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CSI Communications | June 2016 | 1
Cover Story Overview of Artifi cial Intelligence 7
Technical Trends MOP: An Architecture for Web Based Massive Online Polling 17
Article Software Maintenance: An Overview 26
Practitioner Workbench Pattern Recognition in Java using “ENCOG Machine Learning Framework” 37
Security Corner Cyber Security in Smart Cities 34
Volume No. 40 | Issue No. 3 | June 2016
Research Front Internet of Things: Architecture and Research Challenges 21
CSI Communications | June 2016 | 2 www.csi-india.org
K N O W Y O U R C S I
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Executive Committee (2016-17/18) »President Vice-President Hon. Secretary
Dr. Anirban Basu Mr. Sanjay Mohapatra Prof. A K. Nayak309, Ansal Forte, 16/2A, D/204, Kanan Tower, Indian Institute of Business
Rupena Agrahara, Bangalore Patia Square, Bhubaneswar Management, Budh Marg, Patna
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Division ChairpersonsDivision-I : Hardware Division-II : Software Division-III : Applications
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SAIT, Indore Email : [email protected] Email : [email protected]
CSI Communications | June 2016 | 3
ContentsVolume No. 40 • Issue No. 3 • June 2016
CSI Communications
Please note:
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Printed and Published by Mr. Sanjay Mohapatra on Behalf of Computer Soceity of India, Printed at G.P.Off set Pvt Ltd. Unit-81, Plot-14, Marol Co-Op. Industrial Estate, off Andheri Kurla
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Mumbai 400093. Tel. : 022-2926 1700 • Fax : 022-2830 2133 • Email : [email protected] Chief Editor: Dr. A. K. Nayak
Chief EditorDr. A. K. Nayak
EditorDr. Vipin Tyagi
Published byMr. Sanjay Mohapatra
For Computer Society of India
Design, Print and Dispatch byCyberMedia Services Limited
PLUSBrain Teaser 39
Application Form for Individual / Life Membership 40
CSI Reports 43
Student Branches News 47
Cover Story7 Overview of Artifi cial Intelligence
by B. Neelima11 A Method of Classifi cation for AI Systems
by Devesh Rajadhyax13 Knowledge Management, as a Branch of AI, with Emphasis on Social Media
by Hardik A. Gohel15 Applications of Population Based Algorithms for Document Clustering
by Jitendra Agrawal and Shikha Agrawal
Technical Trends17 MOP: An Architecture for Web Based Massive Online Polling
by C. R. Suthikshn Kumar
Research Front21 Internet of Things: Architecture and Research Challenges
by Ankit Desai, Jekishan K. Parmar and Sanjay Chaudhary
Articles26 Software Maintenance: An Overview
by Sharon Christa, and Suma V.30 Fundamentals of Decision Support System and Exploring Research Application in Education
by Ankita Kanojiya and Viral Nagori
Security Corner34 Cyber Security in Smart Cities
by Ezz El-Din Hemdan, Madhvaraj M. Shetty and Manjaiah D. H.
Practitioner Workbench37 Pattern Recognition in Java using “ENCOG Machine Learning Framework”
by Videndra Singh Bhadouria and Rajesh K. Shukla
CSI Communications | June 2016 | 4 www.csi-india.orgCSI Communications | June 2016 | 4 www.csi-india.org
E D I T O R I A L
Dr. Vipin Tyagi, Jaypee University of Engineering and Technology, Guna - MP, [email protected]
Dear Fellow CSI Members,
Artifi cial Intelligence (AI) is the branch of technology that aims
on making intelligent machines so that machines can do tasks
that require intelligence when done by humans. Humans are
capable of solving complex problem, based on abstract thought,
reasoning and pattern recognition. Artifi cial Intelligence can help
us understand this thinking process by recreating it. The goal
of Artifi cial Intelligence is to design a formal model of human
mind that can represent “thinking” process. Such intelligent
machine should be able to mimic the way we think, feel, move
and make decisions. AI research is a combination of philosophy,
information technology, psychology, linguistics, neuroscience,
cognitive science, economics, control theory, probability theory,
optimization and logic fi elds. Till now, the behaviour of human
intelligence has not been captured fully and applied to produce
an intelligent artifi cial creature. Even then Artifi cial Intelligence is
being used in a number of areas like pattern recognition, reasoning,
game playing, natural language processing, medical diagnosis.
Keeping in mind the importance of Artifi cial Intelligence in today’s
context, the publication committee of Computer Society of India,
selected the theme of CSI Communications (The Knowledge Digest
for IT Community) June 2016 issue as “Artifi cial Intelligence”.
In this issue in Cover Story category, the fi rst article “Overview of
Artifi cial Intelligence” by B. Neelima provides an overview of Artifi cial
Intelligence, research fi elds and research techniques evolved as
branches of artifi cial intelligence along with list of AI applications
and tools. Next article “A method of classifi cation for AI systems:
An application oriented classifi cation based on capability level of
Artifi cial Intelligence systems” by D. Rajadhyax proposes a new
method of classifi cation called the SHA classifi cation that can be
used to label any given system as Class 1, Class 2, Class 3 or Class 4
system. In next Cover Story “Knowledge Management, as a branch
of AI, with emphasis on Social Media”, H. A. Gohel provides the
application and challenges of using Artifi cial Intelligence in Social
Media. Next article in this category “Applications of Population
Based Algorithms for Document Clustering “ by S. Agrawal and
J. Agrawal provides a review of population based algorithms.
Technical Trends category contains “MOP: An Architecture for
Web Based Massive Online Polling” by C. R. S. Kumar. This article
presents a Massive Online Polling (MOP) architecture for web
based polling along with discussions on cyber security issues.
In Research Front category, A. Desai, J. K. Parmar and
S. Chaudhary focus on IoT service support and economic impact,
explain IoT applications in “Internet of Things: Architecture and
Research Challenges”. Authors also suggest important research
and development areas along with IoT Technologies.
In Article category, the fi rst article “Software Maintenance: An
Overview” by S. Christa and Suma. V discusses various aspects of
software maintenance. In next article “Fundamentals of Decision
Support System and Exploring Research Application in Education”,
A. Kanojiya, and V. Nagori, propose a decision support system
prototype for selecting pedagogical tools to enhance the teaching
learning experience and measure its eff ectiveness.
Security Corner contains an article “Cyber Security in Smart
Cities” by E. E. Hemdan, M. M. Shetty and Manjaiah D. H.. This
article describes cyber security issues in smart cities to help
researchers and developers to design and develop new strategies
and methods to secure services and infrastructures of smart cities
in eff ective and effi cient manner.
This issue also contains Practitioner’s workbench, Crossword,
CSI activity reports from divisions, chapters, student branches
and Calendar of events.
I am thankful to entire ExecCom, in particular to Prof. A. K. Nayak
and Prof. M. N. Hoda for their continuous support in bringing this
issue successfully.
On behalf of publication committee, I wish to express my sincere
gratitude to all authors and reviewers for their contributions and
support to this issue.
I hope this issue will be successful in providing various aspects
of Artifi cial Intelligence to IT community. The next issue of CSI
Communications will be on the theme “Robotics”. We invite the
contributions from CSI members who are expert in the area of
Robotics.
Finally, we look forward to receive the feedback, contribution,
criticism, suggestions from our esteemed members and readers
With best wishes,
Dr. Vipin Tyagi
Editor
CSI Communications | June 2016 | 5CSI Communications | June 2016 | 5
P R E S I D E N T ’ S M E S S A G E
Dear CSI members,
I have all along been giving thrust on improving the quality of
our events and of our publications and on strengthening our
Digital Library. To meet this goal, we have signed an MOU with
Springer, the terms of which is available to all the organizers.
Organizers of conferences are strongly advised to plan an
event in advance and discuss the requirements specified by
Springer so that these are met. The process of paper reviews
and for checking plagiarism are to be strengthened.
We have been working on preparing the list of Distinguished
Consultants from the applications received from our members
and from the nominations of our Fellows. The list will be made
available through CSI portal soon.
Growth of our Membership is paramount to our success. The
Golden Jubilee discount is no longer available but in view
of the large number of requests received, the Membership
Committee is likely to review this. Thanks to the eff orts of
our Web Developer Anthem Global and persistence of our
Vice President Mr. Sanjay Mohapatra, the online membership
through CSI portal has started working. Any issue in functioning
may be reported to us. This facility will help our Members to
bring in new members to CSI family without undue delay.
Skill Development is one of the focus areas of the Government
of India led by Hon’ble Prime Minister Sri Narendra Modi.
I feel this is an opportunity which CSI should make use of
and work with the Government in this national mission. We
are conducting training programs primarily in Chennai in
association with NIELIT and we plan to expand the trainings
to other cities. Another initiative of the Government is
developing smart cities. CSI has been conducting seminars
on this in association with Infocomm International in different
cities.
We have also participated in the National Survey on Resources
Devoted to Scientific and Technological (S&T) Activities
2015-16. This may help us in getting associated with skill
development activities.
Our efforts on offering trainings on PMP in association with
Project Management Institute (PMI) has not made much
headway due to the delays in processing of our application
by the Singapore office of PMI. We are following it up. CSI
is planning to help the young IT community in building
up a career in Audio-Visual Engineering. For this we are
discussing with Infocomm International on the requirements
for completion of various certification programs like CTS and
how CSI can facilitate this.
I am glad that our premises in Mumbai has been thoroughly
renovated and a training room added in the space lying idle.
We are regularly appealing to our Members for advertisements
in CSI Communications. I think CSIC can be a good medium
for publicizing ones offerings and also for giving job vacancies.
ExecCom will be meeting soon in June/ July to discuss on
filling up the different vacant positions in CSI HQ and in
CSI- Education Directorate in Chennai and how various
administrative reforms can be speeded up.
With best wishes,
Dr. Anirban BasuPresident, CSI
Dr. Anirban Basu, Bangalore, [email protected]
4 June, 2016
www.csi-india.orgCSI Communications | June 2016 | 6
V I C E P R E S I D E N T ’ S D E S K
Dear Fellow CSIians,
Greetings !
Our stress is on organising well planned quality events
at State, Regional and National levels with the support of
each one of you. Team Coimbatore has started working
on making the 51st Annual convention of CSI to be held at
Coimbatore during 8-10 Dec. 2016, a grand mega event
of CSI. We extend our full support to Team Coimbatore.
We will continue our eff orts for publishing quality
publications for the benefi t of our members. An AGREEMENT/ MOU for fi ve years (till 31st December 2020), with
Zero fi nancial responsibility of CSI has been signed
between CSI, represented through its President Dr. Anirban
Basu and Springer publishers, represented through its
Executive Editor/Asst Manager William Achauer on
16th May, 2016. The MoU supports a co-publishing
partnership between Springer and the CSI for the publication of conference proceedings organised by the members and Chapters of CSI. It will help researchers in
publication of their research work through CSI conferences.
Our focus is on providing opportunities to our student
members. My request to all educational institutions who
are shaping the future of India, to involve more and more
students in CSI activities and organize more number of
events for the students.
CSI will continue to meet the expectations of our
stakeholders by improving sustainability and increasing
our corporate value.
Challenges to the systems that support us include an
increase in the number of activities at Chapter level,
Regional Level & National level. As a society, meanwhile,
we are also dealing with the negative impacts of declining
membership, diff erent research related activities etc.
and an increase in insincere at diff erent level. Hope this
EXECCOM will come up on these complications and
create a sustainable and thriving CSI.
New Initiatives for Web Portal : The CSI is in a position
to make tremendous contributions to have online mem-
bership facility, updating database online, incorporation
of student membership online, uploading all backlog
of CSIC, CSI Journal in portal, upgrading Digital Library
etc. with Zero Financial Investment. This ExecCom
headed by Dr. Anirban Basu is devoting valuable time for
this new transparency system of CSI portal. Dr. Durgesh
Mishra, Chairman, DIV IV is working on Digital Library
& Sri Raju LK, RVP 5 is working on student membership
management system.
CSI Communications is an icon of CSI and Prof. Vipin Tyagi,
RVP 3 is devoting too much time for timely publication of
CSIC in time.
The strength of any society is its members. My sincere
appeal to each one of you is to help the society in its
expansion by increasing its membership base. I would like
to request all RVPs, Divisional chairpersons to connect to
members, Chapters, Student branches of their regions
and provide all possible support in organising events.
ExecCom has decided to strengthen the linkages of CSI
with sister societies like ACM, IETE, IEEE, societies of other
countries. We are trying our best in this direction to provide
benefi t to our members to add value to the CSI membership.
“Dhumenavriyate vahnir yathadarso malena ca
Yatholbenavrto garbhas tatha tenedam avrtam”
(Bhagavad Gita 03.38)
(As fire is covered by smoke, as a mirror is covered by dust,
or as the embryo is covered by the womb, the living entity is
similarly covered by diff erent degrees of this lust.)
For feedback & suggestions please write to -
With kind regards
Sanjay MohapatraVice President, CSI
Mr. Sanjay Mohapatra, Bhubaneswar, [email protected]
CSI Communications | June 2016 | 7
C O V E R S T O R Y C O V E R S T O R Y
Introduction
A branch of Computer Science,
namely “Artifi cial Intelligence”
aims on building machines and
software with intelligence similar to
humans so that they can perform similar
thinking, reasoning, decision-making,
problem solving and natural language
processing like humans. It is basically the
simulation of human thinking performed
by machines or software. Primary goals
of AI include reasoning, learning, natural
language processing, decision making,
perception and knowledge representation.
In 1950, Alan Turing, a British
Mathematician came up with an idea of
machines that can think. He designed a
Turing Test which is used as a benchmark
even today to test the machine’s thinking
ability in his paper “Computing Machinery
and Intelligence”[1].
John McCarthy and Marvin Minsky
were one of the fi rst persons to come up
with Artifi cial Intelligence Lab. McCarthy
was the person who created the term
“Artifi cial Intelligence” and organized the
famous Dartmouth Conference in summer
of 1956 [1]. He also developed LISP which is
a programming language most commonly
used in Artifi cial Intelligence (AI) even
today. AI being a broad topic is categorized
into three competence level as follows:
• Narrow Intelligence
• General Intelligence
• Super Intelligence
Narrow Intelligence
Narrow Intelligence is an AI that
concentrates on one particular area or
fi eld. It is also known as weak AI and is
contrast to “strong AI” (strong represents
the ability of the intelligence to be applied
on any given problem or simply defi ned for
a border range of problems). One example
of Narrow Intelligence is like the ones
that beat Checkers Champions as it is not
capable of doing anything else other than
playing Checkers. Some Classic examples
of Narrow Intelligence can be given
as Google’s Self Driving Car, Personal
Assistants like Google Now, Apple Siri [2],
Spam Classifi ers and Google Translators,
as they operates in a limited defi ned range.
General Intelligence
General Intelligence also referred to as
“strong AI”, is capable of performing
tasks as smart as humans like deducing
a problem into smaller pieces and solving
them effi ciently, with approximate
reasoning, fuzzy logic, decision-making
processes and many more.
Super Intelligence
The ability of AI to perform smarter than
the brightest brains is referred to as Super
Intelligence. This AI includes problem
solving in almost all the fi elds. The Super
Intelligence competence level of AI is the
future where we have to reach from the
current level of fi rst, Narrow Intelligence.
Research Fields and Techniques in Artifi cial IntelligenceIn this era of technology, Artifi cial
Intelligence is one of the leading areas
that are used in a wide range of research
fi elds and techniques as shown in Fig. 1
and discussed in this section.
Research Fields of AI
This section briefs on various research
fi elds of Artifi cial Intelligence.
Intelligent Machine (Deduction, Reasoning and Problem Solving): Earlier
algorithms that were developed mimicked
step by step reasoning like humans and by
late 90s systems were designed to handle
probabilistic uncertainty and approximate
reasoning. The capability of Humans to
break down a complex problem and then
solve it step by step can be modelled
today with AI that is called as an Intelligent
Machine.
Knowledge Representation: For any
problem to be solved, knowledge is the
main foundation that is to be provided
appropriately to the AI model. Knowledge
Representation deals with representation
of knowledge about the worlds like
objects, their properties, and relation
with other objects, events, causes and
its eff ects and so on for various problem
solving strategies. A computer should be
able to understand adequate concepts
and learn on its own from various sources
and add to its own ontology for building
commonsense knowledge base to add the
essence of commonsense knowledge into
machines.
Machine Learning: Machine
Learning is the fi eld of AI that
deals with building computers
with the ability to learn without
any explicit programming and
improve itself with feedbacks
that it receives. Machine learning
process involves supervised,
unsupervised and reinforcement
learning models. Supervised
learning is the process of feeding
the machine with inputs and their
exact outputs with which the
machine is able to learn and next
time it encounters an input with
Overview of Artifi cial IntelligenceB. Neelima
Professor, Dept. of Computer Science and Engineering N. M. A. M. Institute of Technology, Nitte, Karnataka, India.
Fig. 1: Arti fi cial Intelligence: Research fi elds and techniques [3]
Abstract: This article presents an overview of artifi cial intelligence in all respects such as various categories of intelligence, research
fi elds and research techniques evolved as branches of artifi cial intelligence along with list of AI applications and tools. Further, it gives
reference to the list of organizations working in AI so that the readers can have a quick reference of such organizations to collaborate for
learning and researching in various aspects of AI. The author believes that this article becomes as single point of resource for a person
who is naïve to the fi eld of artifi cial intelligence.
CSI Communications | June 2016 | 8 www.csi-india.org
C O V E R S T O R Y
similar characteristics it could deduce
about what it is and other key features
of it like on seeing an animal it should be
able to identify whether it’s a cat or a dog
or any other animal. Few of the concepts
used in supervised learning are outlined
as follows:
• Decision Tree Learning: It is the
mapping of observations about
something to conclusions like
a tree structure to discover
the survivors based on some
constraints from a lot of people.
• Association Rule Learning: This
deals with identifi cation of
relations between entities in a
huge database like if someone
buys cheese and breads they
might next buy vegetables for
making Pizza.
• Inductive Logic Programming: It is
used in bioinformatics and Natural
language processing. It uses logic
programming to represent all
positive and negative examples,
knowledge and hypothesis.
• Support Vector Machines: These
are used for classifi cation and
regression. It is a binary linear
classifi er.
Unsupervised learning is the mode
of learning in which machines are given
the input data without any labels and the
machine is allowed to explore the structure
and hidden patterns in the given input on
its own like classifi cation of emails into
spam or not spam. Well known methods
of unsupervised learning are listed here as
follows:
• Clustering: It is the grouping
of objects with similar
characteristics in a set of objects
into one group. An example of
clustering is like clusters of benign
and malignant cancer cells in a set
of cancer cells.
• Similarity and Metric Learning: It is
used for the identifi cation of how
closely two entities are related.
• Sparse Dictionary Learning: This
technique is used for data
compression and decomposition.
• Genetic Algorithms: It is used to
solve problems using natural
selection processes by imitating
evolution.
Reinforcement learning is a machine
learning technique involved in continuous
interaction with the environment and
adapt the changes if any required to arrive
at the specifi c goal like learning to play
Checkers by playing against a contender.
Various techniques used in reinforcement
learning are outlined here as follows:
• Bayesian Networks: It is a directed
acyclic graph that represents
random variables and their
dependencies.
• Neural Networks: It is an
information processing paradigm
that is inspired by our biological
nervous system. It uses adaptive
learning and is used for pattern
recognition, data classifi cation
and etc.
• Deep Learning: It is similar to neural
networks but with more layers
to process and view the data at
higher level of abstraction. A good
example can be GoogleNet that
delivers effi ciency up to 92% and
uses nearly 25 hidden layers.
• Manifold Learning: It is similar to
Principal Component Analysis and
used for dimensionality reduction
but it is a non-linear operation.
Computer Vision (Perception): Perception deals with deduction of results
on receiving input from external sources
like camera, microphone etc. Problems like
Speech Recognition, Facial Recognition
come under this fi eld.
Planning: Planning is the base of
achieving any goal. For an AI system to
achieve the goals it sets, it should take into
account other actors in the environment
and the consequences of its plans and
predictions on them and the environment
and should change its plan accordingly
whenever necessary.
Robotics for Motion and Manipulation: Robotics deals with
designing, construction and operation
of computer systems and robots for
information processing. The sole purpose
of Robotics was to play instead of humans
in fi elds like manufacturing, space
exploration, assembly and other such
fi elds without diligence and to deliver
more precise results more.
The research fi elds include a few more,
apart from the listed in Fig. 1, are as follows:
Natural Language Processing (NLP): It is the ability of machines to read,
understand and generate the human
language. It is used mostly in text mining,
question answering. The goal of NLP is
to understand and generate the natural
languages that human speak so as to
build a effi cient and more reliable way of
interactions of humans with computers
just like they interact with other humans.
Some applications like Google Now, Apple
Siri already have implemented NLP.
Social Intelligence: Social Intelligence
deals with the negotiation with complex
relationships such as quarrels, romance,
politics etc.
Fig. 2: Fields of Arti fi cial Intelligence[4]
CSI Communications | June 2016 | 9
Further to the above, the research
fi elds of Artifi cial Intelligence along with
all its subfi elds are summarized as shown
in Fig. 2.
