcium is a universal intracellular second messenger. It is a critical regulator of cardiac myocyte functions. Here we developed a mathematical model to describe the calcium transients in cardiac myocyte. Since, diffusion is a basic transport process involved in the evolution of many non-equilibrium systems toward equilibrium. The rationale of this work is to study the diffusion of calcium in the cytosol of cardiac myocytes. The model is incorporating the fractional derivative and the fractal derivative with respect to the time and space variable respectively. The Crank-Nicolson finite difference scheme is applied to obtain the solution and further numerical simulation is done for various fractal and fractional order.

Initialization of MLP Parameters using Deep Belief Networks for Cancer

Classification

Barış Dinç, Yasin Kaya and Serdar Yıldırım Adana Alparslan Türkeş Science and Technology University, Turkey

Deep Belief Networks (DBN) are deep neural network structure consisting of a collection of Restricted Boltzmann Machines (RBM). RBM are two-layered sim-ple neural networks which are formed a visible and hidden layer respectively. Each visible layer receives a lower level feature set learned by previous RBM and passes it through to top layers turning them into a more complex feature structure. In this study, the proposed method is to feed the training parameters learned by DBN to Multi-Layer Perceptron as initial weights instead of starting them from random points. The obtained results on the bioinformatics cancer dataset show that

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using initial weights trained by DBN causes more successful classification results than starting from random parameters. The test accuracy using proposed method increased from 77.27% to 95.45%.

A Classification Model for Software Bug Prediction Based on Ensemble

Deep Learning Approach Boosted with SMOTE Technique

Thaer Thaher and Faisal Khamayseh

Palestine Polytechnic University, Palestine

In the software development process, the testing phase plays a vital role in assessing software quality. Limited resources pose a challenge in achieving this purpose efficiently. Therefore, early-stage procedures such as Software Fault Prediction (SFP) are utilized to facilitate the testing process in an optimal way. SFP aims to predict fault-prone components early based on some software metrics (features). Machine Learning (ML) techniques have proven superior performance in tackling this problem. However, there is no best classifier to handle all possible classification problems. Thus, building a reliable SFP model is still a research challenge. The purpose of this paper is to introduce an efficient classification framework to improve the performance of the SFP. For this purpose, an ensemble of Multi-layer Perceptron (MLP) deep learning algorithm boosted with Synthetic Minority Oversampling Technique (SMOTE) is proposed. The proposed model is benchmarked and assessed using sixteen real-world software projects selected from the PROMISE software engineering repository. The comparative study revealed that ensemble MLP achieved promising prediction quality on the majority of datasets compared to other traditional classifiers as well as those in preceding works.

Modeling the Relationship Between Distance and Received Signal Strength

Indicator of the Wi-Fi Over the Sea to Extract Data in Situ From a Marine

Monitoring Buoy

Miguel Angel Polo Castañeda1, Constanza Ricaurte Villota1 and Danay Vanessa Pardo Bermúdez2

1Instituto de Investigaciones Marinas y Costeras “José Benito Vives De Andréis” INVEMAR, Colombia

2Universidad del Magdalena, Colombia

One of the most common situations in marine monitoring through buoys is fault analysis, data download and transmission, among others, this can be difficult since it involves the manipulation of these equipment at sea, for this reason connecting the buoy wirelessly through Wi-Fi is an option, on the basis that many technological equipment such as tablets, computers and cell phones are compatible with this technology. Although it is interesting to have wireless communication in situ, there is no known marine monitoring system that uses this technology as a means of extracting data. Therefore, this study is based on finding the maximum distance at which devices can be connected for the visualization and extraction of data without losing information in the process, from the modeling of the equation that relates the received signal strength indicator with the separation distance of the monitoring system, allowing the development of a prototype buoy that includes the extraction of data at sea wirelessly.