Research Techniques
This section briefs on various research
techniques of Artifi cial Intelligence.
Artificial Neural Networks:Inspired by biological neural networks,
artificial neural networks is a family of
models that are used to estimate an
unknown value from a large set of known
values [5].
Evolutionary Computing: Evolutionary computing system is derived
from Darwinian principles and adopts the
trial and error methods to solve problems
and is considered to a global optimization
method. It is mostly applied to black box
problems.
Expert Systems: A computer
application or a module that is specially
designed to solve complex problems
that belongs to a particular domain, and
requires special intelligence in solving it.
It works similar to that of an intelligent
expert human brain. An expert system
is reliable, understandable, and is highly
responsive.
Fuzzy logic: A method of reasoning
similar to that of a human mind is called
fuzzy logic. It makes use of decision
making skills similar to that of a human
mind with all the intermediate stages of
decision making. Fuzzy logic is mainly
used in commercial systems having
an advantage that the mathematical
concepts within this are very simple.
Genetic Algorithm: An algorithm
used to solve both constrained and
unconstrained problems based on a
neutral selection process that involves
biological evolution and is more
robust [6].
Applications of AIThe day is not far when we will
eventually be able to come up with super
intelligent systems that can perform
almost all the tasks better, faster and
reliable than the best of human minds.
AI system is designed and programmed
with one motive that is to improve
its own intelligence and every time it
improves itself becoming easier to adapt
quickly and take bigger steps allowing
it to be smarter than humans. AI has a
numerous applications in a number of
fields [7]; a few applications of AI are as
mentioned below:
• Speech and image processing
• Facial recognition
• Chat bots and machine
translations
• Gaming
• Strategic planning
• Decision support systems
• Automotive systems
• Medical experiments
Tools of AIThere are a large number of tools available
to work on the Artifi cial Intelligence. A few
of the tools are mentioned in Table 1 [5].
Personal Use
GoogleNow Intelligent personal assistant powered by Google
Apple Siri [2] Personal assistant by Apple available in its products
Microsoft Cortana [8] Microsoft powered personal assistant
IBM Watson AI system that uses machine learning and natural language processing and processes information
more like human
Echo Connects to cloud using technology in speakers and mic powered by Amazon Web Services
Gluru Helps in organizing personal documents, emails and other fi les and provides new insights into them
x.ai Helps you in scheduling your meetings and other appointments
CrystalKnows Helps in communicating better with others
RecordedFuture Uses NLP massively at real time
Tamr Provides exclusive advances to Big Data with the help of ML
For Developers
Soar Protégé: Framework and ontology editor to build intelligent systems
h2o.ai Helps in building fast and scalable ML applications
Seldon ML platform to embed intelligence into organizations
OpenCV It’s a library of programming that aims at Computer vision
OpenCog An open-source project to create framework for AGI
For Healthcare, Business, Robotics and Space
Enlitic Healthcare using Deep Learning
CSI Communications | June 2016 | 10 www.csi-india.org
Metamind.io Used for image recognition with use cases and medicines
Deep Genomics ML and AI tools for therapies, precision medicine and diagnosis
Atomwise Prediction and discovery of drugs and medicine using AI
Flatiron.com Delivers insights on treatments using ML and AI
SkyCatch Used for building aerial system that is completely autonomous
Mttr.net Intelligent software used to build fl ying vehicles
SpaceKnow Tracking global economic trends using AI from space
DigitalGenius Interactions of computers with customers to serve business better and scale
Conversica Using AI to help fi nd next customer
Table I: List of AI Tools
Famous Research Centers of AI Artifi cial intelligence centers are
laboratories where AI studies and research
are into focus. There are a number of
research centers worldwide. The list is
available at : www.aiinternational.org/
labs.html
ConclusionsArtifi cial intelligence is growing into many
research fi elds as mentioned. With the
advent of high performance computing
being available at the desktop level,
learning and researching various fi elds
of artifi cial intelligence in the era of big
data is becoming vital. Further artifi cial
intelligence based prognosis is most
promising research that is attracting many
researchers into this fi led of research. This
article has tried to bring various aspects
of artifi cial int elligence into a single place
for the benefi t of the prospective artifi cial
intelligence learners and wish this article
acts a single point resource for the readers.
References[1] https://en.wikipedia.org/
[2] h t t p : //w w w. p o c ke t- l i n t . c o m /
news/112346-what-is-siri-apple-s-
personal-voice-assistant-explained
[3] https://qph.is.quoracdn.net/main-
qimg-f8aca9e35bec6a01f1525bd657
8931c1?convert_to_webp=true
[4] h t t p : // i m a g e s . s l i d e p l a y e r .
com/24/7002258/slides/slide_5.
jpg
[5] http://www.tutorialspoint.com/
artificial_intelligence/artificial_
intelligence_neural_networks.htm
[6] https://www.doc. ic.ac.uk/~nd/
surprise_96/journal/vol1/hmw/
article1.html
[7] http://www.worldscientifi c.com/doi/
abs/10.1142/S0218001403002770
[8] https://en.wik ipedia .org /wiki/
Cortana_(software) n
Prof. (Dr.). B. Neelima [CSI-1084166] is working in the Dept. of CSE at NMAM Ins tute of Technology, Ni e, Karnataka. Prof. Neelima has completed her Ph. D. from Na onal Ins tute of Technology Karnataka (NITK), Surathkal in the area of high performance compu ng. She has completed a R&D project from DST, GoI and has around 50 publica ons in various Interna onal and na onal journals and conferences. She can be reached at neelimareddy@ni e.edu.in.
C O V E R S T O R Y
CSI Adhyayana tri-mmonthly puublication for students
Articles are invited for April-June 2016 issue of CSI Adhyayan from student members authored as original text. Plagiarism is strictly
prohibited. Besides, the other contents of the magazine shall be Cross word, Brain Teaser, Programming Tips, News Items related to
IT etc.
Please note that CSI Adhyayan is a magazine for student members at large and not a research journal for publishing full-fl edged
research papers. Therefore, we expect articles should be written for the Bachelor and Master level students of Computer Science and
IT and other related areas. Include a brief biography of Four to Five lines, indicating CSI Membership no., and for each author a high
resolution photograph.
Please send your article to [email protected].
For any kind of information, contact may be made to Dr. Vipin Tyagi via email id [email protected].
On behalf of CSI Publication Committee
Prof. A.K. Nayak
Chief Editor
CSI Communications | June 2016 | 11
C O V E R S T O R Y
Summary
There are many ways in which
Artifi cial Intelligence systems
are classifi ed. The usual way to
classify AI systems is based on the area
of application, such as Robotics, NLP,
Expert Systems and so on. They can
also be classifi ed according to technique
used, for example machine learning,
genetic programming or plain rule based
system. The well known capability based
classifi cation divides the systems into
three types- Artifi cial Narrow Intelligence
(ANI), Artifi cial General Intelligence
(AGI) and Artifi cial Super Intelligence
(ASI). But these classifi cations are not
rigorous in nature. This article proposes
a new method of classifi cation called the
SHA classifi cation. The SHA classifi cation
can be used to label any given system as
Class 1, Class 2, Class 3 or Class 4 system.
The usefulness of the system increases
as it progresses from Class 1 to Class 4.
Thus, the SHA classifi cation can be useful
in tracking the progress of a system, an
organisation or the whole industry.
BackgroundThe very fi rst attempt at classifi cation was
the Turing Test. This test could classify a
machine as intelligent or not intelligent.
Although extremely high level in nature,
Turing Test has dominated the AI world for
more than 60 years. Although it speaks a
lot about system that pass the test, it does
not say much about those systems that do
not pass.
The Turing Test refl ects a
philosophical problem that has troubled
the AI world since the beginning. What
exactly is intelligence? Turing Test focuses
on the ability to answer questions,
something that requires thinking. Typically,
the early AI researchers focused on what
we call as the ‘higher’ abilities of humans.
These included planning, reasoning and
problem solving. However, as the fi eld
matured, scientists realized that the so
called ‘lower’ abilities are much harder for
machines to achieve. Take the simple case
of walking. Even after 60 years of intense
eff orts, we still do not have machines that
can walk with the confi dence of a 3 year
old human child.
Further, when it comes to labelling AI
systems, there is a lot of confusion in the
terminology. AI has become an umbrella
term that is applied to a vast variety of
systems and it is usually unclear whether
the usage in a particular case is valid. Here
are a few typical cases of such confusion:
1. The system being labeled
falls under a diff erent major
and dominant discipline, but
uses techniques identifi ed as
belonging to AI. The best example
is Analytics. Should Analytics
and Big Data systems be labeled
as AI systems, because they use
Machine Learning?
2. The confusion of technique and
application is quite common.
Machine Learning is a technique
used by some AI applications.
Robotics is actually an application
area that employs multiple
techniques. But quite often we fi nd
a description of an organisation
that works in Machine Learning
and Robotics.
3. Then of course there is the classic
problem of drawing the boundary.
When do we term a system as
intelligent? This is the problem
that Turing Test tries to solve,
but as we know, intelligence is a
spectrum rather than a threshold.
Can we term an ERP or a CRM
system as intelligent? To answer
this question and clear the
confusion in cases such as 1) and
2) we need a more robust system
of classifi cation.
Introduction to SHA Considering the vague nature of
‘intelligence’ and the fact that AI has
become a big umbrella, I wish to propose
the more specifi c term ‘Systems with
Human Aspirations’ (SHA) to refer to
the systems under classifi cation. As is
evident, this nomenclature focuses more
on the result rather than the mechanism.
Thus SHA refers to a machine, program or
any future artifact that aspires to emulate
or surpass one or more of the human
capabilities. This overcomes the major
shortcomings of the word ‘intelligence’.
SHA avoids any reference to higher or
lower order of the capability and instead
focuses on all human abilities.
Prerequisites The SHA classifi cation is based on three
characteristics of any system: Capability
Type, Performance Level and Basis of
Usefulness.
Capability Type: This is the type
of capability that the systems aspires
to emulate. The universe of all human
capabilities is divided into two types:
1. CHS: Capabilities in which
humans are naturally strong, such
as language, planning etc.
2. CHW: Capabilities in which
humans are naturally weak, such
as large calculations, objectivity
etc.
Performance Level: This is the
measure of how good that system is when
compared to human beings as far as the
particular capability is concerned. The
performance level can be categorised as:
1. PSB: Performance of the SHA
is equal or better than average
human
2. PSW: Performance of the SHA is
worse than average human
We can easily see that the
performance level is subject to dispute.
Many systems may appear to be doing
better than human beings, but actually
their performance may not be as good as
humans. As we will see in next section,
A Method of Classifi cation for AI SystemsAn Application Oriented Classifi cation based on Capability Level of
Artifi cial Intelligence SystemsDevesh Rajadhyax
Founder and CEO, Cere Labs Pvt. Ltd. Mumbai
CSI Communications | June 2016 | 12 www.csi-india.org
the major reason for this mistake is Lack
of Constraints.
Basis of usefulness: It may appear to us
that the machines (for this section I use this
word instead of SHA) whose performance
is less than human will not be useful to us.
However, this is not true. Machines are
being used since prehistoric time when our
ancestors invented the fi rst stone tools. The
usefulness of machines is primarily because
of the following two reasons:
1. Lack of energy constraints: Human
beings have evolved to spend
(and partake) certain amount of
energy. This puts limitation on
the amount of work they can do.
Machines however do not have
this constraint. A car consuming
one liter of petrol burns about 8
million calories, whereas humans
burn an average 2500 calories in
a day!
2. Lack of psychological constraints:
Even if a human being has enough
energy, it may not perform a
repetitive task or prefer to do
something else. Human beings
have a mind that has evolved to
drive behaviour in a certain way.
But systems do not have such
minds, and ironically, this gives
them a certain single-mindedness.
A repercussion of this property
is that the machines can work
together much better than
human beings. This gives them an
advantage in scaling by division of
labour and by parallel processing,
i.e. many individuals doing the
same task at the same time. In
humans, psychological factors
dominate, putting a limitation on
scale. Occasionally, we see such
scaling achieved by resorting to
psychological treatment such as
in the army and in the pyramid
builders of ancient Egypt.
We can thus see that SHAs can be
useful because of an advantage due to
Lack of Constraints (LoC) in energy and
psychology. It also means that when I
mentioned Performance Level in the last
section, it should be considered separately
from the LoC advantage i.e. the SHA must
be better ‘as is’, not because of LoC.
The usefulness of SHA to humans
can be of two varieties:
1. The SHA might have exceeded
human capability and therefore
can be used to substitute
human beings for that particular
application. An example of such
a system is the calculator which
totally replaced the human
‘computers’ of 19th century. This
is Usefulness by Substitution
(US).
2. The SHA may not be good enough,
but it can still be useful because of
the LoC advantage. As we saw, the
most familiar example will be the
car. A car is not really better than
humans at going anywhere. But it
can consume a lot more energy.
This can be called Usefulness by
Extension (UE). Human beings
are still required in UE, but they
can achieve much more.
SHA Classifi cation:Now we can defi ne the SHA Classifi cation.
Class 1: This class represents most
Industry 1.0 machines. There are not as
good as humans even in their weak areas,
but are useful because of LoC advantage.
Class 2: Systems better than human
beings in capabilities in which humans are
naturally weak. Thus SHA Class 2 systems
are useful by substitution. The best
examples are calculators and database
systems.
Class 3: Systems compete in areas
where humans are strong, but are not
yet at par. They become useful, however,
because of LoC. Examples of Class 3
are speech recognition and automated
surveillance. In fact, most of today’s AI
systems will fi t into Class 3.
Class 4: CHS-PSB-US - needless
to say, these SHAs can replace human
beings in the areas of their strength. Even
if the SHA is at par and not better, it can
replace humans due to LoC. Example of
such system is hard to fi nd, but I think the
Chess programs or the recent AlphaGo
system from Google should qualify.
ConclusionBecause of its focus on utility, SHA
classifi cation can be employed in the AI
industry. In order to make the system
more rigorous, each of the characteristics
will have to be sharply defi ned. Especially
the Performance Level is an area of debate
and should be strongly formalised. n
Mr. Devesh Rajadhyax is the Founder and CEO of Cere Labs Pvt. Ltd. Mumbai, a company conduc ng research in Ar fi cial Intelligence. He is a post-graduate in engineering and has 20 years of experience of working in IT industry. Cere Labs is the third technology company that he has founded. He is a science buff and writes a science blog Yours Sciencely. He can be reached at [email protected].
C O V E R S T O R Y
Special Interest Group on Innovation and Entrepreneurship (SIG-IE) India is country with high percentage of youth population. There is need to provide gainful and productive engagement to them. It is diffi cult for any
government to produce so many employment opportunities. To generate employment, there is need for initiative from each and every individual.
Government of India has come up with many schemes for development of entrepreneurship e.g. Start Up India, Stand Up India, Make In India etc.
Currently most of start ups are related to IT products and services and are using IT for delivering the services with better quality.
CSI envisages to promote entrepreneurship and assist in creating and incubating entrepreneurs. For this a Special Interest Group has been formed.
CSI has many budding and established entrepreneurs as members. This may become suitable platform for interaction among these people. SIG will
monitor and scan entrepreneurial opportunities in country and will promote among members. Member may be benefi ted by this initiative and will
result in contributing our 2-cents in nation building using this SIG.
We request CSI members interested in entrepreneurship in practice or academic should register in this SIG. For registering, please visit:
http://www.csi-india.org/entrepreneurship.aspx.
CSI Communications | June 2016 | 13
C O V E R S T O R Y
Introduction
Knowledge management is the
developing era which has been
found to plan, capture, use and
re-use of the knowledge. It is meant for
organizations those are having much
information but in the form of hidden
knowledge. So, knowledge management
can be helpful for better utilization of
the knowledge available within the
organization.
In past, there were concepts of
business administration, information
systems, information and library sciences
where knowledge management was just a
single topic of study but were not expanded.
Actually, in 1990, knowledge management
has been emerged scientifi cally but in 1999,
fi rst time personal knowledge management
concept has been introduced which was for
the exercise of knowledge management at
individual level.
After big usage of computer science
for information analysis, knowledge
management approach has been
introduced as a research era and now a
days many top universities are off ering
knowledge management in Master of
Science degrees.
Knowledge Management – as a branch of AIThe specifi c goal of artifi cial intelligence is
to design as well as develop an information
system which can role like human being
and can respond to the surroundings.
Furthermore, we can also mention that
AI systems are hardware, software,
procedures, people, data and knowledge
needed to machine as well as computer
system which reveals qualities of human
intelligence.
In above tasks of AI, knowledge
management plays very pivotal role. As
knowledge management is a system which
organizes collection of hardware, software,
procedures, people, and data to create,
store, fi nd, fi lter, share and use of business’s
knowledge as well as practice.
There are many types of Knowledge
Management but the basic types are two
and others can be considered as subtypes.
1. Explicit Knowledge
It can be measured and also can
be documented as a report as well as
rules. Consider the situation in which the
person who is applying for a loan in bank is
qualifi ed or not, based on bank’s rules.
2. Tacit Knowledge
It is not possible rather harder to
document and measure. This type of
knowledge is not objective or formalized.
In this we can consider the example,
knowing the best way to discuss a diffi cult
employment clash.
Knowledge Management in Social Media – New ParadigmThe usage of social media is not only for
transforming interaction and personal
conversation but also transforms work
culture of people. With the help of social
media with emphasis on knowledge
management, any organization can
optimize knowledge work includes
knowledge sharing and knowledge access.
In recent era of business, complexity of
work and speed in execution increases
dramatically because environment of
work changes constantly. Knowledge
management in social media is very pivotal
application for present business context
apart from social learning, collaboration
and analysis.
In knowledge management a person
needs to know based on their importance
whereas in social media thoughts of people are
important which is helpful to judge individual.
There are various operations of
knowledge management performed on
the unstructured data of social media
which relates to any fi rm for their product
or services. After performing the various
knowledge management operations on
social media data relevant knowledge
can be generated and can be utilized
for organization for their future decision
making.
Fig. 1 explains in very perfect way that
how knowledge management as a resource
can be useful in social media data analytics.
By performing, organization can have proper
utilization of the knowledge which is the fi nal
Knowledge Management, as a Branch of AI, with Emphasis on Social Media
Hardik A. GohelAsst. Prof., AITS, Rajkot
Fig. 1: Knowledge Management in Social Media
CSI Communications | June 2016 | 14 www.csi-india.org
destination for deliberative process.
Knowledge Management vs. Social MediaThe above discussion deliberates that
knowledge management and social
media is having many similarities as both
include technology as well as access to
information. Furthermore, it requires
creating information as well as generation
of knowledge for the purpose of sharing
and also supports inter collaboration.
But, there is a generic diff erence between
knowledge management and social media.
In knowledge management, a person
needs to know based on their important
think whereas in social media people’s
thoughts are important which is helpful to
judge individual.
The above thought is giving some
more preference to the social media
at extends level. But in the terms of
philosophy, Knowledge is like water which
is having free fl ow and pervading down and
across to any fi rm.
“Business leaders recognize that engagement is the best way to glean value from the knowledge exchanged
in social media — and not by seeking to control social media with traditional KM
techniques” --Anthony J. Bradley and Mark P.
McDonald
Benefi ts of KM in Social Media• It gives very high level productivity
and performance to any
organization.
• Any user, by using knowledge
management in social media,
can solve complex problems very
faster and better way also.
• Social media knowledge
management can be useful to
collect hidden knowledge of any
organization.
• It also plays pivotal role in
organizational learning as a
process of creation, retention,
transformation.
• The best advantage of using
knowledge management approach
with social media is that it attracts
and retain extra ordinary young
employees.
Challenges• Knowledge management in
social media requires advance
technologies including
unstructured database
integration, interoperability and
navigational tools.
• The data collected in social media
for knowledge management is
very large, therefore providing
right level of security for
knowledge management is very
diffi cult.
• After generating knowledge from
social media, there is no specifi c
parameter researched yet for
knowledge measuring.
• It is not easy to say that knowledge
generated from social media will
be relevant to any organization.
There are some future research
directions related to knowledge
management in social media and they are
really needs to work. If any multinational
organization is having multilingual social
media then how would they generate
specifi c knowledge from multilingual
social media? This is one of the research
directions. Furthermore, if machine
learning can be combining with knowledge
management in social media, it would be
really big clincher. In this case, it would be
possible to solve the problem of multilingual
social media knowledge management.
ConclusionKnowledge management in social media is
not much innovative idea but it requires to
work after extend usage of social media.
It is not only for getting real knowledge
from social media but also helpful for
future decision making policy of any fi rm.
Social media is one of the signifi cant ways
to promote any organization globally but
the discussion related to any organization
is doing through online discussion can be
identifi ed by knowledge management. So
knowledge management in social media
is having some challenges but most
imperative way to generate knowledge
which would be helpful towards any
association.
References[1] Anthony J Bradley and Mark P
McDonald (2011) Social Media versus
Knowledge Management, Available
at: https://hbr.org/2011/10/
social-media-versus-knowledge/
(Accessed: 21st May 2016).
[2] Jonathan Reichental
(2011) Knowledge management in
the age of social media, Available
at: http://radar.oreilly.com/2011/03/
knowledge-management-social-
media.html (Accessed: 18th May
2016).
[3] Hardik Gohel (2014) ‘Looking Back at
the Evolution of the Internet’, CSIC, pp.
23-26 [Online]. Available at: http://
www.csi-india.org (Accessed: 19th
May 2016).
[4] Hardik Gohel (2015) ‘Role of Machine
Translation for Multilingual Social
Media’,CSIC, pp. 13-16 [Online].
Available at: http://www.csi-india.
org (Accessed: 19th May 2016).
[5] Lauren Trees (2013) Social
Media’s Role in Knowledge
Management, Available at:
h t t p s : //w w w. a p q c . o r g / b l o g /
social-media-s-role-knowledge-
management (Accessed: 20th May
2016).
[6] Anonymous, Basic concepts of
knowledge management and
artifi cial intelligence, Available
at: http://www.cga-pdnet.
o r g /n o n _ v e r i f i a b l e p r o d u c t s /
coursenotes/2010/ms1/module10.
pdf (Accessed: 20th May 2016). n
Dr. Hardik A Gohel [CSI - I1500336] is currently working as Asst. Prof. at AITS, Rajkot. He has wri en two books as a single author. He has chaired session in India as well as in abroad conferences. He has received Academic Excellence award from CSI in 2015. He can be reached at [email protected]
C O V E R S T O R Y
CSI Communications | June 2016 | 15
C O V E R S T O R Y
Introduction
Document clustering is clubbing the documents into many clusters such that documents having similar
properties belong to the same cluster whereas the dissimilar documents belong to diff erent clusters. It has been used in many areas of text mining and information retrieval for enhancing the precision of information retrieval systems by effi ciently obtaining the nearest neighbors of a document. With the advancement of technologies, large amounts of rich and dynamic information are available in World Wide Web. A user can quickly browse and locate the documents with web search engines. Search engines return many documents, some of them are related to the topic while some are irrelevant. Thus, document clustering plays a key role in structuring such monolithic amount of documents returned by search engines into valid clusters. Document clustering algorithm can be categorized as fl at, hierarchical, hard and soft. Flat clustering makes a fl at set of clusters without any straightforward structure that would relate clusters to each other. It divides the document space into discrete clusters. Hierarchical clustering makes a hierarchy of cluster. In hierarchical clustering document of lower level cluster is also a member of corresponding higher level cluster. In hard clustering each document is a member of exactly one cluster whereas in soft clustering algorithm the document has fractional membership in several clusters.
It is a basic step used in unsupervised document organization, information retrieval and automatic topic extraction. Clustering is the process of partitioning a set of objects into a fi xed number of bunches. The objective of clustering is to fi nd implicit anatomy in the data and to display this constitute as diff erent sets. The data objects within a set show a large degree of similarity while the data objects of diff erent sets should be dissimilar. Most document clustering algorithms can be categorized into Hierarchical and Partitioning clustering techniques.
Hierarchical techniques produce an arrangement of partition where every partition is nested into the next sequence of partition. The algorithm divides the database into smaller subgroups, until stopping condition is reached. One of the advantages of this algorithm is that
it does not need ‘k’ as an input parameter. Agglomerative and Divisive are the two basic approaches for hierarchical clustering. In Agglomerative clustering algorithm is a bottom up approach where each object is placed in a unique group and for every pair of groups, value of disparity in terms of distance is calculated. The distance must be minimal distance of all pairs of points from the two groups; the groups with the minimum distances are merged at every step. The termination criteria can be set by fi xing the minimal distance between the clusters. Divisive clustering algorithm is a top down approach in which all objects are placed in a single cluster. At every step divide a cluster until only singleton groups of individual points remain.
Partitioning algorithms construct division of a database of N objects into a group of k clusters. The construction implicates fi nding the optimal division according to an objective function. There are around “kN/k” ways of partitioning a set of N data points into k subsets. It is an iterative optimization paradigm. It begins with an initial partition and utilizes an iterative control strategy. It swaps the data points and test if this enhances the quality of clusters. When swapping does not return any improvement in clustering, it ends with a local optimal solution.
Two categories of above mentioned algorithm are K-Means algorithm and K-Medoid algorithm. K-Means algorithm is developed by MacQueen, it is simplest and well known unsupervised learning algorithm. It is an effi cient algorithm for clustering large datasets. This is a top down clustering algorithm which assigns each document to the cluster whose centroid is nearest. The aim of K-Means algorithm is to partition a set of objects into ‘k’ clusters, where ‘k’ is a user defi ned constant. For each cluster, there is a need to defi ne ‘k’ centroids. The centroid of a cluster is formed in such a way that it is nearest to all objects in that cluster. In K-Medoid algorithm each cluster is represented by one of the objects of the cluster located near the centre. Here, random selection of ‘k’ medoids is done that represents ‘k’ cluster and rest of the data objects are put into a cluster according to their nearest distance from any of the medoid. After allocating all data objects, new medoid is calculated to represent the cluster in better way. In each iteration, medoids change their
position step by step. This process is repeated until no change in medoid.
Although the hierarchical clustering technique is able to fi nd better quality clusters but it does not have any provision for the reallocation of earlier poorly classifi ed entities. Also, its time complexity is quadratic. In recent years; it has been found that the partition clustering technique has relatively low computational requirements thus well suited for clustering a large dataset. Although K-means is best partitioning algorithm for clustering large datasets but it traps in local minima so diff erent population based optimization algorithms such as Genetic algorithm (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO) have been proposed to fi nd the global optimal solution for clustering large datasets.
Xiaohui et al. used PSO for document clustering. In contrast to local search property of K-Means, PSO performs globalized search over the entire search space. Authors used PSO, K-Means and hybrid PSO on four document datasets which are derived from Text Retrieval Conference (TREC) and contains 414, 313, 204, 878 documents respectively. In hybrid PSO two modules are used the PSO and the K-Means module. For similarity metrics, “Euclidian Distance” and “Cosine Correlation” measure are used. Cluster quality is measured by average distance between document and cluster (ADDC) and smaller ADDC value indicates good clustering solution. Performance comparison shows that hybrid PSO algorithm performs better clustering than using either K-Means or PSO alone.
Cuiand Potok et al. used hybrid Particle Swarm Optimization (PSO) with K-Means for document clustering. PSO is an optimization algorithm and provide globalized search but require more number of iterations and computational time while K-Means is faster than PSO but it is sensitive to initial solution and can be trapped into local optima. So the author combined both, PSO is for initial stage to fi nd the initial seed and then K-Means is used for refi ning stage. Experimental results on datasets illustrate that hybrid PSO performs better than PSO and K-Means alone. Author also demonstrates various hybridization of PSO with K-Means which are: PSO followed by K-Means, K-Means followed by PSO & K-Means followed by PSO which is further
Applications of Population Based Algorithms for Document Clustering
Jitendra AgrawalAsst. Prof. DCSE, University Institute of Technology
Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal (MP)
Shikha AgrawalDepartment of CSE,
Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal (MP)
CSI Communications | June 2016 | 16 www.csi-india.org
C O V E R S T O R Y
followed by K-Means. From the experimental result reported it is concluded that PSO followed by K-Means outperforms all the other cases.
Singh et al. applied fl at clustering algorithms like K-Means, Heuristic K-Means and Fuzzy C-means for clustering of text documents. In their experiment authors used diff erent representation such as term frequency, Inverse document frequency and Boolean. Diff erent selection schemes “(with or without stop word removal & with or without stemming)” are also used. Stop words are common words like ‘the’, ‘am’, ‘is’, ‘are’, ‘who’ etc. which do not provide any information about the representation of the topic. Diff erent form of terms like ‘computer’, ‘computes’, ‘computational’, ‘computing’ are represented by its root word ‘computer’, this process is called stemming. Diff erent experiments are performed using K-Means, heuristic K-Means and Fuzzy C-means and results illustrate that Inverse document frequency performs better than both term frequency and Boolean representation while term frequency performs better than only Boolean. Performance of Fuzzy C-means is better than K-Means and Heuristic K-Means both. The results of Stemming alone produce better clustering than stop word removal and stemming &stop word removal together.
In 2012, Forsati et. al. presented Harmony Search (HS) for document clustering. Authors fi rst proposed pure HS based clustering for fi nding near optimal solution which is called HSCLUST. Then HS is integrated with K-Means which combines explorative power of K-Means with refi ning power of HS. In contrast localized searching property of existing K-Means HS performs globalized search and it is less dependent on the initial partition. Authors combine Harmony Search with K-Means in diff erent ways. The Sequential hybridization, in which optimum region is found by HSCLUST and then optimum centroid is found using K-Means. In Interleaved Hybridization, after every iteration of harmony search K-Means is used and Hybridization K-Means as one step of HSCLUST is used in which HSCLUST and K-Means are combined for every iteration. In this research HS is applied with K-Means and GA based clustering algorithm on diff erent document sets such as Politics dataset, TREC,
DMOZ collection, 20 NEWSGROUP, WebACE project(WAP). Quality of clusters is compared based on Entropy, F-measure, Purity, and Average Distance of Documents to Cluster Centroid (ADDC). Experimental results yields that the proposed algorithms generate best clusters.
Akter and Chung proposed an evolutionary approach for document clustering based on genetic algorithm. In this paper genetic algorithm is not applied on the whole dataset directly. Authors propose two phase genetic algorithm approach in which dataset is partitioned into some groups and genetic algorithm is applied into each separate partition and another phase of genetic algorithm is applied on the result. This avoids the problem of local minima. Another advantage of this approach is that it does not need to specify the total number of clusters in advance. Authors compare the performance of K-Means, Genetic algorithm and proposed algorithm using benchmark database REUTERS-21578 which include 1000 texts from topics such as acq, crude, trade, grain and money-fx. Performances are compared using F-measure metric and latent semantic indexing (LSI) is also applied on dataset. Results show that proposed algorithm performs better than K-Means and Genetic algorithm.
In order to improve the effi ciency of clustering, Changchun and Wang proposed a query specifi c density clustering in IR. Here relationships of documents that are relevant to specifi c query are taken into consideration. Proposed model has been evaluated using TREC collections based on density clusters. The result reported verifi es the superiority of the proposed methodology over other algorithms compared.
In 2013 Minjuan proposed a “Semantic Optimization Clustering Method” for XML documents. In this research work, for XML element clustering “Latent Semantic Indexing Model” is used to fi nd semantic relationship between terms and evolution function for K-Medioid clustering algorithm is performed to automatically produce the optimal cluster number. Evolution function for clustering is based on compaction and resolution. Compaction is the intra-cluster distance and Resolution is the inter-cluster distance. In this research, experiments were performed
on “IEEE CS data” and to compare the performance of cluster quality “information gain” criteria is used. The results indicate that clustering with optimization provides better clustering quality.
DiscussionIn this survey, various population based algorithms are discussed for document clustering. Hierarchical clustering algorithm provides better clustering but it has quadratic time complexity. K-Means partitioning algorithm has linear time complexity but it produces inferior cluster. Previous studies show that although among various algorithms K-Means algorithm is suitable for clustering large datasets but it produces a local optimal solution. To fi nd global optimal solution various optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Hybrid PSO, and Harmony Search were applied for document clustering. These optimization algorithms improved the quality of clustering but require their own algorithm specifi c control parameters with common controlling parameters like population size and number of
generations.
References[1] Forsati R, Mahdavi M, Shamsfard M and
Meybodi M R: Effi cient stochastic algorithms for document clustering. Information Sciences.220, 269-291 (2012).
[2] Xiaohui C, Thomas E P and Paul P: Document Clustering using Particle Swarm Optimization. In: Swarm Intelligence Symposium, pp. 185-191. Pasadena, CA, USA(2005).
[3] Akter R and Chung Y: An Evolutionary Approach for Document Clustering. In: 2013 International Conference on Electronic Engineering and Computer Science, pp. 370-375(2013).
[4] Singh V K, Tiwari N and Garg S: Document Clustering using K-Means, Heuristic K-Means and Fuzzy C-means. In: 2011 IEEE International Conference on Computational Intelligence and Communication Systems, pp. 297 -301. Gwalior (2011).
[5] Xiaohui C, Thomas E Potok, 2005. Document Clustering Analysis Based on Hybrid PSO+K-Means Algorithm. Journal of Computer Sciences, 27-33 (2005).
[6] Li C, Wang J Y : A Clustering Approach to Improving Pseudo-Relevance Feedback. In: Information Science and Engineering, pp. 35--38. IEEE, Shanghai (2012).
[7] Minjuan Z: An Eff ective Search Results Semantic Optimization Clustering Method for XML Fragments. In: Computer Science and Applications, pp. 479-482, Wuhan (2013).
n
Dr. Jitendra Agrawal [01177532] is currently working with Department of CSE at the Rajiv Gandhi Proudyogiki Vishwavidyalaya, MP, India. His research interests include Data Structure, Data Mining, So Compu ng and Computa onal Intelligence. He can be reached at [email protected].
Dr Shikha Agrawal [01186620] is an Assistant Professor in Department of Computer Science & Engineering at University Ins tute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal (MP) India. Her area of interest is Ar fi cial Intelligence, So Compu ng and PSO and Database. She has been awarded as “Young Scien st” by Madhya Pradesh Council of Science and Technology, Bhopal in 2012. Her other extraordinary achievements include “ICT Rising Star of the Year Award 2015” and “Young ICON Award 2015”.
CSI Communications | June 2016 | 17
Introduction
The world wide web (WWW) and
internet have become ubiquitous
and reached every nook and corner
of our country. We routinely access www
for news, emails, online banking, online
shopping, social networks, chatting, etc.
It is relevant to examine at this juncture
whether the upcoming elections for Lok
sabha can be web based online elections.
Online Polling as an alternative solution is
attractive as :- Quickness of conducting elections
as current duration of 30 days can be reduced to 3 days resulting 10x speedup in.
- Development and reconfiguration: General purpose computers, servers, network equipments, can be used with customized software.
- Performance and security requirement of polling can be met with current cyber security tools and equipments.
- Various types of cyber attacks can be met with effective counter-measures.
There are several benefits of Web based online elections. We list some of the benefits here:
- Online voting would ease the pressure on polling booths and security required for deploying the booths and voting machines/ballot papers.
- Removes the need for specialized equipments such as voting machines as general computers/PCs/Servers/Networks can be used.
- Voters who are far away from their home constituencies can easily cast their votes.
- The results can be instantaneously
made available.
- Customization required for the
different constituencies can be handled
only at the s/w level and hence same
systems can be reused.
Online elections have been
attempted with limited success[4][5].
Detailed discussion on rewards and
risks of online elections along with
case studies have been presented in
literature[16]. There are several issues to
be resolved for web based online voting.
Some of the issues are:
- Method of foolproof authenti-
cation of voters
- Ensuring that the web server and
related s/w and H/w are robust against
attacks.
- Removing chances of proxy voting.
- Fault tolerant system for ensuring
any faults in H/W/S/W does not affect
the election process.
- Voter training and building
confidence as voters may find it difficult
to trust the new systems.
- S/W development and on-time
delivery for the elections.
- Ensuring the performance of the
web servers.
- S/W compatibilities between
different s/w such as operating systems,
web browsers, web servers, scripting
languages etc.
MOOC (Massive Open Online
Course) has become highly successful
with examples such as NPTEL, coursera.
org, edx.org, etc. MOOC supports
thousands of students online for
learning and quizzes. On similar lines,
in this paper we propose an architecture
for MOP (Massive Online Polling).
This paper examines the technologies
involved in web based elections, policy
and technology issues, pros and cons,
attacks and countermeasures for web
based online polling.
Indian Parliamentary Elections - Current ScenarioIndia is the largest democracy in the world.
With more than a billion population, the
polling exercise will be extraordinarily
big. Conducting national level elections
will involve substantial fi nancial, human
and other resources. Security during the
elections is a major concern. With literacy
among the voters being sometimes poor,
the elections have to be conducted with
least sophistication.
The current electioneering in India
either involves ballot papers or the
electronic voting machine (EVMs). The
simple printed ballot papers are to be
used by the voter while casting their votes.
Each voter is authenticated with the voter
ID cards which are specifi cally issued for
facilitating the voting. Special ink markers
are used for ensuring the duplicate or
proxy votes are not carried out.
On the day of the elections, the
voters are expected to visit the polling
booths which are secured. The entry of
each voter is fi rst verifi ed with voter id
card and also the voter list. Then the voter
either uses the EVM or the ballot paper to
cast the vote. An ink mark is made on the
voter once he/she has performed voting.
Hence, the same person is prevented from
voting multiple times.
EVM: Electronic Voting Machines
(EVMs) have been successfully used in
Indian elections. The EVMs speedup the
MOP: An Architecture for Web Based Massive Online Polling
C.R. Suthikshn KumarDepartment of Computer Engineering, Defence Institute of
Advanced Technology (DIAT), Girinagar, Pune
Abstract : With world’s largest democracy tag, India excercises voting in large scale with millions of voters casting their votes. This
paper addresses automating the elections using web based Massive Online Polling(MOP). MOP architecture for web based polling for
Indian Lok Sabha election is presented along with discussions on cyber security issues. While the web based online polling presents
various challenges and issues, it provides cost-eff ective, effi cient way of conducting polls. The use of general purpose computers
and networks instead of custom built electronic voting machines(EVMs) brings several benefi ts such as ease of deployment,
reconfi gurability, portability and scalability. However, the computer networks are prone for various cyber threats and attacks. We list
such possible threats and present eff ective counter measures.
T E C H N I C A L T R E N D S
CSI Communications | June 2016 | 18 www.csi-india.org
voting phase and also the results phase.
They simplify the process of casting votes
and also counting the votes for declaring
the results. The current day EVM consists
of two main units[1]:
• Control Unit(CU)
• Balloting Unit(BU)
A long cable connects both CU and
BU. The Polling Offi cer operates the CU
and the voter operates the BU while
casting a vote. The EVMs programs are
frozen during the manufacturing process
and cannot be altered.
The EVMs are very simple to use.
The BU is enabled by operating the CU by
polling offi cer. The voter presses a button
against the candidate provided on the
BU. The vote is thus recorded. The results
can be obtained at the end of polling by
pressing the results button on the CU. The
EVMs have signifi cant limitations in terms
of number of votes which can be recorded
i.e., 3,840. Also, they limit the number
of candidates to 64. Extensive security
analysis of EVMs indicate that they are
vulnerable to serious attacks[14].
MOP Architecture for Online Polling for Lok SabhaMassive Online Polling(MOP):
Computerized web based elections are
practical for Lok Sabha and other elections
only when the proven protocols which can
fulfi ll the following requirements[3].
Dual Signature based voter
authentication:
The Dual Signature has earlier been used
for Credit card authentication in Secure
Electronic Transactions(SET) protocol[6].
The Dual signature concept can be
extended for voter authentication in the
Online polling approach to ensure the
objectives of ensuring confi dentiality and
secure voting. Voter’s Digital signature
is required on the vote but his/her
privacy needs to be guarded. Also, the
vote contains the digital signature of the
polling offi cer of the concerned booth
where the vote was cast. When the vote
is cast, the web application automatically
generates voter’s Digital signature and
adds it to the vote. When the polling
booth offi cer authenticates the vote, his/
her digital signature is added while also
encrypting the voter’s Digital Signature
to ensure confi dentiality. In essence, all
the votes will have in clear the digital
signature of the polling offi cers and the
vote information. The Digital signatures of
the voters is encrypted and kept private.
The IEEE VSSC/1622 is a voting
system standard for creating common data
standard for elections[15]. Thus, the voting
system data created in standard format
will be easier to process by commercial
and open source tools. The standard
data format also helps in interoperability
and compatibility with various tools and
polling systems.
In order to ensure secure online
elections, distributed data center
architecture with dedicated network is
proposed. The data centers (Tier 1) are
equipped with latest servers, storage,
switches and are protected by Firewalls
and Intrusion Detection/Prevention
systems. The Data centers are housing
webservers and other applications
necessary for conducting the online polls.
There is a dedicated computer network
for online polls and the data centers are
isolated from the internet.
A data center serves as a central
facility to house computer systems,
networking equipments, storage systems,
power supply, Telecommunication
equipments. Data center in an University
environment includes redundant or
backup power supplies, redundant
data communications connections,
environmental controls (e.g., air
conditioning, fi re suppression) and
security devices. The data center provides
all the infrastructure needed for IT
operations which are central to operations
of the online election.
The MOP online elections rely on
the central computing facilities and
networking equipments in the data center
for important functions such as:
• Web servers for hosting the online
poll website: Good website of
the online elections serves as an
important criterion successful
elections. The website not
only provides the information
about online elections, staff and
facilities, it also may have online
applications, online feedback
systems, videos, etc.
• Email Server which
supports email communication
internally and externally.
• FTP server which
Fig. 1: Electronic Voti ng Machine[2]
Fig. 2: Data Center for online polling
Sl No Requirements Remarks
1 Only authorized voters can vote Voter ID required
2 No one can vote more than once Marking of voter with ink
3 No one can determine for whom anyone
else voted
Privacy of voting
4 No one can duplicate anyone else’s vote Hard requirement
5 No one can change anyone’s vote
without being discovered
6 Every voter can make sure that his vote
has been taken into account in the fi nal
tabulation`
This is very diffi cult requirement
but in large democracies such as
in India, very diffi cult to meet.