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Signal Processing Techniques for Coherence Analysis Between Ecg and

Eeg Signals with A Case Study

Rajesh Polepogu and Naveen Kumar Vaegae

Vellore Institute of Technology, India

Each organ of the human body has some synchronism, affiliation and connection to one another. Typically, the biomedical signals electrocardiography (ECG) and electroencephalography (EEG) are considered as the main signals for analysis and handling of circulatory and central nervous system related anomalies. In this scenario, non-invasive, cost-effective, minimal effort and precise continuous monitoring of aforementioned systems is needed. Stand-alone ECG and EEG signals utilized in the investigation of corresponding abnormalities may lead to insufficient analysis and inadequate results in many situations where the abnormalities are interrelated to both the systems. Both ECG and EEG signals are utilized simultaneously in neurocardiology for study of heart and brain related problems. Thus, signal processing of such signals are of utmost importance. Coherence analysis is an important signal processing technique to correlate ECG and EEG signals. In this paper, magnitude squared coherence (MSC) and phase coherence (PC) are used to determine the coherence between the ECG and EEG signals. Also, various mathematical and experimental techniques used to determine the coherence between circulatory and nervous system are discussed concurrently to signify the necessity of adaptive and intelligent signal processing techniques. A case study is presented with statistical results and graphical illustrations to validate the use of signal processing techniques. The results of proposed techniques can be used to construct an intelligent decision making system for early prediction and detection of various abnormalities of neuro-cardiac systems.

Data Classification Model for Fog-Enabled Mobile IoT Systems

Aung Myo Thaw1, Nataly Zhukova2, Tin Tun Aung1 and Vladimir Chernokulsky3

1ITMO, Russia

2St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Russia

3Saint Petersburg Electrotechnical University "LETI" St. Petersburg, Russia

Fog computing has become the key solution that allows reduce latency and energy consumption when collecting and processing data in IoT systems. The advantages of fog computing can also be used to collect and process data in new mobile IoT systems. It is a challenging task because these systems contain multiple mobile devices that generate huge amounts of heterogeneous data. In many cases the data is redundant. The same data can be send several times. Such data is not useful for end users. Therefore, we propose a new model for data collecting and processing in fog-enabled mobile IoT systems. Instead of collecting and processing huge amounts of data using cloud technologies, the data is collected and processed on fog nodes. The new model is based on using classification algorithms, in particular, K-nearest neighbor algorithm. It is used to classify heterogeneous data generated by mobile devices. This allow reduce the amount of data that is transmitted to the cloud for further processing and storage. The proposed model can be considered as is an effective intermediary that allows collect and process data in mobile IoT systems.

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Assessing the Role of Age, Population Density, Temperature and Humidity

in the Outbreak of COVID19 Pandemic in Ethiopia

Amit Pandey1, Rajesh Kumar1, Deepak Sinwar2, Tesfaye Tadele1 and Linesh Raja3

1College of Informatics, BuleHora University, Bule Hora, Ethiopia

2Department of Computer and Communication Engineering, Manipal University Jaipur, India

3Department of Computer Applications, Manipal University Jaipur, India

Abstract: First case of Corona Virus Disease 2019 (COVID19) was reported in Ethiopia on March 13, 2020. The acute respiratory syndrome coronavirus 2, also known as SARS-CoV-2, was responsible for the disease. As of May 19, 2020, overall of 365 confirmed cases have been recorded in the several zones of Ethiopia, causing 5 deaths. As per the current statistics the pandemic has progressed to its next phase and has started transmitting via local transmissions. The situation is alarming, and researches should be carried out revealing the factors affecting the transmission of COVID-19 in the society. Initially, Correlation analysis was performed to find any strong association between the environmental factors and the transmission rates of COVID-19 in the various parts of Ethiopia. The factors such as age, temperature, and humidity and population density in various zones of Ethiopia were used against the reported number of COVID-19 cases to plot the regression graphs, showing their effects on the transmission of COVID-19 in Ethiopia. This study shows that there is a strong correlation between the age, population density, Temperature, Humidity, (Temperature / Humidity) ratio and the number of confirmed COVID19 cases in Ethiopia. The values obtained from the Pearson Correlation (µ) analysis are stated as under, µ [Temperature] [COVID19 Cases_Fst 19 Days] = 0.421831, µ [Humidity] [COVID19 Cases_Fst 19 Days] = - 0.815642, µ [(Temperature / Humidity)] [COVID19 Cases_Fst 19 Days] = 0.602778, and µ [Population per sq KM] [COVID19 Cases_Fst 19 Days] = 0.375511. Also, the values obtained from the Spearman’s Rank Correlation (φ) analysis between the age and the number of COVID19 cases are stated as φ [Age Groups] [COVID19_Total Cases] = - 0.416667. The transmission rate of COVID19 pandemic varies in different zones of Ethiopia, subjected to the factors age, population density, Temperature, Humidity and (Temperature/Humidity) ratio. Further, the study shows that these factors have substantial impact on the transmission rate of COVID-19 pandemic in Ethiopia.