7 Every voter should mandatorily cast the
vote
Optional
T E C H N I C A L T R E N D S
CSI Communications | June 2016 | 19
hosts the fi les, videos, etc.
• Application servers for election
applications such as Matlab,
Scilab etc
• Enterprise Resource Planning
• High Performance Computing:
The conduction of online elections
and counting votes may require
high performance computing
facilities.
• Backing up the data in storages
• Important requirement of the
data center is the Availability
of the data center. Based on the
Availability metrics the data
centers have been classifi ed into
diff erent tiers as follows:
The Online Election relies on
their information systems to run their
operations. If a system such as mail server
or webserver becomes unavailable, online
election operations may be impaired or
stopped completely. It is thus necessary
to provide a highly reliable infrastructure
for IT operations, in order to minimize
disruption. Information security is another
major issue in online elections. Data
center must therefore keep high standards
for assuring the integrity and functionality
of its hosted computer environment.
With the fast pace of the IT growth,
the data centers are fast aging. The
average age of a data center is seven to
nine-years-old. The data centers which
have equipments/software older than nine
years are sometimes termed as obsolete.
The new generation data center
require transformation initiatives such as :
• Standardization/consolidation: The integrated single data center
is better than several smaller data
centers. This helps to reduce the
number of hardware, software
platforms, tools and processes
within a data center. Thus, it is
easier to replace aging data center
equipment with newer ones
that provide increased capacity
and performance. Computing,
networking and management
platforms are standardized so
they are easier to manage
• Virtualize: IT virtualization
technologies can be used to
replace or consolidate multiple
data center equipment, such as
servers. Virtualization lowers
capital and operational expenses
and enhance energy effi ciency.
Virtualization technologies
are also used to create virtual
desktops, which can then be
hosted in data centres.
• Automating: Several routine data
center tasks such as provisioning,
confi guration, patching, release
management and compliance
can be automated. Automating
tasks make data centers run
more effi ciently and also reduce
reliance on manpower.
• Securing: The security of a
modern data center must focus
on physical security, network
security, and data and user
security.
• Energy Effi ciency and Renewable Energy Sources: The new
generation university data centers
can utilize solar panels to power
the systems. The use of renewable
energy sources as solar power,
wind power etc is advisable as
they are environmental friendly.
• Modularity and fl exibility: These are necessary in a data
center to grow and change over
time. Data center modules are
pre-engineered, standardized
building blocks that can be easily
confi gured and moved as needed.
Data centers contain a set of routers
and switches that transport data traffi c
between the internal computers and
external computers. The uptime of the
internet may be ensured to be high with
provision of redundant connections.
Several servers at the data center are used
for running the basic Internet and intranet
services needed by internal users in the
organization, e.g., e-mail servers, proxy
servers, and DNS servers.
Network security elements are also
usually deployed: fi rewalls, VPN gateways,
intrusion detection systems, etc. Also
common are monitoring systems for the
network and some of the applications.
Comprehensive studies of data centers
in leading international universities and their
deployment are being carried out. This is
inorder to fi nd the areas of improvement
for MOP data center to become the next
generation data center. Examples are :
• Stanford data center which is in
tier 2 and is highly energy effi cient.
• Harvard-MIT Data center
(HMDC[9]: The mission of HMDC
is “To develop and provide
world-class research computing
resources, data services,
and supporting information
technologies to further social
science research and education.”
• UC Berkeley Data center[10]: This
datacenter sets out academic
highest priorities: high Availability,
low cost, 10/100/1000
Networking, Secure rack, Remote
Access, On site Access, Sandbox
and Safe. The research highest
priorities are: Fiber and Optical
Infrastructure, Infi niband,
1000/10000 Ethernet, Flexibility
– rack and rerack regularly, High
speed copper Cat 5e / 6, 200
watts/sq foot (15kw rack), Needs
very large Staging
MOP: Network Architecture:
The Data centers and Polling booths
are to be networked similar to National
Knowledge Network(NKN)[11]. The
architecture consists of locating 4 Data
centers in 4 metropolitan cities. Then all
the state capitals are connected through
optical networks. Further, the district
headquarters and Taluk Headquarters are
connected through the state capital. The
Polling network is completely sandboxed
from Internet. This is to prevent any
attacks, entry of intruders, entry of
malicious software etc. The following
diagram shows the important elements of
online polling.
The Polling Booths:
The polling booths consist of dedicated
computers which are confi gured
specifi cally for the voting. These are
connected to the servers of the Polling
Data centers. The polling booths are
temporarily setup during the elections.
These may be also mobile booths with
modifi ed busses for the specifi c purpose
of polling. The network connectivity is
through the wired/wireless connectivity
to the central server. In hilly and diffi cult
Tier level Availability
1 99.67%
2 99.741%
3 99.982%
3 99.995%
CSI Communications | June 2016 | 20 www.csi-india.org
terrain, wireless networking may be preferred.
The voter is fi rst authenticated using voter’s ID and allowed into the polling booth for casting the vote. The voter casts vote by clicking on the computer screen displaying the Candidate list. All the steps being followed will be similar to the conventional EVM voting except the computers replacing the EVMs.
The following table gives the estimates for the storage size based on the publicly available information about voters and previous election statistics [13].
Cyber Security for Online VotingCyber Security serves as the backbone of the successful online web based elections. Extensive security related studies on online polling have been published by NIST[12]. The cyber security analysis will consider the online polling from four dimensions i.e., Confi dentiality, Integrity, Availability and Authentication & Identifi cation. While there are various threats and attacks possible, a list of important ones is as follows:
• DDoS attacks can make the polling servers crash or become unavailable for a period of time.
• Spyware and Keyloggers and other malicious software can be used for collecting information such as the current voting scenario, voter’s identity etc.
• Botnet attacks can be deployed to change the election outcome.
The preparations and precautions while conducting online elections can
counter such threats. Firewalls, Anti-virus software, Intrusion prevention/Detection systems, Secure Socket layer(SSL), Virtual Private Network(VPN), Login/Password, Hardened OS etc are some of the important components of cyber security. The Secure Software Engineering principles need to be adapted while developing the customized applications for online voting. Further,the networks and computers need to be isolated from Internet. The use of memory sticks or pen drives for data transfer should be avoided as this may result in entry of malicious software.
Summary and ConclusionsThe MOP online elections for the Lok sabha will be convenient and cost eff ective. The use of general purpose computers in place of custom EVMs solves various problems but introduces new challenges. In this paper, we have presented the details of the architecture for online polling for Lok sabha. We have discussed important issues and challenges facing the online elections. We have proposed dual signature based authentication for voters. The MOP based online polling for lok sabha will not only reduce the time for poll conduction, but also instantaneously provide results by automated counting.
References[1] Wikipedia entry on “Indian Voting
Machines”, http://en.wikipedia.org/wiki/
Indian_voting_machines
[2] Election Commission of India website:
http://eci.nic.in/
[3] B Schneier, “Applied Cryptography”, Second
Edition, John Wiley, 2006.
[4] D S Hillygus, “The Evolution of Election
Polling in US”, Public Opinion Quarterly, vol
75, No.5, 2011, pp.962-981.
[5] M J Wilson, “E-Elections: Time for Japan
to embrace online Campaigning”, Stanford
Technology Law Review, 2011 STAN. TECH.
L REV. 4.
[6] B Menezes, “Network Security and
Cryptography”, Cengage Learning, 2011.
[7] Wikipedia entry on Data center: www.
wikipedia.org
[8] Joe Cosmono, “Choosing a Data Center”,
Disaster Recovery Journal, Summer 2009.
[9] HMDC: Harvard-MIT Data center : http://
www.hmdc.harvard.edu/
[10] S Waggener, “ UC Berkely Data center
Overview”, Aug 2006.
[11] National Knowledge Network(NKN)
website: www.nkn.in
[12] N Hastings et.al., “ Security Considerations
for Remote Electronic UVOCAVA voting”,
NISTIR 7770, NIST Report, Feb 2011.
[13] Wikipedia entry on “Indian General Elections
2014”, http://en.wikipedia.org/wiki/Indian_
general_election,_2014/
[14] H K Prasad et al., “ Security Analysis
of India’s Electronic Voting Machines”,
17th ACM Conference on Computer and
Communication (CCS’10), Oct 2010.
[15] J Wack, “ IEEE VSSC/1622: Voting System
Standards”, IEEE Computer, Sept 2014, pp.
94-97.
[16] P Hanes, “Online Voting: Rewards and
Risks”, Atlantic Council-McAfee Report,
2014. n
Sl No Parameter Quantity
1 No. of voters 814.5 million
Voter Registration Database size 815 Terabytes (assuming
1 Megabyte for each voter)
2 No. of Constituencies 543
3 Approx no. of Candidates 8,251
4 Average Election turnout (2014) 66.38%
5 Total Election Expenditure( 2004) 1300 Crores
6 No. of Polling Stations 935,000
7 Cost of Voting Machines(EVM) 10,500
8 Polling and Security staff 11 million
9 No. of EVMs and Control Units 1.7million and 1.8 million
Fig. 3 : MOP Online Polling Elements
T E C H N I C A L T R E N D S
Inauguration of Student Branch at VaranasiThe CSI Students Branch at Kashi Institute of Technology, Varanasi was inaugurated on Monday, 11th April,
2016 by CSI-National Secretary Prof A. K. Nayak, Chairman, CSI Varanasi Chapter, Dr. Sunil Kr Pandey and
Founder Chairman, CSI Varanasi Chapter Dr. S.C. Yadav.
After the inaugural session, a dedicated lab for the activities of CSI Student Branch at KIT Varanasi was
also being inaugurated. The inaugural ceremony was followed by a Workshop on "Mobile Application
Development using Android" by Prof. Rakesh Roshan and Prof. Abhay Ray from I.T.S, Mohan Nagar, Ghaziabad.
Following Guests were present during the inaugural ceremony of the CSI Student Branch: Shri Vipul Jain, Vice Chairman, KIT, Varanasi, Dr. Punit Tiwari,
Ex. Professor - IIT, BHU, Varanasi, Dr. K.K. Mishra Director, KIT, Varanasi, Dr. Niranajn Kumar Manna, Director, KIP, Varanasi
CSI Communications | June 2016 | 21
IoT Scale
IoT provides networking to connect
people, things, applications, and data
through the Internet to enable remote
control, management, and interactive
integrated services. IoT network scale,
the number of mobile devices exceed the
number of people on Earth. In addition,
predictions are made that there will be
50 billion ‘things’ connected to the
Internet by 2020.
IoT Service SupportSome advanced IoT services will need to
collect, analyse, and process segments of
raw sensor information, raw sensor data,
and we need to turn this into operational
control information. Some sensor data
types may have massive sizes, because the
number of sensor IoT devices are so large.
Therefore, a platform is required which
can collect and store all of this massive
amount of information. IoT databases will
be needed, which can be done using Cloud
Computing Support. IoT data analysis will
be needed, which can be done using Big
Data. The infl uence of IoT can be seen
in people, processes, data, and things.
If we see people wise, more things can
be monitored and controlled, so people
will become more capable. Process-wise,
more users and machines can collaborate
in real time, so more complex tasks can
be accomplished in lesser amount of time
because now we have more collaborative,
more coordinated eff orts that can be
pulled together. Data wise, we can collect
data more frequently and reliably. That
would result in more accurate decision
making. Things wise, things become more
controllable. So therefore, mobile devices
and things become more valuable. There
is more that you can do with them. The
overall economic impact, predictions have
been made that IoT has the potential to
increase global corporate profi ts by 21%
by 2022.
Where is this all coming from? It is a
combination of asset utilization, employee
productivity, supply chain and logistics
improvement, customer experience, and
other type of combined innovations. The
economic impact can be seen where
machine to machine connections are
increasingly becoming more and more
important.
IoT ApplicationsSecurity wise, surveillance applications,
alarms, real-time object and people
tracking and monitoring. Transportation-
wise, fleet management, road safety,
emission control, toll payment, real-
time traffic monitoring, and many
more are intelligent transportation
system applications. Healthcare-wise,
e-health, personal security, body-
sensor based customized healthcare
systems. Utilities wise, measurement,
provisioning, and billing of utilities for
gas, water, electricity, and so much
more. Manufacturing-wise, monitoring
and automation of a product chain.
Service and provisioning-wise, freight
supply, distribution monitoring, and
vending machines can be controlled and
provisioning support can be provided.
Facility management wise, home,
building, and campus automation can be
achieved through IoT technology.
IoT ArchitectureFirst, there are four major layers. To
start from the bottom, it is a sensor
connectivity and network layer, layer one.
On top of that is the gateway and network
layer, layer two. Next, on top of that is the
management service layer, layer three.
Finally, on top of it is the application layer,
layer four. If you look at what is in here,
for the service connectivity and network,
there is the sensor network, sensors, and
actuators, tags, which include RFID and
barcodes, and other types of tags as well.
At the gateway and network layer, we
are talking about a wide area network, a
mobile communication network, a Wi-Fi,
Ethernet, gateway control and things like
that. Then, going into the management
service layer. Here, device modelling
confi guring and management is a major
focus. Datafl ow management, security
control needs to be provided at the
management service layer. Finally, we
reach the overall application layer. This
is where we have endless applications.
In order to understand this, each layer is
depicted in Fig. 1.
The sensor layer provides sensor
connectivity and networking. At the
Internet of Things: Architecture and Research Challenges
Sanjay ChaudharyProf., Institute of Engineering & Technology,
Ahmedabad University, Ahmedabad
Ankit DesaiAsst. Prof., Babaria Institute of Technology,
Vadodara
Jekishan K. ParmarAsst. Prof., Babaria Institute of Technology,
Vadodara
Abstract: The article fi rst focuses on IoT Service Support and Economic Impact, and then explain IoT Applications and the IoT and
M2M Ecosystem. In order to describe the IoT Architecture, details on the Application Layer, Management Service Layer, Gateway &
Network Layer and Sensor Layer are explained. Finally, some important research and development areas are suggested along with IoT
Technologies.
R E S E A R C H F R O N T
Asset Utilization $2.5T
$19 Trillion
Market
Employee Productivity $2.5T
Supply Chain & Logistics $2.7T
Customer Experience $3.7T
Innovation $3.7T
M2M $6.4T 45%M2P or P2M $3.5T
55%P2P $4.5T
Table 1: IoT Market Table 2: IoT Technologies
CSI Communications | June 2016 | 22 www.csi-india.org
C O V E R S T O R Y
bottom, it starts off with the tags which
includes RFID and barcodes. Then, on
top of it, is sensors and actuators. This
is a part that has solid state, catalytic,
and also gyroscope, photoelectric, GPS,
photochemistry, infrared, accelerometers,
and similar things. On top of it is where,
network connectivity comes in picture
and that is like LAN, Wi-Fi and Ethernet.
Wi-Fi for wireless, and Ethernet for wired
local area networks. Then for personal
area networks which are the smaller scale
networks, which comes with wired and
wireless side both. To focus on wireless,
it includes Ultra Wi-Band (UWB), ZigBee,
Bluetooth, 6LoWPAN, and there are other
wired technologies.
The sensor layer is made up of
sensors and smart devices, real-time
information to be collected and processed.
Sensors use low power and low data rate
connectivity. This is where wireless sensor
network formation need to be made such
that, this sensor information is connected
and can be delivered to a targeted location
for further processing. Sensors are
grouped according to their purpose and
data types such as environmental sensors,
military sensors, body sensors, home
sensors, surveillance sensors, and other
things. Also, sensor aggregators, and these
are the gateway units, this needs to be
provided through networking connectivity.
At the local area network, there is Ethernet
and Wi-Fi, at the Personal Area Network,
there is ZigBee, Bluetooth, and 6LowPAN,
and other protocols as well. At sensors
which do not require connectivity to a LAN
gateway, some of them may be directly
connected to the Internet through a Wide
Area Network.
Now, gateway and network layer,
which is on layer two. At this layer,
the gateway needs to include micro-
controllers, radio communication modules,
signal processors and modulators, access
points, embedded and operating systems,
SIM modules, encryption, and units like
that. On top of it is our gateway network
which connects the gateways and the
sensor information together. In this
domain, wide area network and our local
area network are located.
In further details, the gateway and
network layer are layer two. This must
support massive volumes of IoT data
produced by wireless sensors and smart
devices. It requires a robust and reliable
performance regarding private, public,
or hybrid network modules. In addition,
network models are designed to support
the communication quality of service
requirements for latency, error probability,
scalability, bandwidth requirements,
security while achieving high levels of
energy effi ciency meaning that they’re
Table 3: IoT M2M ecosystem
Table 3 depicts about IoT and M2M ecosystem. Moreover, Table 4 depicts IoT based
software and hardware categorization along with providers.
Table 4: IoT Soft ware and Hardware
Fig. 1: IoT Architecture
R E S E A R C H F R O N T
CSI Communications | June 2016 | 23
low energy consuming. In addition, it is
important to integrate diff erent types of
networks into a single IoT platform. IoT
sensors are aggregated with various types
of protocols and heterogeneous networks
using diff erent technologies. IoT networks
need to be scalable to effi ciently serve a
wide range of services and applications
over a large scale network where in this
large scale network, some parts may have
diff erent protocols and diff erent packet
types, and diff erent security requirements.
Now, management service layer.
Operational support system (OSS), these
includes device modelling, confi guration,
management, performance management,
and security management, all of these
rests in this layer. Then, there is the billing
support system which includes billing
reporting, service analytics platform, this
is for statistical analytics, data mining,
text mining, in-memory analytics, and
predictive analytics. Then, management
service for security, always needed access
control, encryption, identify the accessed.
In addition, Business rules management
(BRM), rule defi nition, modelling,
simulation and execution. Then there is
the Business process management (BPM)
which is in charge of workfl ow process
modelling, simulation, and execution.
In the management service layer, it is
in charge of information analytics, security
control, process modelling, and device
management. The data management side
needs to consider periodic and aperiodic
characteristics. On the periodic side, for
periodic IoT sensor data, this requires
fi ltering because some data may not be
needed, but because it is periodically going
to be collecting information, there is going
to be a lot of information, lot of sensor
data that is not needed. Filter those out,
choose the ones that is needed, and use
and actuate, provide control management
based upon these types of fi lter of the
information that has something important
included inside. Then comes aperiodic
event triggered IoT sensor data. This may
require immediate delivery and immediate
response. For example, patient medical
emergency sensor data, if you fi nd
something is wrong with your heart and a
heart pacer is sending out a signal, well,
that needs to be sent on the top priority.
In addition, data management and data
abstraction. On the data management
side, this manages data information
fl ow. In addition, information access,
integration control all needs to provided,
at this data management control unit. In
addition, data abstraction, information
extraction processing is needed. This
needs to be used as a common business
model because there will be so much
information, that is needed to be able to
provide an abstract view of the overall
data that is in the system.
Now, the application layer. In the
application layer, fi rst, we describe the
horizontal market, fl eet management,
asset management, supply chain, people
tracking, and surveillance. The sectors
that use this overall domain of the
application are environmental, energy,
transportation, healthcare, retail, and
military. In the application layer, various
applications from industry sectors
can use IoT for service enhancement.
Applications can be classifi ed based
on the type of network availability, the
coverage size, the heterogeneity. Also,
business model as well as real-time or
non-real-time requirements. At enterprise
level of IoT, the scale of a community is
much larger. Moreover, there are diff erent
characteristics that needs to be consider
once reaching at the enterprise domain of
application services for IoT. Now, the utility
level, and here it is much larger, a national
or regional scale of IoT service support.
Now, then there is a mobile devices, which
are usually spread across other domains,
and this is because they have mobility. A
lot of the devices will be battery operated
and they will be portable IoT devices, and
Fig. 2: IoT Sensor Layer
Fig. 3: Gateway and Network Layer
CSI Communications | June 2016 | 24 www.csi-india.org
therefore they are going to move around,
or the car, or the train, or some other type
of transportation mechanism. In that case,
mobility support is very important.
Table 5. depicts the application
layer and looks at it in terms of smart
environment application domains.
Moreover, Table 6. shows services for
same smart applications of IoT.
IoT Research ChallengesIoT services must guarantee the security,
privacy and integrity of information and
user confi dentiality. Therefore, some of
the key features are thing authentication
and authorization, user authentication and
authorization. Now, what is this about?
The IoT network is there, now what things,
what objects, are going to be allowed data
to be collected from. In addition, when
a control signal is sent, what things are
going to be controlled? This needs to be
authenticated and authorized. In addition,
what users will be allowed to access to
IoT network to look at the data that is
sensed and collected, and also control the
objects, the things? The users need to be
authenticated and authorized. In addition,
thing to thing access control as in machine
to machine access control. In addition,
for security, IoT public key management
and IoT private key management is very
important.
In addition, IoT low overhead
protocol and IoT low complexity
processing is also very important.
In addition, mobility support is also
important. Mobility support increases the
applicability of IoT to new areas. Now,
mobile platform based IoT enables an
enormous range of future applications,
such as location based services (LBS),
social networking, and environment
monitoring and interaction. In addition,
energy and resource management. Now,
energy issues are related to optimization
of energy harvesting, conservation,
and usage and are essential to the
development of IoT. It is important to
consider resource constrictions, such as
wakeup delays, power consumption, and
limited battery and also packet size. Then
the identifi cation technology is another
Fig. 4: IoT Management Service Layer
Fig. 5: IoT Applicati ons
Smart Home
Smart offi ce
Smart Retail
Smart City
Smart Agriculture
Smart Energy & Fual
Smart Transpor-tation
Smart Military
Network Size Small Small Small Medium Medium /
Large
Large Large Large
Network Connectivity
WPAN,
WLAN,
3G, 4G,
Internet
WPAN,
WLAN,
3G, 4G,
Internet
RFID,
NFC,
WPAN,
WLAN,
3G, 4G,
Internet
RFID,
NFC,
WLAN,
3G, 4G,
Internet
WLAN,
Satellite
commu.,
Internet
WLAN,
3G, 4G,
Microwave
links,
Satellite
Commu.
WLAN,
3G, 4G,
Satellite
Commu.
RFID, NFC,
WPAN,
WLAN, 3G,
4G, Satellite
Commu.
Bandwidth Requirement
Small Small Small Large Medium Medium Medium –
Large
Medium –
Large
• WLAN: Wi-Fi, WAVE, IEEE 802.11 a/b/g/p/n/ac/ad, etc.
• WPAN: Bluetooth, ZigBee, 6LoWPAN, IEEE 802.15.4, UWB, etc.
Table 5: IoT Applicati ons
R E S E A R C H F R O N T
CSI Communications | June 2016 | 25
important area. IoT devices produce their
own contents, and the contents are shared
by any authorized user. Identifi cation and
authentication technologies need to be
converged and interoperated at a global
scale. Such that global users can use IoT
devices far away. Management of unique
identity for thing and handling of multiple
identifi ers for people and locations is very
important.
ConclusionInternet of Things is one of the
most emerging area of research and
development. IoT has received a huge
attention due to support of technology and
it is ability to penetrate into the existing
system very eff ectively for the purpose
of improvement in performance. This
article mainly showcases the market of
IoT, the applications and its domains and
architecture of IoT. Future, research and
development areas are also suggested.
References[1] J Bradley, J Barbier, and D Handler,
“Embracing the Internet of Everything
To Capture Your Share of $14.4
Trillion”, Cisco, White Paper, 2013.
[2] J Bradley, C, Reberger, A Dixit, and V
Gupta, “Internet of Everything: A $4.6
Trillion Public-Sector Opportunity”,
Cisco, White Paper, 2013.
[3] D Evans, “The Internet of Everything,”
Cisco IBSG, White Paper, 2012.
[4] S Mitchell, N Villa, M Stewart-
Weeks, and A Lange, “The Internet
of Everything for Cities,” Cisco, White
Paper, 2013.
[5] Hersent, O, Boswarthick, D and
Elloumi, O (2011) IEEE 802.15.4,
in The Internet of Things: Key
Applications and Protocols, John
Wiley & Sons, Ltd, Chichester, UK.n
Service Domain Services
Smart Home Entertainment, Internet Access
Smart Offi ce Secure File Exchange, Internet Access, VPN, B2B
Smart Retail Customer Privacy, Business Transactions, Business Security, Business Security, B2B, Sales & Logistics
Management
Smart City City Management, Resource Management, Police Network, Fire Department Network, Transportation
Management, Disaster Management
Smart Agriculture Area Monitoring, Condition Sensing, Fire Alarm, Trespassing
Smart Energy &
Fuel
Pipeline Monitoring, Tank Monitoring, Power Line Monitoring, Trespassing & Damage Management
Smart
Transportation
Road Condition Monitoring, Traffi c Status Monitoring, Traffi c Light Control, Navigation Support, Smart Car
support, Traffi c Information Support, Intelligent Transport System (ITS)
Smart Military Command & Control, Communications, Sensor Network, Situational Awareness, Security Information, Military
Networking
Table 6. IoT smart applicati ons and its services
Mr. Ankit Desai [CSI-1161555] is an Assistant Professor at Babaria Ins tute of Technology and Ph. D. Scholar at Ins tute of Engineering and Technology, Ahmedabad University. His areas of research interest includes Big Data Analy cs, Data Mining Classifi ca on and Distributed Systems. He can be reached at [email protected].
Mr. Jekishan K. Parmar [CSI-1161556] is an Assistant Professor at Babaria Institute of Technology. His areas of research interest includes Wireless Sensor Networks (WSN) with specialization in Underwater WSN, next generation networks and Mobile Computing. He can be reached at [email protected].
Mr. Sanjay Chaudhary [CSI-10170] is a Professor and Research Head at Institute of Engineering and Technology, Ahmedabad University. His areas of research interest includes Distributed Computing , Cloud Computing, Data Analytics, ICT Applications in Agriculture and Rural Development. He can be reached at [email protected].
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CSI Communications | June 2016 | 26 www.csi-india.org
Introduction
Since the time fi rst programming
language was developed, software
and software systems are evolving.
Software and software deliverables have
high impact on almost all fi elds from
research to medicine to astronomy to
home appliances. It doesn’t matter what
type a software is, or what domain it is on,
a software and its related attributes should
be maintained continuously from the time
it is delivered to the customer to the time
it is taken out of use. A software need to be
enhanced for its functionality for not just
in preventing the errors that may occur in
future but for making it compatible with
diff erent environment and also to fi x the
errors observed in a software that is in
use. Maintenance of a software should
be done in a very time effi cient manner
with less cost since it has high impact on
the performance of a system and therein
the customer satisfaction, which is the
ultimate goal of any service provider.
Even though software maintenance
is not tagged as a core fi eld in software
engineering compared to other software
related activities, almost 70% of time and
resources are allotted for maintenance
activities. On top of that, maintenance
activities are made challenging due
to the lack of proper documentation,
unstable team, unskilled staff etc. An
effi cacious software stand the test of
time irrespective of the hardware or the
environment it was designed for. In 1969,
Lehman addressed the issues related to
software maintenance, the core issues in
maintenance remain the same. The more
the software age is the more composite
its structure is and it will be diffi cult to
understand what happen to the system
which in turn makes it diffi cult to maintain.
The characteristics of a high quality
software is not just the development of
the software product but also to maintain
it according to the customer requirements
as and when it requires changes.
Why Software Maintenance?Software maintenance is a post-delivery
activity and its main purpose is to
preserve the value of software over time[2].
All the issues related to software, from
character enhancement to defect fi xing
is handled as a maintenance activity once
the software is delivered to the customer.
It is a well-known fact that world is never
static and perfect. Hence, requirements
and enhancements always keep evolving
during post-release span, or the product is
prone to failure due to creeping of latent
bugs. Requirements on which product was
initially defi ned, build and released hence
will undergo modifi cations.
An effi cient maintenance team
should perform the functional and
performance enhancements raised by
the customer. They should make changes
according to the environment and should
be able to fi x the post production bugs. If
maintenance is not performed reliably in
the specifi ed time, it will result in business
down time and it directly or indirectly
result in ‘unsatisfi ed customer’. It further
raise questions on product quality that
directly aff ects the organization.
Taking the example of scenario
that happened in UK on 12th December
2014, when they had to shut down fi ve
International Airports because of a
software glitch. The air traffi c control
system of UK dated back to 1960’s with
its source code written in redundant
JOVIAL language. The supercomputer
that runs the software crashed from 15:30
until 16:30 just for an hour. The incident
happened due to one line error in the
software source code. This one hour
window of software failure resulted in loss
of business and almost 10,000 customers
were directly aff ected.
This above stated example solely
reveals the importance of eff ective and
effi cient software maintenance.
Maintenance ProcessIn the current scenario maintenance
activity is either performed by the
software developing organization or can
be outsourced to a third party. Whoever
does the maintenance, the whole set
of activities is same. The whole set of
maintenance activity aims at maintaining
the reliability and quality with minimum
eff ort, cost and time. In real world, even
organizing the maintenance activity and
fi nding the right person is a diffi cult task[1].
In case of a third party doing software
maintenance, all the resources related to
the software to be maintained is handed
over to them.
Once customer raises any issues, it
is registered formally as a modifi cation
request and is fi rst analyzed and cross
verifi ed to check if the issue is relevant. A
change request form is generated based
on that. It is passed on to the maintenance
team after verifying its relevance, where
it is classifi ed into major enhancement,
minor enhancement or large
enhancement. The resources required for
the corresponding request as well as its
impact on the system is analyzed. Required
resources are allocated. According to the
urgency, service level agreement type and
classifi cation. Each maintenance activity
is performed and is released back to
the customer. For each bug identifi ed a
bug report is generated. For a migration
request, i.e. platform migration, a whole
maintenance team is assigned and the
same should be done without any change
in the software functionality.
Software maintenance is a continuous
eff ort, and the whole set of maintenance
activities costs fi ve times more than the
cost of whole development process[3].
Hence, it is important that maintenance
has to be performed with care, which
demands a clear, consistent and complete
knowledge of the requirements. The main
necessity for an effi cient maintenance
work is proper communication of the
requirement from customer to the
maintenance team. The complete set
of documents related to the product
development must be made available
to the maintenance team. The team
members should have appropriate skill set
to handle the product under maintenance.
In most of the cases, people who does
maintenance may not be a part of the
product development, thereby demanding
enough awareness to be given to the team
about the tools and techniques used[4].
In case of a feature enhancement, the
maintenance team should be well aware of
Software Maintenance: An OverviewSharon Christa
Dept. of Information Science and Engineering, Dayananda Sagar College of Engineering, Bangalore
Suma V.Professor, Dayananda Sagar College of
Engineering, Bangalore
A R T I C L E
CSI Communications | June 2016 | 27
the modules that directly or indirectly gets
aff ected by it. Failure of which may lead to
the condition called software regression.
Software maintenance was never
considered a rewarding job for the reasons
like less creativity involved and also the
work load. This results in the likelihood
for staff leaving the job or changing
the domain, thus directly aff ecting the
maintenance activity. For the realization of
high quality software that is dependable,
understandable and effi cient and satisfi ed
customer, it is required to have accurate
estimation of eff ort, time and cost
involved in the maintenance activity as
well as the quality of people involved in
the maintenance. This directly results in
the quality of software maintenance.
Types of Maintenance ActivitiesBased on the survey by Lientz and
Swanson in the late 1970’s the whole set
of maintenance activities are classifi ed
into four broad categories[2]. Corrective
maintenance is actually bug fi xing. i.e. to
correct faults detected in the software
post-delivery. The whole process involves
reproducing the failure reported and fi nds
out what can be the cause of the failure.
If it might be in the code, check for the
documentation. Fix the bug without
altering anything, since faults injected
when fi xing bugs are called regression
faults. Update thedocumentation. Once
the maintenance is done it is tested
again to make sure the fi x works and no
regression faults has been introduced.
Adaptive maintenance is performed
to make a computer program usable
in a changed environment which
includes hardware upgrade, software
platform changes or policy changes.
The changes should preserve existing
functionality and performance otherwise
adaptive maintenance is performed.
Perfective software maintenance is
performed toimprove the performance,
maintainability, or other attributes
of a computer program that includes
functional or nonfunctional requirements.
Preventative maintenance is performed
for the purpose of preventing any
maintenance issues before they occur.
It involves changing a software system
in such a way that it does not alter the
external behavior of the code yet it
improves its internal structure[2].
75% of the maintenance eff ort was
spent on adaptive and perfective whereas
17% of eff ort on error correction.It is an
indisputable fact that estimating software
metrics within software development and
maintenance project is important. Since,
software eff ort has a direct relation to
the overall cost fi gures of the project, it
is important to predict software eff ort
metric. Eff ective prediction of eff ort can
help allocate proper resources required
and also helps in realistic cost estimates.
Since eff ort estimation is not made
exactly based on the actual statistics, but
is computed based on domain knowledge,
the estimated eff ort will not refl ect the
complexity or skill set required to perform
a particular maintenance task. However,
the estimation model that exists in
the industry has lacuna since they use
multivariate linear regression techniques,
and this technique itself has drawbacks
demanding the need for an accurate
software estimate[5].
Related Work In general, more than half of the
development time of a software engineer
is spent for understanding, modifying,
and retesting existing code, which is
the maintenance activity. So it is very
much important to identify the factors
infl uencing the maintenance process
and also appropriately calculating the
eff ort, time and cost associated with
it[1]. The analysis of maintenance work
performed on several products helped
the authors of[1] to conclude the lacuna
in the maintenance phase. Software
maintenance activities can be viewed in
diff erent perspectives. Authors of[3] has
very clearly stated the problems in external
and internal perspectives which include
high maintenance cost, slow maintenance
service, striving in prioritizing the change
request, poorly designed and coded
software. To add misery, tremendous lack
of documentation.
Authors in [6] have classifi ed the
various problems encountered by
software maintainers which include
perceived organization alignment
problems, process problems as well as
technical problems. The authors of [7]
have pointed out that very less research is
performed issues in software maintenance
as compared to software development.
Very few books and research articles
are coming up based on it. Most of the
software engineering books are not
referring or considering maintenance in a
larger extent.
Software maintainers provide
services on daily basis based on various
contexts and interfaces.
Even though International standards
body has well defi ned specifi cations
for maintenance related activities, the
SEEBOK initiative identifi ed a large a
number of software maintenance specifi c
activities not covered under it [3].
Authors in [8] clearly state that it is
an unmanageable task to estimate the size
and there by the eff ort related to it with
a degree of accuracy. Along with that the
estimation in maintenance is compounded
by various other factors which includes
complexity and functionality of the system
to the software maintenance team that
does not have anything to do with the
design and development of the same. The
authors of[8] also stated that maintenance
is an evolutionary activity that is entirely
diff erent from development process and
also has diff erent inherent characteristics
and requires more attention in context of
estimation models.
Authors in [9] have stated the
lacuna in the software cost estimation
model and its importance. They managed
Fig. 1: Types of Maintenance Acti vity
A R T I C L E
CSI Communications | June 2016 | 28 www.csi-india.org
to conclude the categories that come
under cost estimation; which includes
size, eff ort, project duration and cost.
The authors has mentioned the factors in
which maintenance practitioner’s struggle
which includes which software cost
estimation model to use? Which software
size measurement to use? And what is a
good estimate?
The authors of [9] have also mentioned
that real time data from software
maintenance projects are not available
because of organizational constraints.
Also stated that very few research is
going on related to software maintenance
eff ort estimation as related to software
development eff ort estimation.
Gerardo Canfora and Aniello[10], in
their article ‘Software Maintenance’ have
mentioned maintenance task as an ice
berg to specify and highlight the herculean
problems and costs that relates to it.
Thomas M. Pigoski[7] has pointed
out very clearly the breakdown of the
maintenance phase. The key issues,
according to him include measurement,
cost, estimation, technical and
management. The author mentioned
the various process models as well
as activities related to it. The author
mentioned the lacuna in the existing
activities related to maintenance. The
author mentioned the unique activities as
well as the supporting activities related to
maintenance. The author has mentioned
the lack of understanding and planning in
the maintenance phase. The author states
that it is due to the lacuna in the attributes
associated with maintenance. The author
mentions in detail the key issues related to
the technical level, the managerial level as
well as organizational level.
Author of[11] has cited the key
issues that come under technical level
in the maintenance phase as limited
understanding, maintainability, and
testing and impact analysis. The author
states that the technical staff will have very
less or zero knowledge about the software
under maintenance which will intensify
the problem. From the organizational
aspect, cost and cost estimation is a major
factor. Since, major share of life cycle cost
is consumed by maintenance phase, and
all organizations completely depends on
the project turn over, the gap in proper
cost estimation is a major factor that
concerns them.
Depending on the above stated
aspect the software, post deployment
is outsourced or a maintenance team is
assigned. In the managerial perspective
there are more complex challenges that
include process, staffi ng, training to
staff , experience of staff etc. Software
at the managerial level is responsible for
deciding which maintenance technique to
be used also.
Measurement and monitoring of
maintenance process is the one area
where there is a major dearth in research.
According to the authors in[12] the current
maintenance practitioners are not able
to keep up with the requirements in the
maintenance fi eld. Because of the lack of
documents as well as design details, it will
become further complex.
Even though software engineering is
a well-defi ned area, evaluation of software
maintenance activity is not well defi ned.
Authors of[12] has proposed a quantitative
based approach. But it lacks in analyzing
the status of maintenance activities.
Outlier behavior again is not considered in
this study.
Scope for Maintenance ActivityBased on the research work done till date
under software maintenance, the broad
maintenance areas can be maintenance
cost and thereby the maintenance eff ort
estimation activity. Since, the complex
maintenance procedure cannot be
evaluated in a stretch, the eff ort prediction
and there after the cost estimation is of
high importance.
Categorizing and estimating the
activity based on maintenance type is
another area that if given importance will
give positive result in the maintenance
process. Even though maintenance
activities diff er very much from
development activities the process model
followed by maintenance comply on
development process only. The lacuna in
process model directly impacts the quality
in the maintenance activity as well as the
product.
Research is habitually overlooked
for maintenance because maintenance
is a post-production activity. Further, the
concern for any industry is more on pre-
production activities and its deployment to
the fi eld within the negotiated constraints.
It is directly related to the key issues
in various levels of the organizational
hierarchy. Selecting the maintenance
practitioner with right skill set is another
issue that needs immediate addressing.
On top of that which technique has to
be adapted for a requested maintenance
activity is also one area that is less
addressed.
Possible OutcomeIdentifying the factors that directly
and indirectly aff ect the maintenance
activity will in turn make the estimation
activities easy. Implementation of an
eff ective software maintenance model
will have a very high impact in the
quality of software and thereby with the
customer satisfaction. It will result in the
development of eff ective estimation of
cost, eff ort and time model. Thus, it will
reduce the time for maintenance activity.
Results in eff ective resource allocation.
Thereby, it improves the reliability and
productivity of the company.
ConclusionSoftware is one of the highly benefi cial
introduction of human thoughts to the
society. In fact generation of software and
application of software in all the domains of
livelihood has turned out to be a panacea.
Hence, it is the rudimentary responsibility
of every software developer to develop
software projects which is going to be the
best fi t for purpose. Hence, every software
industry strives towards all those strategies
which leads towards the development of
good acceptable software. These strategies
include both pre-production and post-
production actions one has to follow for
every software development.
Nevertheless such quality gates
are emphasized, yet there is always a
proneness to overlook post-production
action points. This is because of the
investment on time and eff ort involved in
enhancing productivity in the company
rather than looking at rework under
maintenance activity. However, it is proven
that the cost, time and eff ort required for
maintenance is very high and endorses the
reputation of the company too.
This article therefore acts as a travel
light for reducing the rework expense due
to maintenance and uphold the fl ag of the
company in the industrial quality market.
References[1] Gerardo Canfora and Aniello Cimitile,
“Software Maintenance”, Software
Maintenance: Research and Practice
Journal, November, 2000.
A R T I C L E
CSI Communications | June 2016 | 29
[2] https://en.wikipedia.org/wiki/Software_
maintenance on 27-08-2015
[3] Alain April, Jane Huff man Hayes, Alain
Abran, and Reiner Dumke, “Software
Maintenance Maturity Model (SMmm):
The software maintenance process
model”, 2004.
[4] Henk van der Schuur, Slinger Jansen, Sjaak
Brinkkemper, “Sending Out a Software
Operation Summary: Leveraging Software
Operation Knowledge for Prioritization
of Maintenance Tasks”, Joint Conference
of the 21st International Workshop on
Software Measurement and the 6th
International Conference on Software
Process and Product Measurement, 2011.
[5] Márcio P Basgalupp, Rodrigo C Barros,
Duncan D Ruiz, “Predicting Software
Maintenance Eff ort through Evolutionary-
based Decision Trees”, SAC’12, Riva del
Garda, Italy, March 25-29, 2012.
[6] Bennett, K H Software Maintenance: A
Tutorial. In Software Engineering, edited
by Dorfman and Thayer. IEEE Computer
Society Press: Los Alamitos, CA, 2000;
289-303 pp.
[7] Pigoski T M Practical software
maintenance: Best practice for managing
your software investment. John Wiley &
Sons: New York, NY, 1997; 384 pp.
[8] Pankaj Bhatt, Gautam Shroff, Arun
K Misra, “Dynamics of Software
Maintenance”, ACM SIGSOFT Software
Engineering Notes Page 1 September
2004, Volume 29, Number 5.
[9] Ruchi Shukla, Arun Kumar Misra,
“Estimating Software Maintenance Eff ort
-A Neural Network Approach”, ISEC’08,
February 19–22, 2008, Hyderabad, India.
[10] Gerardo Canfora and Aniello Cimitile,
”Software Maintenance”, Article, 2010.
[11] Rajiv D. Banker, Srikant M Datar, Chris
F. Kemerer, “A Model to Evaluate
Variables Impacting the Productivity
of Software Maintenance Projects”,
A Journal on Management Science,
Vol. 37, No. 1, January 1991.
[12] Suma V, Pushpavathi T P, and
Ramaswamy V , “An Approach to Predict
Software Project Success by Data Mining
Clustering”, International Conference on
Data Mining and Computer Engineering
(ICDMCE’2012), Bangkok (Thailand),
December 21-22, 2012. n
Sharon Christa is currently working as Assistant Professor in the Dept. of Informa on Science and Engineering, Dayananda Sagar College of Engineering, Bangalore. She is perusing Ph.D in Computer Science and Engineering from Visvesvaraya Technological University. Her research interest includes So ware Maintenance, Data Mining and Machine Learning Techniques and its Applica ons.