A Review on Dimensionality Reduction in Fuzzy and SVM based Text

Classification Strategies

Shalini Puri Poornima College of Engineering, Jaipur, Rajasthan, India

Dimensionality reduction in text classification is the compact form of the high dimensional data with detailed feature analysis and evaluation of distinct dimensions. It generally requires searching methods to obtain the optimal set of good and important features which reduces system computational overhead, computational cost and complexity while enhancing the text classification accuracy. Data augmentation is another approach to analyze data efficiently and effectively. DR is also related to the curse of dimensionality problem, in which due to high data dimensionality, classifier learning is comparatively difficult and increases the computation time exponentially with respect to the number of variables used. Such problems can be avoided by analysing the features effectively by re-moving harmful, unimportant, irrelevant and confusing features; thereby, developing the high performance classifier and reducing space, time and computational complexities.

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Multi-Objective Teaching–Learning-based Optimization for Vehicle Fuel

Saving Consumption

Trong-The Nguyen1, Ngoc-Cuong Nguyen2, Hong-Jiang Wang3, Truong-Giang Ngo4 and Thi-Xuan-Huong Nguyen1

1HPU, Viet Nam

2Cyber security and Counter high –Tech, Viet Nam

3FJUT, China

4TLU, Viet Nam

A multi-objective paradigm arises a viable approach to solving complex problems of optimization based on integrating multiple disciplines. This paper sets forth a new multi-objective teaching-learning based optimization (short for MTLBO) for the issue of automotive fuel consumption. The vehicle's fuel consumption is related to the cost of traveling by gasoline and the current traffic conditions, e.g., congestion, roads. The least use of fuel and the shortest routes are modeled as the objective functions for candidates' solutions on the automobile navigation route. A road transportation system is built based on Wireless Sensor Network (WSN) fitted sensor nodes and the car fitted with the Global Positioning System (GPS). The results of the simulation are contrasted with the approaches of the Dijkstra and the A* algorithm. Results from experiments show that the proposed method increases precision.

Soft Computing Tool for Prediction of Safe Bearing Capacity of Soil

Narhari Chaudhari1, Neha Chaudhari2 and Gaurav Bhamare1

1Gokhale Education Society's R. H sapat college of engineering, Management studies & Research, Nashik.

2Matoshri College of Engineering & Research Centre, Nashik.

Safe bearing capacity (SBC) of soil plays vital role in structural designing of civil engineering projects. SBC of soil is an initial requirement to structural designers. Conventional plate load test method for knowing safe bearing capacity of soil is costly, cumbersome and time-consuming experimental setup. Soft computing technique of linear genetic programming (LGP) is a useful tool which can be employed in geotechnical engineering branch as an alternative tool for prediction of safe bearing capacity of soil. In the pre-sent work, seven soil parameters like angle of internal friction, cohesion of soil, % silt & clay, % sand, % gravel, specific gravity of soil and safe bearing capacity of soil were collected from various sites located in seven districts of Maharashtra state in India. First six soil parameters were set as input parameters and safe bearing capacity of soil was set as output parameter in formulating LGP models. Discipulus software was employed for model formulation. Total 125 sites data sets were employed of which first 88 values were used to train the models and remaining 37 values were used for validation purpose. Good results were observed for GP4 model with four inputs (r =0.88) which worked slightly better than GP5 model with five inputs. Results were poor for GP3. GP4 model can be used for prediction of safe bearing capacity of soil by merely using simple four input parameters which can be obtained easily, quickly and with lesser cost from soil labs of academic institutions or private soil labs.

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Effective Teaching of Homogenous Transformations and Robot Simulation

using Web Technologies

Yashaswi S Kuruganti, Apparaju S. D. Ganesh, D Ivan Daniels and Rajeevlochana G Chittawadigi

Amrita School of Engineering, Bengaluru

Robotics has gained importance over the time. It has become an important interdisciplinary course in the engineering education. The position and orientation of a robot, or any part of a robot are described in many ways. One of the most commonly used methods is Homogenous Transformation Matrix (HTM). One of the main topics of robotics is kinematics, which relates to motion of the joints and the end-effector of the robot. Understanding these concepts re-quire 3-dimensional visualization of vectors, transformations and motion, which are difficult to teach using conventional teaching methods, such as using black-board. There exist many teaching software for effective three-dimensional visu-alization. One such attempt is RoboAnalyzer, for which the last author is one of the main developers. RoboAnalyzer is a Windows based applications and new developments to make similar modules for internet browsers using web technol-ogies have been reported in this paper.