Dr. Suma V. [CSI - 01150179] is currently working as Professor in Dayananda Sagar College of Engineering, Bangalore, India. She holds a B.E., M.S. and PhD in CSE. She has vast experience spread across Industry, Academics and R&D. She has published several Interna onal publica ons and an invited author for an Interna onal book chapter. She is listed in various Interna onal Biographical centres and recipient of various recogni on awards. She can be reached at [email protected].
CSI Communications | June 2016 | 30 www.csi-india.org
C O V E R S T O R Y
Introduction
Human life is an engraved version of
various decisions taken at various
stages. Decisions are an integral and
indispensable part of the human being. In
present era, technology drives every aspect
of human life from trades to talks, from
evaluation to education, from socializing
to sectoring, and so on. It has been feasible
to integrate technology in the process of
making decisions due to the advancement
of tools and technology and the ease of
their availability. Decision Support Systems
also termed as DSS are employed at various
levels and complexity of human life. This is
an eff ort to brief the evolution of Decision
Support System, basic concepts and
various applications of Decision Support
System and exploring the research scope
of Decision Support System in educational
spheres. Historically, Decision Support
Systems were addressed for long term
decisions of managerial nature. However,
with the increased resource availability and
improved user perception about Decision
Support System, it’s now more feasible to
device DSS oriented tools that enhance the
eff ectiveness of human decision making
capability and access to information that
enables better decision making[1]. Decision
Support Systems can support human
cognitive defi ciencies by enabling to access
relevant knowledge that helps structuring
of decisions and selection from alternatives
defi ned intelligently[2].
History of Decision Support SystemFrom simple decision making tool for
individual users, Decision Support System
has evolved to include and cater to
various functionalities. Decision Support
Systems primarily assist decision makers
to take prompt, powerful, productive and
profi cient decisions. Hence, Decision
Support System can act as technology
solutions that aid decision making and
problem solving of complex nature[3] .
History of Decision Support System
evolution and advancement can be
tabulated as follows:
Defi ning Decision Support SystemDecision refers to the ability of an individual
to think and judge in selection process
from the alternatives available. Support
in broader terms refers to assistance for
performing certain execution. The system
refers to a prescribed way of doing things.
Decision Support System refers to the
system that supports an individual or
group in the decision making process.
A “Decision Support System” may be
defi ned in numerous ways. Few defi nitions
accentuate hardware and software
components while others may focus
primarily on functionalities, while a few
even describe system dynamics as user
interfaces, job functions and data fl ow.
Among the various defi nitions of Decision
Support System that exist, we enumerate
some of them as below:
Turban defi nes it to be “an interactive,
fl exible, and adaptable computer-based
information system, especially developed
for supporting the solution of a non-
structured management problem for
improved decision making. It utilizes
data, provides an easy-to-use interface,
and allows for the decision maker’s own
insights.”[4].
According to Keen and Scott Morton,
Decision Support System couples the
cognitive ability of individuals with the
technical abilities of the computer to
improve the quality of decisions. “Decision
Support System are computer-based
support for management decision makers
who are dealing with semi-structured
problems.” [5]
For Sprague and Carlson, Decision
Support System is “interactive computer-
based systems that help decision
makers utilize data and models to solve
unstructured problems[6].
According to Power, the term
Decision Support System remains a useful
and inclusive term for many types of
information systems that support decision
making[7].
Characteristics of Decision Support SystemFrom the above defi nitions, it becomes
clear that Decision Support System does
not confi ne its applications and functions
to its name but also encompasses
many varied and exclusive features
and capability. Some of the distinct
capabilities of Decision Support System
can be enumerated as:
Fundamentals of Decision Support System and Exploring Research Application in Education
Ankita KanojiyaAsst. Prof., GLSICA, GLS University, Ahmedabad
Viral NagoriAsst. Prof., GLSICT, GLS University, Ahmedabad
A R T I C L E
Year Advancement
Late 1950s Theoretical work on organizational Decision Support System
1960s Development of technical context of interactive computer
systems
Middle 1970s Evolution of Decision Support System as an area of research
1980s Decision Support System research and spread got momentum
Middle 1980s Evolution of variations as Executive Information Systems,
Group Decision Support Systems
Organizational Decision Support Systems
1990s Data Warehousing and On-Line Analytical Processing advanced
the Decision Support System applications and modelling
Turn of millennium Web-based analytical applications were introduced manifesting
the effi ciency of Decision Support System
Table 1: History of Decision Support System
CSI Communications | June 2016 | 31
• Computer based
• Interactive
• Flexible
• Adaptable
• Support solution of unstructured/
semi structured problems
• Utilizes data and Handle large
amount of data [8]
• Enables to include users
own intuition and knowledge
application
• Supports for better and effi cient
decision making
• Supports interdependent and/ or
sequential decisions
• Contains capabilities as
generation as presentation of
reports with lieu to needs[8]
• Can be equipped with ability
to represent data as texts and
graphical representations[8]
• Use advanced software packages
for performing analysis and/or
comparisons both multifaceted
and/or sophisticated in nature[8]
• Intended as a system to support
decision making process rather
than replacing decision makers[9]
• Focuses to make process more
eff ective instead of effi cient[9]
• Can support multiple independent
or interdependent decisions taken
as part of individual, group or
team-based decision-making[9]
Thus, it is clear that Decision Support
System is a multifaceted interactive
system to assist fundamentals as
database research, artifi cial intelligence,
human-computer interaction, simulation
methods, software engineering, and
telecommunications, the list being
exhaustive [4].
Components of Decision Support SystemBecause of the homogeneous nature of the
application areas and domains of Decision
Support System, describing Decision
Support System using a particular
structure is neither feasible nor possible.
However, to generalize the idea, any
Decision Support System shall compriseof
basic components that are at the core of
the Decision Support System architecture.
In general terms, Decision Support
System components commonly include:
• Data and information that forms
the base for any Decision Support
System
• System comprising of input,
output and processing – that
forms the dynamic
• Data for maintaining DBMS• Model describe and pertains to
model employed in a particular
Decision Support System
• User interface
Development Life Cycle of Decision Support SystemThe approach followed by diff erent
developers for developing and composing
Decision Support System may be
diff erent. However, the basic development
sequence that may be adopted by various
developers are:
Categorizing Decision Support SystemThe varied nature of Decision Support
System, the various defi nitions available,
and also considering the outlook and
viewpoint of various researchers, Decision
Support System may not be categorized
into simple types. However, below is
an exhaustive list of types of Decision
Support System depending on their
functionalities, capabilities, etc.
• Data driven Decision Support System– these type of Decision
Support System enable
manipulation of data connected
to time series by access large
databases of companies.
They may take form of MIS,
data warehousing, executive
information systems, etc.[10].
• Model driven Decision Support System–these type
of Decision Support System
enable manipulation of data
by employing various forms of
models as accounting, fi nancial,
representational, optimization,
etc. such systems are basically
employed for data analysis of
elementary or complex level
depending on pre-defi ned data
and parameters. Includes systems
that use accounting and fi nancial
models, representational models,
and optimization models[10].
• Knowledge driven Decision Support System– these type of
Decision Support System enable
solving of specialised problems as
experts using data mining[10].
• Document driven Decision Support System– these type
of Decision Support System
enable storage and retrieval
of documents for analysis in
form of large online or offline
databases [10].
• Communication driven Decision Support System– where
communication driven Decision
Support System includes
communication, collaboration
and coordination[10].
• Single user Decision Support System– this type of Decision
Support System enable
functionality that replace multiple
decision makers by a single
system[11].
• Group Decision Support System– this type of Decision
Support System enables solving
unstructured problems as group
by employing special technical,
personnel and procedural
requirements [11].
Fig.1: Development Life Cycle of Decision Support System
A R T I C L E
CSI Communications | June 2016 | 32 www.csi-india.org
• Organizational Decision Support System– this type
of Decision Support System
enable use of common tools at
several workstations in multiple
organisational entities [11].
• Passive Decision Support System–this type of Decision
Support System just support
decision making but does not have
outcome as specifi c suggestion
and/ or solutions.
• Active Decision Support System–this type of Decision Support
System support decision making
by giving outcome as one or more
can suggestions and/ or solutions.
• Cooperative Decision Support System– this type of Decision
Support System enable user to
manipulate the outcome before
sending it back to system for
authentication.
Benefi ts of Decision Support SystemThe diversity that a Decision Support
System characterise, enable users to yield
many benefi ts from the sophisticated
system. Some of the implicit as well
as explicit benefi ts that can be yielded
through Decision Support System can be
enlisted as follows:
• Increased productivity – effi cient,
timely and accurate decision
will ultimately lead to increased
output both qualitative and
quantitative.
• Increased understanding – while
integrating Decision Support
System in decision making
process, the user can get aware
of many undiscovered aspects
pertaining to decision making,
thus enabling increase in the
understanding of the problem as
well as the domain.
• Increased speed – the integration
of Decision Support System will
surely speed up the decision
making process
• Increased fl exibility – Decision
Support System integration can
introduce fl exibility for the user in
terms of analysing and adjudging
the alternatives and selecting
decisions.
• Reduce problem complexity – the
computerized form of Decision
Support System can reduce the
complexity of problem to be
addressed as the problem domain
is encompassed in knowledge
base of Decision Support System
• Reduce cost – the cost in terms of
time, cognitive energy, fi nancial
expense and many other concerns
can be reduced using Decision
Support System.
Application Areas of Decision Support SystemDecision Support System as interactive
computer based systems have a wide span
of area of applications. The fl exibility and
adaptability of Decision Support System
and its diversifi ed structure enable them
to be applicable in various fi elds pertaining
of diff erent domains. We are exploring
the possible implementation of DSS in
selection of eff ective pedagogy. Hence, we
are providing an exhaustive enumeration
of the applications in the education
domain where Decision Support System
can be employed and benefi ts harvested
for:
• Selecting high school teaching plan - The research proposes a
model that uses O-NET Scores
and multiple intelligence to enable
to choose high school learning
plans[12].
• As an advisor – The research aims
to address the last mile issue by
proposing a web based tool that
enables effi cient use of existing
student information system at the
university[13].
• Predicting student performance – in this proposed work, the
researchers supervised data
mining algorithm [Naive Bayes
[NB) algorithm) to predict course
success[14].
• Selecting/ purchasing Smart phone – enable selection
of smart phones by narrow
recommendations based on
Fuzzy Simple Additive Weighing
algorithm[15].
• Capacity utilisation - The
research focuses to address the
solution to problem of enabling
effi cient utilisation of capabilities
in terms of teaching resources for
students[16].
• Class room scheduling – the
proposed system tries to address
the intellectual problem of
allotting subjects, classrooms,
lectures and other class room
scheduling problems[17].
• E – Commerce– the paper
addresses some of the critical
issues and extensiveness of DSS
in e – commerce [18].
• Admission process – the research
propose the use of ERP based
Decision Support System to solve
the shortcoming for admission
process [19].
Decision Support System: Prospective Research Perception In EducationTechnology is largely influencing and
commanding the education field in many
aspects. The new science of learning
focuses on learning with understanding.
With the learners that are diverse in
many aspects as IQ level, adaptation
to learning culture and infrastructure,
change in technological approach, etc. it
has been noted that the same pedagogy
do not uniformly apply to the all the
learner(s). With increasing personal
teaching demands, personalisation of
selecting pedagogy also has become
a challenge. The prospective research
can be carried out to integrate the
Information and Computer Technology
in Education by proposing a Decision
Support System prototype that for
selection of pedagogy for targeted group
of learners as individual or group. Also
the Decision Support System prototype
will measure the effectiveness of the
pedagogy during teaching – learning
experience.
The research shall focus on
possibilities of developing a prototype
model that will try to integrate aspects
and features:
• Suggesting pedagogy or set of
pedagogy that can be used to
targeted set of users.
• Enable user to add various
pedagogy and tools.
• Enable user to add, modify
and update dimensions and
characteristics of the learners.
• Enable users to evaluate the
eff ectiveness of applied pedagogy
by means of evaluation.
To implement above listed
characteristics that Decision Support
System prototype should acquire, the
research is planned to design a hybrid
Decision Support System that can
A R T I C L E
CSI Communications | June 2016 | 33
exhibit the capabilities of data driven
and knowledge driven Decision Support
System.
ConclusionDecision Support Systems are computer
based dynamic and interactive
systems that enable decision makers
in prompt, appropriate, efficient,
effective and affluent decision making
thereby increasing the effectiveness
of decisions. Decision Support System
have paved its way of enhancing the
capabilities and applications since the
inception of the concept. The varied
domains wherein DSS are applicable
and the prospective domains that are
unexplored present much scope of
research. The domain of interest for
our research comprises of proposing a
Decision Support System prototype for
selecting pedagogical tools to enhance
the teaching learning experience and
measure the effectiveness of same. The
area of major insight shall be developing
Decision Support System, estimating
pedagogical tools, alluring the teaching
learning techniques to be included,
identifying various variables and factors
affecting the implementation and
working proposed DSS.
References[1] Daniel J Power, Frada Burstein, and
Ramesh Sharda. 2011, Reflections on
the Past and Future of Decision Support
Systems: Perspective of Eleven Pioneers.
Springer Science+Business Media, ,
pp. 25-48.
[2] Castro-Schez, J J Jimens, L Moreno,
J & Rodringues, L 2005, Using fuzzy
reporting table–based technique for
decision support. Decision Support
Systems, pp. 293-307.
[3] J P Shim a, *, Merrill Warkentin a,*,
James F Courtney b, Daniel J Powerc,
Ramesh Shardad, Christer Carlssone.
2002, Past, present, and future of
decision support technology. Elsevier
Science B.V., pp. 111 - 112.
[4] E, Turban, Decision support and
expert systems : management support
systems. s.l. : Englewood Cliff s, N J,
Prentice Hall, 1995.
[5] Keen, P G W and M S Scott
Morton., Decision support systems:
an organizational perspective. s.l.:
Reading, Mass., Addison-Wesley
Pub. Co., 1978.
[6] Sprague, R H and E D Carlson.,
Building eff ective decision support
systems. Englewood Cliff s, : N J,
Prentice-Hall., 1982.
[7] Power, D J 1997, “What is a DSS?”. The
On-Line Executive Journal for Data-
Intensive Decision Support.
[8] Tripathi, K P , Organization, Decision
Support System is a tool for making
better decisions in the Indian Journal of
Computer Science and Engineering.
[9] Marakas., Decision Support Systems.
s.l. : Prentice-Hall, 2003.
[10] Power, Concepts and resources for
managers.
[11] Eom, Sean B Decision support systems.
International Encyclopedia of
Business and Management . London :
International Thomson Business
Publishing Co, 2001.
[12] Sanrach, Thanrat Sintanakul and
Charun. July 2015, A Model of
Decision Support System for Choosing
High School Learning Plan Using
Students’ O-NET Score and Multiple
Intelligence. International Journal
of Information and Education
Technology.
[13] Tony Feghali, Imad Zbib and Sophia
Hallal. 2011, A Web-based Decision
Support Tool for Academic Advising.
Educational Technology & Society.
[14] lalit Dole, Jayant Rajurkar, A Decision
Support System for Predicting Student
Performance.
[15] Robertus Nugroho Perwiro Atmojo,
Anggita Dian Cahyani, Bahtiar Saleh
Abbas, Bens Pardamean, Anindito,
Imanuel Didimus Manulang. 2014,
Design of Single User Decision
Support System Model Based on Fuzzy
Simple Additive Weighting Algorithm
to Reduce Consumer Confusion
Problems in Smartphone Purchases.
Applied Mathematical Sciences,
pp. 717 - 732.
[16] Scholl, Svetlana Mansmann and
Marc H. May 2007, Decision Support
System for Managing Educational
Capacity Utilization. IEEE Transactions
on Education.
[17] Jaime Miranda, Pablo A Rey, Jose M
Robles. 2012, A web architecture based
decision support system for course
and classroom scheduling. Elsevier,
pp. 505-513.
[18] M Senthil Velmurugan, Kogilah
Narayanasamy. 2008, Application
of Decision Support System in
E-commerce. Communications of the
IBIMA .
[19] Das, Rajan Vohra & Nripendra
Narayan. oct. 2011, Intelligent Decision
Support Systems for Admission
Management in Higher Education
Institutes. International Journal of
Artifi cial Intelligence & Applications.
[20] Gachet, Hättenschwiler. Decision
Support Systems. Wintersemester.
[21] Lohala, Kumar. Decision Support
Systems.
[22] Decision Support Systems. [book
auth.] IGNOU Notes.
[23] Jolana Sebestyénová. 2007, Case
based Reasoning in Agent based
Decision Support System. Acto
Polytechnica .n
Prof. Ankita Kanojiya [CSI - 8000672] is a faculty member at GLS (I & RKD) Ins tute of Computer Applica ons (BCA) at the Faculty of Computer Applica ons and Informa on Technology, GLS University, Ahmedabad. Her area of interests include educa on and technology integra on, expert systems, integra ng managerial aspects in technology and others.
Dr. Viral Nagori [CSI - 100066] is an Asst. Prof. at GLS Ins tute of Computer Technology (MCA), Ahmedabad. He is currently working as Hon. Treasurer of CSI Ahmedabad chapter. His areas of interests include cyber security and ar fi cial intelligence. He can be reached at [email protected].
CSI Communications | June 2016 | 34 www.csi-india.org
C O V E R S T O R Y C O V E R S T O R Y
Introduction
Security of smart cities is an important
topic which concerns with securing
of infrastructures and services for
smart cities. The city that uses new and
advanced technologies to improve and
control services to make people life more
comfortable and better, this city called
“Smart City”. Using these technologies,
have several advantages such as better
utilization of hardware resources, save
money and time with providing better
services. Smart city is considered as a
development vision of urban through
use Information and Communication
Technology (ICT) solutions in a secure
way to administer services such as
transportation systems, power plants,
energy management, water supply
networks and waste management. An
intelligent management system is used for
controlling and managing these services.
The ICT can be used to improve quality,
interactivity and performance of urban
services. Smart city applications aims for
enhancing the management of urban fl ows
and allowing for rapid and fast responses
to complex challenges. The main target
of constructing smart cities is to enhance
and improve quality of people life through
using advanced technologies such as
Internet of Things (IoT), Fog Computing,
Cloud Computing and Big Data.
In recent time, the number of new
and advanced technologies inside smart
cities is increased, which raised the
danger of being attacked by hackers and
malicious users. Every new technology
and innovation creates new opportunities
for attackers for lunching new attacks
and crimes so that the number of
cybercrimes in smart city will increase.
Providing and creating secure, reliable
and resistant smart city are important
issues for protecting people life and
guarantee continuity of providing better
and intelligent services. Higher degree of
connectivity of services has the possibility
to open up new several vulnerabilities,
cyber-attacks and severe crimes and
incidents against critical sectors in smart
city. This article discusses services and
technologies in addition to security
issues in smart city for designing and
implementing new strategies and methods
to secure infrastructures of smart cities in
eff ective and effi cient manner.
Services in Smart CitiesSmart city can provide many services
for citizens to make their life more
comfortable. There are many services in
smart city as shown in Fig.1 as follows:
• Smart Public Transportation: Public transportation is very
critical sector for providing
transportation services for
citizens. Real-time data about
schedules of arrivals and
departure time is provided to
inform the citizens. In smart
city, there also intelligent
highways with warning messages
about climate conditions and
unexpected incidents.
• Smart Car Parking: Providing
smart parking services through
parking application to fi nd
available parking slots which help
in saving time and monitoring of
parking spaces available in the
city.
• Smart Traffi c Congestion Control: Monitoring traffi c jams and
congestions depend on size and
present traffi c conditions are very
important services for citizens.
This service will help to optimize
driving and walking routes, and
save time.
• Smart Street Lighting: Managing
and controlling street lighting
based on weather and detection
of moving cars and people will
help to save energy and providing
intelligent and weather adaptive
lighting services in streets of
smart city.
• Surveillance and Traffi c Security: Traffi c and surveillance security
through using cameras, detection
sensors of gunshot in addition
to other security solutions will
provide more control and monitor
of illegal activities in street like
stealing of banks.
• Smart Energy Management: Energy management system
Cyber Security in Smart CitiesEzz El-Din Hemdan
Research Scholar, Dept. of Computer Science, Mangalore University, Mangalore, India.
Madhvaraj M. ShettyResearch Scholar, Dept. of Computer Science,
Mangalore University, Mangalore, India.
Manjaiah D. H.Professor, Dept. of Computer Science,
Mangalore University, Mangalore, India.
S E C U R I T Y C O R N E R
Fig. 1: Smart City Services
CSI Communications | June 2016 | 35
will help to deliver energy based
on needs, in addition to energy
consumption monitoring to save
cost and resources.
• Smart Water Management: Measuring of water quality,
detects leaks, and determine
problems through smart pipes will
help to save water consumptions.
• Smart Waste Management: Waste containers are providing
with sensors to detect the volume
of garbage. This will help stopping
a container that smells at early
stage.