An Automated Citrus Disease Detection System using Hybrid Feature

Descriptor

Bobbinpreet Kaur1, Tripti Sharma1, Bhawna Goyal1 and Ayush Dogra2

1Chandigarh University, Mohali, Punjab, India

2Ronin Institute, Montclair, NJ 07043, USA

An automated system for Citrus disease detection relies upon using Computer aided tools. By minimizing manual error in disease detection and diagnosis, the Computer Aided Diagnostic (CAD) tools have acquired a major role in the resources needed to boost and increase the efficiency in production yield.The CAD tools have the properties of automatically speeding up the system's decision-making capability on the complex disease data. Since these plant diseases can be devastating in terms of economic loss and loss of nutrition, the need for disease detection is growing at an early stage in order to meet human nutritional needs.The use of these computer-aided methods has enabled reducing the loss of production and early disease detection. This article proposes a machine learning based methodology to automate the process of disease detection thereby reducing the manual efforts. While designing the complete system the optimization and improvement at every sub stage is considered in this article so that the complete system possess high levels of accuracy and thereby can equip farmers to deploy disease detection in a better way.

Adaptive Fuzzy Algorithm to Control the Pump Inlet Pressure

Sivakumar Palaniswmay1, Sandhya Devi2, Angamuthu A1, Vinoth Kumar B1, Anushyni S K1 and Jeenbritto M3

1PSG College of Technology, India

2Kumaraguru College of Technology, India, 3ISRO, India

The pump inlet pressure is controlled using PID controller. Since the process is non-linear, the performance of the pump inlet pressure control could not be achieved in a straight line as per the requirement. Instead, the pump inlet pressure was controlled with saw tooth pattern during

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a rapid change in pump flow rate. In case of conventional PID controller, when controller gain (Kp) is very high, there will be a faster rise time leading to an overshoot. If it is very low, the transient dynamic behavior of the system will be slow. Due to the above limitation, achieving best control was not possible for the non-linear process using conventional PID algorithm. Hence it is proposed to develop an Adaptive Fuzzy control algorithm, which will accept inputs as an error and also rate of change of process variable, and calculate the change in output based on the fuzzy control algorithm. In this project, Adaptive Fuzzy control algorithm is developed and implemented to control the pump inlet pressure to the required value.

Clustering High Dimensional Datasets using Quantum Social Spider

Optimization with DWT

Jetti B. Narayana, Satyasai Jagannath Nanda and Urvashi Prakash Shukla

Malaviya National Institute of Technology Jaipur, Rajasthan, India

In last few years the quantum mechanics inspired algorithm Quantum Particle Swarm Optimization has become popular among the swarm intelligence researchers. In this algorithm instead of the Newton inspired random walk, a type of quantum motion has been used in the search process. Social-Spider Optimization a relatively new intelligent algorithm inspired from the social behavior of spiders, especially their communications on the web for search of prey and for mutual sexual interaction towards growth of their family. In this paper, a Quantum Social-Spider Optimization (QSSO) is formulated where the search agents (spiders) are assumed to be bounded in a quantum potential well, through which solution gets attracted towards the global optima. The QSSO has been employed to cluster five low dimensional and four high dimensional datasets. To deal with high dimensionality, two level discrete wavelet transform (DWT) has been applied for feature reduction. The use of transformed domain features with DWT significantly reduces the computational complexity to almost half of that achieved by QSSO, while marginally affecting the accuracy of the algorithm. The statistical results obtained with three validating indecies such as Accuracy, Silhouette Index and Run time justify the superior performance of the proposed approach over benchmark APSO, RGA and original SSO algorithm.

Intuitive Control of 3 Omni-wheel based Mobile Platform using Leap

Motion

Devasena Pasupuleti, Dimple Dannana, Raghuveer Maddi, Uday Manne and Rajeevlochana G Chittawadigi

Amrita School of Engineering, Bengaluru

Autonomous and remotely controlled mobile robots are used extensively in industries and are slowly becoming a norm in day-to-day life. They can be manually controlled using devices such as joysticks or by using mobile phone applications, through Bluetooth or similar technologies. In this paper, the authors propose usage of Leap Motion device which can track hands of human users. By using the gestures, the human can operate a mobile robot. For the demonstration, the authors propose its usage on a 3 Omni-wheel based mobile platform. First, the mathematical model of the robot was formulated and simulated in V-REP software for various types of motion. Then, a physical prototype was developed, which was integrated with both Bluetooth based mobile phone application and Leap Motion device. Field trials and survey of 30 people was carried out, where both methods were perceived to be of similar ease of use and intuitiveness. However, the authors feel the results of Leap Motion control may improve with subsequent usage by the users.

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Abnormal Event Detection in Public Places by Deep Learning Methods

Mattaparti Satya Bhargavi, Bibal Benifa Jv and Rishav Jaiswal

Indian Institute of Information Technology Kottayam, India

Surveillance in public places has become an important aspect of modern lifestyle and security purposes. This is due to an increase in the number of crimes, mischievous activities, and abnormal events. The monitoring process should be automated because of the excessive time consumption associated with the manual monitoring process. In general, there are many deep learning methods exist for the classification process of abnormal events. This article provides a comparative analysis of three state-of-the-art deep learning methods used for abnormal event detection. The three methods namely, Convolutional Long Short Term Memory (CLSTM) autoencoders, Convolutional Autoencoders (CA) and One-class Support Vector Machines (SVM) are tested on a benchmark dataset UCSD Ped-1 which contains 34 training videos and 36 testing videos. Out of the three methods of implementation, one-class SVM offers the highest Area Under the Curve (AUC) about 0.692 with the accuracy of 65.82%. By employing these deep learning strategies, the occurrence of abnormal events at public places like malls, roads, and parks can be detected quickly and immediate remedial measures can be implemented to enhance the public security.

Maximum Power Point Tracking Of Photovoltaic System Using Artificial

Neural Network

Kusum Lata Agarwal and Shubham Sharma

Jodhpur Institute of Engineering & Technology, Jodhpur, India

The use of renewable energy sources is increasing day-by-day in order to meet ever increasing energy demands at the same time to keep the carbon emissions low. The solar photovoltaic (PV) systems are offering best solution amongst the available renewable energy alternatives. The troubles with the solar PV system are its higher installation cost and lower energy efficiency at commercial scale. The nonlinear I-V characteristic of PV module is a function of temperature and solar insolation. The MPPT techniques are used continuously to track maximum power point at variable temperature and solar insolation. In this paper, dc-dc boost converter topology is used for transferring maximum power point to the connected load. The “Perturb and Observe” method is commonly used MPPT technique and in this paper back propagation algorithm-based artificial neural network (ANN) technique is presented for maximum power point tracking and the results of this technique are compared with the conventional “P and O” method. The complete model is simulated in MATLAB/Simulink software. The results obtained through the ANN technique are quite satisfying and it is found that this MPPT offers better results than the conventional ”P and O” method.

Design & Implementation of Traffic Sign Classifier Using Machine

Learning Model

Samarth Patel1, Pankaj Agarwal1, Vijander Singh2 and Linesh Raja2

1Amity University Rajasthan, India, 2Manipal University Jaipur, India

Recent decade witnesses the advancement in computing hardware and power. This advancement is the result of Machine Learning and Artificial Intelligence. One of the important advancements took place in development of autonomous cars. It was one of the long-standing points of research for scientists. The crucial components of any autonomous car are being able to recognize Traffic Signs. Traffic Signs serve as the non-verbal communication channel on the road. Thus, the task of recognizing traffic signs is a challenge.

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Moreover, due to the rise in number of drivers on the road the need for a system that can automatically recognize traffic signs has increased. As a result, this manuscript aims at solving the challenges of traffic sign recognition.