Technologies for Building Smart CitiesSmart city need various advanced
technologies for enabling and building it to
provide better services for citizens. There
are many requirements which are required
for building smart cities as shown in Fig. 2
as follows:
• Network Connectivity: Network
connections enable to use smart
services inside the smart city in
eff ective and effi cient manner.
Smart city will need higher
degrees of network connectivity
to assist new advanced services.
• Internet of Things (IoT): Internet
of Things enables to connect
things (i.e. devices) which use in
smart city in smart and intelligent
way.
• Cloud Computing: Cloud
computing can use in smart city
to provide pool of computing,
processing and storage resources
at any time and from anywhere.
• Big Data Analysis Solutions:
Analysis of large amount data
which are generated from sensors
and devices in smart cities is
very essential and important
for making better decisions and
more intelligent management of
services.
• City Management System (CMS): This system can help to automate,
manage, monitor and control
diff erent city administration tasks
for providing high quality services.
• Machine to Machine (M2M): There is a need to making
decisions automatically between
machines inside smart cities
through communicating to each
others in intelligent way. This will
make the cities smarter.
• Wireless Sensor Network (WSN): Sensors are considered as the core
part of smart cities. They used
for everything; wireless sensors
continuously sense and feed data.
• Shared Data: Data generated
from devices in smart cities will
be shared to among applications.
This will enhance and improve
the services through sharing and
exchange this data.
• Smart Mobile Applications: Smart mobile applications can
help to enable citizens for using
smart services that providing by
government in the smart cities.
The citizens can extract data from
infrastructures such as sensors
via these smart applications.
This can help to make decisions
automatically depend on the
extracted data.
• Service and Infrastructure Security: Security of services and
infrastructures of smart cities is
an important issue to ensure the
continuity of smart services for
people.
Cyber Security Issues for Smart Cities Cyber security concerns in protection
of systems from theft or damage as well
as from disruption or misdirection of the
services they providing. Cyber security in
smart cities used to secure and protect
of smart city infrastructures and services.
Higher degree of connectivity of services
has the possibility to open up new severe
cyber-attacks and crimes against critical
sectors in smart city so there is a serious
need for providing and creating secure,
reliable and resistant smart city is very
important issues for protecting people
life and guarantee continuity of providing
better and intelligent services. To satisfy
and achieve these issues, governments
have to take in considerations to design,
implement and provide smart services
with these considerations like robustness,
reliability, privacy, integrity and resilience,
in their mind. Smart city is dependent on
gathering and processing real time data
to increase service quality and effi ciency.
There are several important thresholds
must take in consideration to gain
maximum benefi ts from this data such as:
• Privacy: It is considered as an
aspect of information technology
that deals with the ability to
determine what data in digital
systems can be shared with
others so that these systems have
to be secure to protect privacy of
sensitive data for the users.
• Confi dentially: It means
preventing the sensitive
information from being taken by
wrong people through using fraud
authentication methods.
• Integrity: It means that data
cannot change by unauthorized
persons during transit of data.
• Availability: It means the
functionality of system working
properly and services in smart city
is an important issue for providing
services for people in 24 hours per
week.
These will help to make commercial
decisions with confi dence in addition to
control a physical environment in a safe and
reliable manner. There are many challenges Fig. 2: Smart City Technologies
S E C U R I T Y C O R N E R
CSI Communications | June 2016 | 36 www.csi-india.org
related to any new technology these
challenges can impact the services that will
provide by government and companies that
conduct business for customers and users.
There are many problems related to smart
city security such as:
• Encryption: In smart city most
devices work in wireless mode
which makes them easy to be
hacked by attackers due to
poor encrypted communication
system. Most devices in the
smart city are wireless which
can be attacked by malicious
users easily if there is no secure
communication channel.
Encryption is good solution to
secure this channel and reduce
risk of intercept and hijacking
techniques which can take full
control over machines and devices
inside smart cities.
• Distributed Denial of Service (DDoS): It is a kind of Denial of
Service (DoS) where multiple
compromised system, which can
be infected malicious software
such as Trojan horse.
• Lack of Computer Emergency Response Teams: Computer
Emergency Response Teams
(CERTs) for smart cities will help
manage and handle diff erent
security problems in effi cient and
eff ective way.
• Simple Bugs with Huge Impact: A simple bug in software programs
can make enormous impact so that
it is very important and essential
for taking in consideration when
designing and developing software
for critical services in smart cities.
There are a lot of devices running
software programs to perform its
function and operation normally.
If there vulnerabilities in the
infrastructures and services of
smart city, this may lead for many
problems.
ConclusionSmart city uses new and advanced
technologies to improve and control
services to making people life more
comfortable and better. Secure smart
city is an important issue to guarantee
the continuity of providing smart services
from illegal activities and attacks that can
make people life not safe so that smart
city security considered as health and
safety of smart city. This article discussed
services in smart city, technologies for
building of smart city and cyber security
issues in smart cities to help researchers
and developers to design and develop new
strategies and methods to secure services
and infrastructures of smart cities in
eff ective and effi cient manner.
References[1] Elmaghraby, Adel S, and Michael M
Losavio. “Cyber security challenges in
Smart Cities: Safety, security and privacy.”
Journal of advanced research 5.4 (2014):
491-497.
[2] Cesar Cerrudo,” An Emerging US (and
World) Threat: Cities Wide Open to Cyber
Attacks”,white paper, IOActive Labs,2015.
[3] http://www.maynoothuniversity.ie/
progcity/2015/12/how-vulnerable-
are-smart-cities-to-cyberattack/ [Last
accessed 15-04-2016].
[4] https://en.wikipedia.org/wiki/Smart_city
[Last accessed 15-04-2016].n
Mr. Ezz El-Din Hemdan is working towards his Ph.D. degree in Department of Computer Science, Mangalore University, Mangalore, India. His research area of interests includes: Virtualiza on, Cloud Compu ng, Digital Forensics, Cloud Forensics, Big Data Forensics, Internet of Things Forensics, Networks and Informa on Security and Data Hiding. He can be reached at [email protected].
Mr. Madhvaraj M Shetty has received his B.Sc. and M.Sc. degree in Computer Science from Mangalore University, in 2011 and 2013 respectively. Currently, he is working towards his Ph.D. degree in Department of Computer Science, Mangalore University, Mangalore, India. His research area of interest include: Computer Networks, Networks and Information Security, Big Data Security. He can be reached at.
Dr. Manjaiah D.H [LM00002429] is currently working as a Professor in Computer Science Department at Mangalore University. He holds more than 23 years of academic and Industry experience. His area of interests includes: Advanced Computer Networks, Cloud and Grid Computing, Mobile and Wireless Communication. He can be reached at [email protected] and [email protected].
Kind Attention: Prospective Contributors of CSI CommunicationsPlease note that Cover Themes for forthcoming issues are planned as follows:
• July 2016 - Robotics��• Aug 2016 - Virtual Reality• Sept 2016 - Medical Image Processing��• Oct 2016 - Bioinformatics
Articles may be submitted in the categories such as: Cover Story, Research Front, Technical Trends and Article. Please send your contributions before 20th June 2016 for June issue. The articles may be long (2500-3000 words maximum) or short (1000-1500 words) and authored in as original text. Plagiarism is strictly
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S E C U R I T Y C O R N E R
CSI Communications | June 2016 | 37
Pattern Recognition in Java using “ENCOG Machine Learning Framework”
Videndra Singh BhadouriaChief Technology Offi cer, MI Digital, Bhopal
Rajesh K. ShuklaHOD, CSE and Dean (R&D), Sagar Institute of Research and
Technology-Excellence, Bhopal
P R A C T I T I O N E R W O R K B E N C H
Encog is a freely available powerful machine learning
framework that supports a variety of machine learning
algorithms like Support Vector Machine, Artifi cial Neural
Networks, Bayesian Networks, Hidden Markov Models, Genetic
Programming and Genetic Algorithms. In this article, you will
learn how to use this library with Java for performing simple
pattern recognition task using ADALINE artifi cial neural network.
For compiling and executing this program, you need to download
ENCOG library from following URL:
http://www.java2s.com/Code/JarDownload/encog/encog-core-
3.1.0.jar.zip
Program// AdalineCharRecongnizeimport org.encog.Encog;import org.encog.ml.data.MLData;import org.encog.ml.data.MLDataSet;import org.encog.ml.data.basic.BasicMLData;import org.encog.ml.data.basic.BasicMLDataSet;import org.encog.ml.train.MLTrain;import org.encog.neural.networks.BasicNetwork;import org.encog.neural.networks.training.simple.TrainAdaline;import org.encog.neural.pattern.ADALINEPattern;
public class AdalineCharRecongnize {
public fi nal static int PatternWidth = 5; public fi nal static int PatternHeight = 5;
public static String[][] PATTERNS = { {“ O “,”O O”,”O O”,”OOOOO”,”O O”}, {“OOOO “,”O O”,”OOOO “,”O O”,”OOOO “}, {“ OOO”,”O “,”O “,”O “,” OOO”}};
public static MLDataSet prepareTrainingSet() { MLDataSet TrainingDataSet = new BasicMLDataSet(); for (int i = 0; i < PATTERNS.length; i++) { BasicMLData IdealResult = new BasicMLData(PATTERNS.length); MLData input = preprocessPattern(PATTERNS[i]);
for (int j = 0; j < PATTERNS.length; j++) { if (j == i) IdealResult.setData(j, 1); else IdealResult.setData(j, -1); } TrainingDataSet.add(input, IdealResult);
} return TrainingDataSet; } public static MLData preprocessPattern (String[] InputPattern) { MLData result = new BasicMLData (PatternWidth * PatternHeight);
for (int row = 0; row < PatternHeight; row++) { for (int col = 0; col < PatternWidth; col++) { int index = (row * PatternWidth) + col; char ch = InputPattern[row].charAt(col); result.setData(index, ch == ‘O’ ? 1 : -1); } } return result; } public static void performTraining(BasicNetwork ANN) { MLDataSet trainingSet = prepareTrainingSet(); MLTrain train = new TrainAdaline(ANN, trainingSet, 0.01); while (true) { train.iteration(); if (train.getError() <= 0.01) break; }System.out.println(“ANN Training completed !!”); } public static void performRecognition(BasicNetwork ANN) { char RecognizedCharacter = ‘ ‘; for (String[] MYPATTERNS : PATTERNS) { int output = ANN.winner(preprocessPattern(MYPATTERNS)); for (int j = 0; j < PatternHeight; j++) { System.out.println(MYPATTERNS[j]); } switch (output) { case 0: RecognizedCharacter = ‘A’; break; case 1: RecognizedCharacter = ‘B’; break; case 2: RecognizedCharacter = ‘C’; break; } System.out.println(“Above pattern is recognized as ‘” +
CSI Communications | June 2016 | 38 www.csi-india.org
C O V E R S T O R Y
RecognizedCharacter + “’\n”); } System.out.println(“Pattern recognition completed”); } public static void main(String args[]) { int NoOfInputNeurons = PatternWidth * PatternHeight; int NoOfOutputNeurons = PATTERNS.length; ADALINEPattern AdalinePattern = new ADALINEPattern(); AdalinePattern.setInputNeurons(NoOfInputNeurons); AdalinePattern.setOutputNeurons(NoOfOutputNeurons); BasicNetwork AdalineANN = (BasicNetwork) AdalinePattern.generate(); performTraining(AdalineANN); performRecognition(AdalineANN); Encog.getInstance().shutdown(); }}
ExplanationPatternWidth and PatternHeight are integer values that denote
the width and height of each input pattern present inside the
string array PATTERNS. Each character pattern is represented
as one dimensional character array within the two dimensional
character array PATTERNS. The goal of this program is to create
an ADALINE artificial neural network, train it to recognize
the patterns present in PATTERNS array, and then test the
network with same dataset. We have considered three different
patterns, one for each of the character A, B, and C to train and
test the network.
In supervised learning, ANN is presented with an input pattern
and its expected output. In Encog, input pattern and its expected
output are represented as an object of MLData and BasicMLData
class respectively. The prepare TrainingSet method returns a
TrainingDataSet, which is an object of MLDataSet containing
three diff erent sets of input pattern and its expected output.
TrainingDataSet serves the ADALINE as training data set with
which the network can be trained to recognize the patterns used
in this program. This function sets fi rst, second and third bit of
expected output to 1 for fi rst, second and third pattern respectively
leaving all the other bits to -1 Representation of training data set
can be depicted as shown below:
In MLDataSet, an input pattern must be represented as an object
of MLData that can contain either a value 1 or -1 at any index. It
can be imagined as a two dimensional array where 1 represents
yes and -1 represents no. For example, following fi gure shows how
character a pattern can be represented as MLData:
Note: A character pattern is represented as one dimensional
character array where as MLData can be thought of as an
equivalent formatted two dimensional array. The preprocessPattern
method takes input pattern as one dimensional array and returns
its equivalent MLData object.
The performTraining method trains the ADALINE using training
data set returned by prepareTrainingSet method until the
error becomes less than 0.01. Once the network is trained,
the performRecognition method is invoked which provides the
PATTERNS array to ANN as input and prints the recognized
character as output. The program exits after giving the
following output:
Representation of Training set in Encog Library
P R A C T I T I O N E R W O R K B E N C H
Mr. Videndra Singh Bhadouria is working as Chief Technology Officer, MI Digital Bhopal. His area of research is Cloud Computing, Web Mining, Adhoc mobile Network and Cellular Communication. He can be reached at [email protected].
Mr. Rajesh K Shukla [LM00155595], presently is HOD, CSE and Dean (R&D) at Sagar Ins tute of Research and Technology-Excellence, Bhopal. He has authored 8 books including Object Oriented Programming in C++, Data Structure using C and C++, Analysis and Design of Algorithms, Basic Computer Engineering with Wiley India; Theory of Computa on and Formal Languages and Automata Theory with Cengage Learning. He is presently vice president, CSI Bhopal Chapter. He can be reached at [email protected].
About the Authors
CSI Communications | June 2016 | 39
Crossword »Test your knowledge on Artifi cial IntelligenceSolution to the crossword with name of fi rst all correct solution provider(s) will appear in the next issue. Send your answer to CSI
Communications at email address [email protected] and cc to [email protected] with subject: Crossword Solution – CSIC
June Issue.
Solution to May 2016 Crossword
CLUESACROSS2. Formally naming and defi ning entities
3. A hypothesized unit of human knowledge
6. To cut off undesirable solutions
7. Specifi c area
9. A rule of thumb
10. A working piece of software which performs limited function
13. Study of forms
DOWN1. Ability to act independently
4. Basic size of a unit
5. A sequence of two words
8. A language used to represent knowledge
11. Omission of some words in an statement
12. Vague
We are overwhelmed by the response and solutions received
from our enthusiastic readers
Congratulations!All nearby Correct answers to May 2016 month’s crossword received from the following readers:
T Nishitha, Vasavi College of Engineering, Hyderabad
Dr. Sandhya Arora, Assistant Professor, Cummins College of Engineering for Women, Pune
Did you know?
RoBoHoN: The World’s First Humanoid Robot Smartphone
Japanese company Sharp began sales of the fi rst robot smartphone, RoBoHoN in May end 2016. The robot which operates with the
voice of the user has all the features of a smartphone. Apart from this, it dances and sings. It can be used as projector for presentation. It can answer quizzes, and when you praise, it raises hands to express his joy. Most importantly, it can recognise human faces , and talk with them by their names. It continuously learns, and as the time passes, it becomes more acquainted and useful to its user. Unfortunately, it is being sold in Japan only.Sources: www.japantoday.com
Rashid SheikhAssociate Professor, Sri Aurobindo Institute of Technology Indore
B R A I N T E A S E R
Dr. Durgesh Kumar Mishra, Chairman, CSI Division IV Communications, Professor (CSE) and Director Microsoft Innovation Center, Sri Aurobindo Institute of Technology, Indore
CSI Communications | June 2016 | 40 www.csi-india.org
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CSI Communications | June 2016 | 43
F R O M C H A P T E R S A N D D I V I S I O N S
Ahmedabad Chapter
CSI Ahmedabad chapter sponsored ‘World Telecom
& Information Society Day’ celebrations held on
May 17, 2016, organized by Institution of Engineers
(India) IEI, Gujarat, Ahmedabad Chapter at Bhaikaka Hall, Law
Garden, Ahmedabad. The Programme was supported by various
Technical/ Professional Associations of Gujarat Like, Association
of British Scholars (ABS), Broadcasting Engineers Society (BES),
Gujarat Innovation Society (GIS), IEEE Communication Society,
Association of Computing Machinery(ACM). The Theme of the
celebration was “ICT Entrepreneurship for Social Impact”.
The session started with welcome address by Dr. Manish
Doshi, Chairman Technician Chapter & Member ET, IE(I). He
welcomed the audience and informed all about the legacy
carried out by IE(I) to host this event every year successfully
along with other supporting processional society chapters
in Ahmedabad Gujarat(India). Dr. Jayesh Solanki, Chairman
CSI, Ahmedabad Chapter and Dr. Kishor Maradia, Chairman
IETE, Ahmadabad briefed about their activities and technical
contributions to the professional fraternities and students.
Dr. N. P. Gajjar Chairman IEEE ComSoc Gujarat Chapter introduced
all supporting organizations. The event was attended by more
than 160 members of participating organizations/ associations,
professionals, academicians and students.
Ghaziabad Chapter
CSI Ghaziabad Chapter organized 2nd CSI IT Excellence
Awards and Panel discussion’ at Ghaziabad.
Chief Guest of the evening was Mr. Tanmoy Chakrabarty
(Vice President and Global Head of Government ISU, TCS).
Mr. Anul Mandal (CTO – HT Media Ltd) was the Guest of Honour.
Mr. Grant Xia (MD – Cathay Communications, China) was the
keynote speaker. He delivered keynote address on the topic “Indo-
China Technology Exchange: Win-win situation”.
Dr. Vineet Kansal (Chairman, CSI Ghaziabad Chapter), Mr. Saurabh
Agrawal (Past Chairman) Mr. Anil Ji Garg (Vice Chairman), Dr. Arun
Sharma (Imm. Past Chairman) and Mr. Vikas Srivastava (Treasurer)
were present at the occasion.
Ghaziabad Chapter Newsletter was released by the Chief Guest
on this occasion.
A Panel discussion followed on the theme ‘START UP INDIA – Who
has to lead this revolution (Government, Corporates, Academia or
the Entrepreneur)?’ Panelists were Dr. Urvashi Makkar (Director
General – GL Bajaj Institute of Management & Research, G Noida),
Mr. J. P. Bhatt (Founder – ImpactQA), Mr. Sanjoy Bhattacharya
(Business Head – LG), Mr. Sumit Mohan Saxena (CEO – B2B
Alliances Pvt Ltd), Mr. Harinder Makkar (Director - Ministry of
Home Aff airs, Govt. of India). Discussion was moderated by
Col. Jitendra Minhas (CEO – STEP Business Incubator).
Ghaziabad Chapter Past Chairman Mr. Saurabh Agrawal shared
the need and idea behind IT Excellence Awards in Ghaziabad. IT
Excellence Awards were presented by Chief Guest as follows:
IT Start-up of the Year: Admito (www.admito.in) incubated by
IMT Ghaziabad
SRC Technosoft: Incubated by JSS Academy of Technical
Education
Outstanding Contribution to Wholesome Education: Dr. Urvashi
Makkar (Director General - G L Bajaj Institute of Management &
Research)
Outstanding Contribution to IT Entrepreneurship: Mr. J P Bhatt
(Founder & CEO of ImpactQA)
Outstanding Contribution to IT Awareness: Mr. Sanjoy
Bhattacharya (Business Head in LG Electronics for Monitors &
PCs)
Haridwar Chapter
CSI Haridwar Chapter in Association CSI Student Chapter
of COER Roorkee with has successfully organized the one
day workshop on “Cloud Computing and Its Applications”
on April 28, 2016 at COER, Roorkee
A total number of 240 students were registered in the workshop
from Computer Science & Engineering, Information Technology
and Electronics & Telecommunication Departments. The
workshop was comprised of two sessions. In fi rst session, lectures
CSI Communications | June 2016 | 44 www.csi-india.org
C O V E R S T O R Y
and hands on exercises were held on Cloud Computing by
Dr. Mayank Aggarwal and Mr.Mahendra Nath Dubey. Second
session was having two parallel events: Poster Presentation and
Android App Making Competition.
The event was inaugurated by Director General COER, Maj. Gen.
A.K. Chaturvedi (Rtd.), Director COER-SM, Dr. V.K. Jain, Dr. Mayank
Agarwal, Vice Chairman, CSI, Haridwar Chaoter, Mr. Mahendra
Nath Dube, Senior Manager, On Mobile Technologies,Bangalore
and Dr. B.M. Singh. Dr. B.M. Singh welcomed all participants and
discussed the benefi ts of the event and brief about the CSI. Maj.
Gen. A.K. Chaturvedi addressed Industry Academia Gap and How
to fi ll those gaps. Dr. V. K. Jain focused on role of computer society
of India in technical education. Dr. Mayank Agarwal addressed
participants about the usage and applications of the workshop.
Mr. Dube addressed computing challenges.
The technical experts werer Dr. Mayank Agarwal, Head, CSE
Department, GKU Haridwar and Mr. Mahendra Nath Dube.