Multipurpose Advanced Assistance Smart Device for Patient Care with

Intuitive Intricate Control

Puppala Ravi Sankar, Anappindi Venkata Ratnam, Kollu Jayalakshmi, Akshada Muneshwar and Dr. Prakash Kodali

National Institute of Technology, Warangal, India

In India, more than 48.5% of population is suffering with regular disability as per the last Census data. Strokes, Dementia, Autism, Spinal injuries etc. are few common reasons of having disorder. Requirement of assistance for care is a must to disorder patients. Our design model varies with conventional ideas as it doesn’t have bulk hardware setup which is not comfortable to patients. The proposed design deals with sensing movements of a patient’s body with an attached sensor, designed using PVDF sheets and silver ink. This sensor is flexible, small in size and hence could be attached at any moving part of body like finger, toe, lower jaw, etc. It detects signals and assists the patient with their needs to control the equipment around them, communicate their thoughts with intuitive intricate control to reach the guardian. This model communicates between two Node MCUs wirelessly thereby reducing the hardware. The assistance is displayed on the master screen, where the concerned person could see and help the patient.

Smart Saline Monitoring System for Automatic Control Flow Detection

and Alertness using IoT Application

D Ramesh Reddy1, Srishti Prakash2, Andukuri Dinakar2, Sravan Kumar Chinta2 and Dr. Prakash Kodali2

1VNR Vignana Jyothi Institute of Engineering & Technology

2National Institute of Technology, Warangal

Since the population of the world is increasing day by day, there is a need to ramp up health care facilities. The most important and primary mandate for patients that are in hospitals is that they should be observed and treated properly with the provision of necessary nutrition/medicine at the right time. Apart from various treatments that they receive from doctors, the saline therapy is the most basic treatment in which bottle of saline water is fed to them for treating their dehydration thus improvising their condition. However the patient needs to be continuously monitored by the nurse during the whole time which is not possible to a complete extent due to high number of beds and other duties on them. Unfortunately, the patient’s blood starts flowing back in the saline tube due to unavoidable condition and carelessness. These results can be fatal and life-threatening in many cases. So, to prevent them from vital effects and unconditional flows, smart saline bottle level monitoring and alert system has to be developed protecting their lives during feeding hours. Here the proposed system having a suitable method of measuring the saline flow rate and remotely monitoring it using IOT platform. The proposed system determine the levels of the saline and indicate at the partial and critical level with the help of buzzer, level led and send notification to the hospital staff/control room using blynk mobile app (IOT Platform) over Wifi. This automatic system implements a mechanism that will stop reverse flow when the bottle goes empty. The system is compact, cheap and can be implemented in rural as well as urban hospitals.

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Comparative Study on AutoML approach for Diabetic Retinopathy

Diagnosis

Harikrishnan Vk1, Harshal Deore1, Pavan Raju1 and Akshat Agrawal2

1Heu Technologies private Ltd., 2Amity University Haryana

Diabetic retinopathy is one of the common eye diseases caused as a result of diabetics. There are mainly four types of Retinopathy conditions – mild, moderate, severe, and proliferative. Once retinopathy reaches proliferative stage, the person will have vision loss. In this study, Random Wired and NASnet Auto ML models are trained to predict diabetic retinopathy from retina images. Using architecture search technique and random graph models, optimized architecture is achieved. A comparative analysis was done between NASnet model and ER, BA and WS graph theory models within Random Wired Architecture, to understand how each algorithm impacts the architecture. Model was trained on 3652 images. The trained model achieved sensitivity and specificity above 80% on E-Ophtha Database when trained for up to 80 epochs.

Development and Control of a 7-DOF Bionic Arm of with Data Gloves and

EMG Arm Band

Suriya D.B., Venkat K., Balaji Aparajit and Anjan Dash

SASTRA Deemed University, India

The major challenge while dealing with a bionic arm is their control by patient (amputees) while wearing it. The present bionic arm is of 7 degrees of freedom with each finger having a separate servo motor, one servo for wrist and one servo for elbow. Since, single servo cannot account for elbow part which has to lift the whole hand, a gear assemble is made to increase the torque capacity of the servo. The bionic arm is 3D modelled and fabricated using 3D printers. Initially, a Data Glove with blue tooth module is used to verify the motion capability of all the fingers. Secondly, an EMG Armband with EMG sensors is used to get the data from the elbow muscles and it is mapped to the movement of the fingers and wrist. A Python program is used to map EMG sensor data to signal from data gloves while doing activities. Based in this, EMG sensor data is converted to the rotation of the motors of the bionic arm and finally, the bionic arm is controlled with the EMG armband. The arm is tested for various tasks like lifting a bag, holding a bottle etc and it is found to be very successful.

A Deep Learning based Segregation of Housing Image Data for Real Estate

Application

Annu Kumari1, Vinod Maan2 and Dhiraj Sangwan3

1NIT Warrangal, India

2Mody University Lakshmangarh, Rajasthan, India

3CSIR-CEERI, Pilani, India

Pictures have become an important part of our life nowadays. Humans tend to analyze all the images they come across to find which category they belong to and an-notate them. This analysis will become difficult for large volumes of data and we introduce a machine learning methodology to work more efficiently to do such classification. The input dataset contains images belonging to different categories of parts of the house for the Real Estate work thus

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reducing hectic work of an agent and will increase his working efficiency. The approach to this type of classification algorithm contains a Convolution Layer Network with a fully connected layer. The algorithm for the same approach also comes with an image enhancement technique as the quality of images of the dataset might not be sufficient for the architecture. So, the preprocessing technique ‘Contrast-Limited Adaptive Histogram Equalization (CLAHE) has been implemented for the enhancement of images. The image classification results of the CNN based architecture are superior to other traditional methods used for classification.

Design of Decision Support System to Identify Crop Water Need

Vaibhav Bhatnagar1 and Ramesh Chandra2

1Department of Computer Applications, Manipal University Jaipur, India

2Amity Institute of Information Technology, Amity University Rajasthan, Jaipur, Inida

Crop Water Need (ET crop) is referred as the amount of water needed by a crop to grow. ET crop has high significance to identify the adequate amount of irrigation need. In this paper, a decision support system is proposed to identify Crop Water Need. The proposed decision support system is implemented through sensors and smart phone. Internet of Things (IoT) based sensors are used to acquire the real time environmental factors that affect the ET crop. These sensors will communicate with the smartphone application using Bluetooth Technology. This proposed decision support system has been compared with available evapotranspiration and existing manual method of evapotranspiration and it was found that proposed system is more correlated than existing manual method of evapotranspiration therefore proves out to be beneficial for farmers and agriculture professionals.

Development of Inter-ethnic Harmony Search Algorithm based on Inter-

ethnic Harmony

Hyun Woo Jung1, Young Hwan Choi2, Donghwi Jung3 and Joong Hoon Kim3

1Department of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, South Korea

2School of Civil, Research Institute for Mega Construction, Korea University, Seoul 02841, South Korea

3School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, South Korea

Harmony Search (HS) has been presented with good results in various fields. Various improved versions of HS have been presented to enhance the HS's convergence capability. In this study, Inter-ethnic Harmony Search (IeHS) which is a new improved HS is proposed. IeHS developed mimics the concept of the reconciliation of various ethnicities from Turkish history, and this algorithm considers the self-adaptive parameters and decision variables setting, balancing local and global search, overcoming the solution stagnation problem using the historical concept (i.e., Millet and Jannissary). To prove IeHS has enhanced performance, the mathematical benchmark functions from CEC 2014 are applied and compared representative improved version of HS using performance indices.

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Electric Load Forecasting Using Fuzzy Knowledge Base System with

Improved Accuracy

Bhavesh Chauhan and Praveen Shukla

Babu Banarasi Das Northern India Institute of Technology, Lucknow, India

Fuzzy Techniques are the mathematical approaches to deal with imprecision and uncertainty existing in the real world problems to be modeled. Fuzzy Rule Base Systems or Fuzzy Knowledge Base Systems are the decision makers for different prediction based models addressing real world applications. These systems are applicable in many areas like Engineering, Management, Economics, Cyber Security etc. In this paper, a new Fuzzy Knowledge Based System is proposed and implemented based on Mamdani Fuzzy System for Electric Load Forecasting. The model is implemented using Guaje Open Access software which is a java based framework. The rule base of the proposed model is implemented using Wang Mendel Method. The fuzzy partitions are regular triangular and hierarchical triangular. The model is trained using the data set having the load values under various parameters. The performance of the proposed model is analyzed under the parameters accuracy and interpretability. The accuracy is measured by Performance Index, Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error. On the other hand, interpretability is estimated using Nauck’s Index, Total Rule Length (TRL), Average Rule Length (ARL) and Number of Fired Rules.

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