Dr. Aggarwal told the students about the Concepts of Cloud
Computing and also tarined them on IBM Cloud Bluemix.
Mr.Dubey told about the evaluation of costs for cloud computing.
The event was coordinated by Dr. B.M. Singh, HOD-IT,
Dr. Himanshu Chauhan, HOD-CSE, Mr. B.D. Patel, HOD-ET.
Ms. Supriya Shukla was the co-convener of this event.
Dr. Devendra Kumar, Mr. Taresh Singh, Mr. Neeraj Pandey,
Ms. Nilima, Mr. Pranav Bansal, Ms. Nidhi Agarwal, Mr. Dhaneshwer
Kumar, Mr. Sohan Lal, Mr. Vineet Kumar, Mr. Ashutosh Shukla,
Bhupal Arya, Isha Vats, Bharti Sharma, Manish Pant and others
did very commendable work in making this event successful.
Jabalpur Chapter
CSI Jabalpur Chapter organized an expert talk on
topic “Optimization Techniques for Engineering and
management Problems” by Dr. Sunil Agrawal from
IIITDM, Jabalpur. The talk was organized at Gyan Ganga Institute
of Technology and Sciences, Jabalpur on 27th May 2016 from
3:00 PM.
He was welcomed by Dr. Maneesh Choubey, Chairman, CSI
Jabalpur Chapter. The welcome was followed by his session.
He briefed about the Optimization techniques available giving
example about each. He discussed about mathematical modelling,
how to solve mathematical problems (using time and cost as
constraint) and gave a brief about numerical optimization.
He was felicitated at the end of session by Shri Rajneet Jain
Secretary Gyan Ganga Group. The distinguished members
present during the session were Shri I.S. Ruprah, Vice Chairman-
CSI Jabalpur Chapter, Shri Jitendra Kulkarni – Treasurer, Dr. Vinod
Kapse, Dr. R.K. Ranjan, Dr. Neeraj Shukla, Prof. Ashok Verma, Prof.
Ajay Lala, Prof. Meghna Utmal, Prof. Ashish Mishra, etc.
The session was attended by students of UG, PG and faculty
members. CSI Jabalpur Chapter members also witnessed the
session.
Dr. Santosh Vishwakarma, Secretary, CSI Jabalpur Chapter
thanked all the distinguished members of CSI Jabalpur Chapter for
their presence. He also thanked Aishwarya Soni, Shankar Gupta
and other student members for their special contribution to make
the program a success.
Mumbai Chapter
CSI Mumbai Chapter conducted Two days Workshop on
Software Eff ort Estimation - Function Point Analysis and
its Applications, Based on latest release 4.3.2 during
14-15 April 2016.
Workshop was conducted by Prof. V. K. Garg, gold medalist
bachelor of engineering and M. Tech from IIT Delhi.
The objective of the workshop was to impart skills in Software
Eff ort Estimation using “Function Point Analysis” as per revised
computing manual 4.3.2. Participants were able to learn the
conceptual base and put the same in practice using near real life
case studies.
CSI Mumbai Chapter conducted Two days hands on Workshop on
Wireless Security during 15-16 April 2016.
This Workshop addressed multiple security problems with
wireless networks and methods how real world hacker’s use break
into diff erent wireless infrastructures. Participants were able to
take preventive measures to protect wireless networks from such
intruders, and also share some best practices a user should follow
to avoid such attacks.
CSI Mumbai Chapter conducted One day Workshop on Project
Risk Management on 29 April 2016.
Workshop was conducted by Prof. V. K. Garg, gold medalist B.E. and
M. Tech from IIT Delhi. This course included the risk management
practices, techniques and tools drawing on current international
F R O M C H A P T E R S A N D D I V I S I O N S
CSI Communications | June 2016 | 45
best practices to help project manager deal eff ectively with risks.
The objective of the workshop was to allow a project manager to put
solid project management best practices in place in order to achieve
project objectives of schedule, cost and scope.
Participants were able to eff ectively manage uncertainties and
achieve your project objectives.
CSI Mumbai Chapter conducted Knowledge Forum Session on
IoT and Analytics for startups – Discussion by IBM team on 2
April 2016.
The Session was held at Tilak Bhavan, University of Mumbai,
Kalina Campus, Santacruz East, Mumbai. The session started
with the presentation and talk by Mr. Radhesh K, Country Lead
IBM Entrepreneur Program followed by presentation by Mangesh
Patankar, IBM technology Specialist on Internet of Things. In this
Session, the IBM team discussed its AI technology Watson, its
strategy on IoT and how it will encourage and work with startups.
They explained their entrepreneur program in detail related to
startups. The audience understand that the areas like renewable
energy, healthcare - the two technologies are deployed together
to provide better services to customers.
CSI Mumbai Chapter conducted SPIN Session – An evening with
CMMI Institute on 26th April 2016.
On the evening of 26th April, J.P Naik Bhavan of Mumbai
University was abuzz with the presence of intellectuals from the
industry to interact with the Senior Management Team of CMMI
Institute comprising of Mr. Kirk Botula, CEO – CMMI Institute,
Ms. Katie Tarara, Partner Relationship Manager – CMMI Institute
and Mr. Douglas Grindstaff , Business Development Specialist –
CMMI Institute.
The event was organized by Computer Society of India (CSI)
under their SPIN program Under the leadership and guidance of
Prof. Dr. Sureshchandra J. Gupta, Hon. Head and immediate Past
Chairman of CSI Mumbai Chapter, and Ex head of Dept. of Physics.
Mr. Hitesh Sanghavi, Convener CSI – SPIN Mumbai, MD – CUNIX
and CMMI – HMLA welcomed the guests and introduced them
to the audience.
Recent acquisition of CMMI Institute by ISACA was centre point
of discussion. In his presentation Mr. Kirk Botula elucidated his
vision behind this acquisition and the road ahead for CMMI
Institute and convinced the audience regarding the synergy which
propelled this acquisition and the benefi ts CMMI Institute is
going to derive from it. It was followed by a more than an hour of
Q&A session with wide variety of questions ranging from making
CMMI model leaner to the future of PCMM, from presence of
CMMI Institute in India to making CMMI cost eff ective for small
business and from co-relation of CMMI with other models to new
model of CMMI for security.
The interaction ended with a vote of thanks from Mr. Hitesh
Sanghavi and presentation of memento to the esteemed guests.
The interaction was enlightening and candid. The audience
derived the benefi t of understanding the perspective regarding
future of CMMI directly from the Senior Management Team of
CMMI Institute.
Mysuru Chapter
A talk was organized by CSI- Mysuru Chapter on “SAP in
Today’s market” in association with Centre for Information
Science and Technology ( CIST), UoM on 30th April
2016. Mr. Rajesh Kutnikar, CEO, IT Champs SAP Solutions spoke
on the technical aspects of SAP, how the modules are organized,
functionality covered by various modules. He also spoke about
the large fi rms that IT Champs works with and the demand that
exists for qualifi ed SAP consultants with certifi cation. Members
of CSI – Rampur Srinath, Aruna Devi, Veerander Kumar, CIST
members – Santhosh Kumar, Rashmi, IT Champs members
from Mysuru and Bengaluru and student members from various
Engineering colleges and other post graduate departments of
University of Mysore were present.
Nashik Chapter
Computer Society of India, Nashik Chapter; on the
occasion on inception of CSI celebrated Information
Technology Day 2016 on 24th March 2016 at Institute
of Engineer’s Ashoka Virtue Hall, Untawadi Nashik. Shri Prasad
Deore, regional head NASSCOM was chief guest & Shri.
Narendra Goliya, chairman Rishabh Instruments, Nashik was
guest of honour. Dr. Shirish Sane, regional vice president CSI
was also present.
Prof. Prashant Patil welcomed the gathering & requested industry
and academia to synergize the eff orts together to achieve vision of
CSI. Dr. Shirish Sane in his address shared various initiatives of CSI
at national level and list of activities done by region VI in last year.
Hon. secretary Sandeep Karkhanis presented the audience various
activities & initiatives of the chapter. Organizing regional level and
state level events, visit of members of executive committee; etc.
CSI Communications | June 2016 | 46 www.csi-india.org
Special edition of newsletter ACCESS was released by the guest
on the occasion. On the occasion, Shri Narendra Goliya insisted
on “Innovate India – Incubate India – Make in India”.
Academic excellence awards to 11 students from chapter region,
IT excellence award to Shri Shashank Todwal of UMS Tech Labs
& Hrushikesh Wakadkar of eLuminous Technologies Pvt Ltd
were given in the hands of Shri Goliya. Past chairmen of Nashik
chapter were felicitated with “Certifi cate of Recognition” towards
their eff orts, hard work, vision & vigour in development of Nashik
Chapter.
Dr. Arati Dixit, Asst. Professor of Savitribai Phule Pune University;
was awarded with prestigious “Yashokirtee Puraskar” instituted by
chapter patron Shri. Avinash Shirode in the name of his mother Late
Sou. Shevantabai Shirode. Dr. Arati humbly accepted the award
along with her parents and husband; and expressed gratitude to
all those who supported her for what she is today. She appealed
the students to be determined to excel. Chief guest Prasad Deore,
regional head NASSCOM; recognized the initiatives of Computer
Society of India and eff orts by nashik chapter to achieve the vision:
‘IT for masses’. He shared how economy is growing and the role of
IT in all sections of life. Taking inspiration from the IT experts and
infrastructure facilities of the city, expressed that Nashik is well
place to become IT: Next destination.
Hon. Secretary Sandeep Karkhanis proposed vote of thanks.
CSI Nashik Chapter on the occasion of Chapter foundation day
organized Program on Virtual Reality on 27th April, 2016. Mr.
Diwakar Yawalkar, Chairman CSI Nashik Chapter welcomed
Mr. Manas Gajare, Founder & CEO, Zabuza Labs. Mr. Manas
Gajare (Guest Speaker) explained concept of Virtual Reality with
demo of VR devices like Google Cardboard & Oculus Rift in this
session. The program was attended by delegates from various IT
companies and Academicians.
CSI Nashik Chapter organized a program on Virtualization and
End User Computing on 13th May, 2016 powered by VMware.
Program was very useful to understand virtualization and
end user computing, its benefi ts / advantages and how as an
organization we can tap the full potential of virtualization and End
user Computing, to secure applications and data and apply the
zero-trust concept.
The program was attended by CEO, IT/EUC individuals, C-level
strategists, CIO, CISO, IT Heads from various organizations.
Vellore Chapter
CSI Vellore Chapter organized a one day online seminar on
“An Overview of Requirements Engineering” on 27-04-
2016 from TCS Research Labs from Pune. Ms. Preethu
Rose Anish covered Introduction to software engineering, life
cycle and diff erent techniques for requirements gathering
and tools for the same, around 60 members attended the
event organized by Prof. G. Jagadeesh, Prof. K.Govinda and
Prof.H.R.Viswakarma.
F R O M C H A P T E R S A N D D I V I S I O N S
Inauguration of Student Branch at Gaya College, Bihar
The CSI Students Branch at Gaya College, Gaya was inaugurated on Tuesday,
3rd May 2016 by CSI-National Secretary Prof. A. K. Nayak, in the auspicious presence
of Chairman- CSI Varanasi Chapter Dr. Sunil Kr Pandey and Bihar State CSI student
coordinator Prof. Sams Raja.
The inaugural session was followed by one day seminar on “Digital India: Prospects
and Challenges”. The session was preside over by the Principal, Gay College
Prof. (Dr.) M. Shamsul Islam & the welcome address was delivered by head
department of Computer Applications & Math, Prof. RKP Yadav. Mr. Satyendra Kumar
has been elected student coordinator of CSI - Gaya College Students Branch.
CSI Communications | June 2016 | 47
F R O M S T U D E N T B R A N C H E S
REGION - IIIGYAN GANGA INSTITUTE OF TECHNOLOGY AND SCIENCES, JABALPUR THE LNM INSTITUTE OF INFORMATION TECHNOLOGY, JAIPUR
12-4-2016 – during motivational talk by Mr. Prashant Dubey, Director for
inclusion of new student members
10-4-2016 - Prof. Bipin Mehta, Immediate Past President, CSI with students
during Felicitation Ceremony
REGION - IIIPOORNIMA COLLEGE OF ENGINEERING, JAIPUR POORNIMA COLLEGE OF ENGINEERING, JAIPUR
14 & 15-3-2016 – during Two Days workshop on Web Designing 11-5-2016 - during One Day workshop on Microsoft Azure Cloud
Computing Platform and IoT
REGION-IV REGION-VFAKIR MOHAN UNIVERSITY, BALASORE JSS ACADEMY OF TECHNICAL EDUCATION, BENGALURU
9-4-2016 – during Seminar-cum-Training programme on Mobile
Application Development
27-4-2016 - Lighting the Lamp on the occasion of CSI Student Branch
inauguration
REGION-VGSSS INSTITUTE OF ENGINEERING AND TECHNOLOGY FOR WOMEN, MYSURU GSSS INSTITUTE OF ENGINEERING AND TECHNOLOGY FOR WOMEN, MYSURU
12-4-2016 - Smt Ayesha Taranum, Dr. Reshma Banu, Mr. Sachin Kumar,
Dr. Dayananda & Mr. Mahesh during a talk on IBM Bluemix
23-4-2016 - Ms. Shamila, Technology Lead, HP, Bengaluru during a
technical seminar on Application of Data Structure related with industry
CSI Communications | June 2016 | 48 www.csi-india.org
C O V E R S T O R Y
REGION-VDR. K V SUBBA REDDY COLLEGE OF ENGINEERING FOR WOMEN, KURNOOL DR. K V SUBBA REDDY COLLEGE OF ENGINEERING FOR WOMEN, KURNOOL
5 & 6-2-2016 – during two days workshop on Programming Skills 6-3-2016 – during CSI Day Celebrations
REGION-VKLE COLLEGE OF ENGINEERING AND TECHNOLOGY, CHIKODI RAJARAJESWARI COLLEGE OF ENGINEERING, BENGALURU
27 & 28-4-2016 – during Technical Event 11 to 13-5-2016 – Inauguration of International Conference on Innovations
in Computing and Networking 2016
REGION-VSTANLEY COLLEGE OF ENGINEERING & TECHNOLOGY FOR WOMEN, HYDERABAD VASAVI COLLEGE OF ENGINEERING, HYDERABAD
5 to 7-5-2016 during three days Workshop on Object Oriented Programming
through Java
11 & 12-4-2016 – during two days National Conference on Emerging &
Innovative Trends in Computer Science (NCEITCS-2016)
REGION-VI REGION-VIIPUNE INSTITUTE OF COMPUTER TECHNOLOGY, PUNE KINGS ENGINEERING COLLEGE, CHENNAI
30-3-2016 – during Student Branch Inauguration and Orientation 9-4-2016 – during International Conference on Innovations and
Challenges in Engineering and Technology
F R O M S T U D E N T B R A N C H E S
CSI Communications | June 2016 | 49
REGION-VIIVIT UNIVERSITY, VELLORE VIT UNIVERSITY, VELLORE
23 & 24-4-2016 – during an annual event on Devfest 7-5-2016 – during Python programming
Prof. Hari Mohan Gupta received B.E. (Electronics and Communications) from University of Roorkee (Now IIT, Roorkee) in 1967, M.Tech (Electronics and Electrical Communications) from IIT, Kharagpur in 1969, and Ph.D. (Communications and Information Systems Major) from IIT, Kanpur in 1974.
He joined the faculty of Electrical Engineering at IIT, Delhi in 1973 where he became a Professor in 1986. At IIT Delhi, he was instrumental in establishing the fi rst industrially sponsored initiative, viz. Bharti School of Telecommunication Technology and Management, as its founding coordinator ( Head). He had been the Head of the Department and the Dean (UGS) at IIT,
Delhi. Prof. Gupta has a wide international exposure. He held faculty appointments at McGill University, Montreal, Canada, Drexel University, Philadelphia, USA. He has been Visiting Professor at University of Maryland, College Park, USA, Massachusetts Institute of Technology, Cambridge, USA, Swiss Federal Institute of Technology (EPFL), Laussane, Switzerland, Helsinki University of Technology, Helsinki, Finland and many European universities. He published close to 170 technical papers in reputed international and national journals and conferences. He successfully completed several sponsored R&D Projects and was consultant to several Government and private sector organizations such as Power Grid Corporation, TCIL, DRDO.
Prof. Gupta has been a Senior Member of the CSI for the last two decades. He has represented the CSI in TC 6 (Communication Systems) and TC 13 (Entertainment Computing) of the International Federation for Information Processing (IFIP). He was Chairman, Data Communication Division during 1999- 2001. He has organized CSI Seminars at Delhi and has chaired several technical sessions in CSI Seminars and Conferences, including CSI Convention.
He was conferred “Eminent Engineer” recognition by Institution of Engineers, Delhi State Centre in September 2012. He was recognized with “Distinguished Professional Engineer” award at 13th National Convention of Computer Engineers at Roorkee, Dec.1998. He delivered plenary lecture on Energy Effi cient Wireless Network Protocols at International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2008) held at Edinburgh, UK, on June 16-18, 2008.
In grateful recognition of his services to the Computer Soceity of India (CSI), and his outstanding accomplishments as an IT professional, the CSI has decided to name him FELLOW of the society. The society takes pride and pleasure in presenting nd him with this citation on the occasion of its Golden Jubilee Annual Convention held at New Delhi on 02 December, 2015.
Prof. Hari Mohan Gupta
Fellow Award
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ri Mohan Gupta
[The citation of Prof. H. M. Gupta was incorrectly printed in CSIC Feb. hard copy. The error is regretted..... Editor]
Memorandum of Understandingbetween Computer Society of India and Springer Nature
valid upto 31st December 2020Requirements :
• Formulate strong Technical and Advisory Committees comprising of national and international experts (from renowned Universities/corporates
of repute) in the focus area of proposed conferences
• Build communities around conferences
• Defi ne steps to check plagiarism
• Focus on stringent peer-review process involving all the members mentioned in the Committees and by allowing suffi cient time for review
Interested Conference organizers can contact:
Ms. Suvira Srivastav, Associate Editorial Director, Computer Science & Publishing Development
Springer India, 7th Floor, Vijaya Buiding, Barakhamba Road, New Delhi, India.
Ph: +91-11-45755884, Email: [email protected];
CSI Communications | June 2016 | 50 www.csi-india.org
Rules / Procedure for Approval of Technical Collaborations, for Technical Events Organized by the Non-CSI Entities like Organizations / Institutions / Universities, etc., by CSI Chapters / Regions / Divisions,
without any Financial liability to CSI
Technical sponsorship / collaborations to good quality technical events, without any fi nancial liability, subject to the
following conditions, can be approved, on case to case basis:-
1. The concerned Organization / Institution must be a valid Institutional member of Computer Society of India (CSI). If they are obtaining fresh membership, they should be encouraged to take membership for longer dura-tion like 10 / 20 years.
2. As part of this Technical Sponsorship, at-least one Life Member or 05 individual annual professional members must be generated, out of this event. For this, a copy of CSI Life Membership Form should be distributed, in the registration kit, to all the non-CSI Member delegates and arrangements should be made to collect the fi lled in membership form, payment details (Bank counter folio after depositing the payment in the bank or cheque, payable at par, in favour of Computer Society of India) of the interested delegates, on the spot. This can be done though keeping a counter of CSI having copies of CSI forms and other related information through a person deputed there by the organizers, on the venue of the event.
3. In order to justify the CSI Technical Sponsorship and also to motivate the delegates / participants to obtain the CSI Membership, delegates / participants must be given at-least 20% discount in registration fee, to existing CSI Members or would be CSI members (if they deposit the fee and CSI membership form on the spot).
4. If the Institution does not have the CSI Students’ Branch, at-least after the event is over, they should work hard to establish the Students’ Branch. This will be a compulsory condition for their 2nd event to be approved for technical sponsorship.
5. Quality of papers, technical materials and publications should be of high standard and be checked thoroughly by Turnitin or any other licensed antiplagiarism / cross check / similarity index softwares to avoid embarrass-ment to the society, at later stage. Open source softwares, for antiplagiarism checking, are not recommended, as their database is very limited and the reports are not authentic.
6. OBs and few related ExecCom members, with the consent of the sponsoring heads, be involved in the Advi-sory Committee or Steering Committee of the event.
7. Two delegates, based on the recommendation of the sponsoring / collaborating head, be given complimentary
registration. They will be monitoring the execution / conduct of the event and submit a brief report, after the
event, to the respective sponsoring / collaborating head.
8. After the event is over, a DVD having copies of the related presentations / papers / other technical materials be submitted to CSI for uploading them on CSI Digital Library (DL).
9. After the event is over, a post event report with few good quality photographs having CSI logo be submitted to the CSI HQ for its record and publication in CSI Communications.
10. The event must be planned in advance and be included, through the sponsoring / collaborating head, in the event calendar published in the CSI Communications.
11. The CSI logo, as available at CSI website www.csi-india.org and also available on the header line of this document be included at prominent places of all the fl yers, backdrops, banners, publications, and other printed materials, under the head; Technical Sponsor, if there is only one sponsor, otherwise, as Technical Co-Sponsor.
A proposal giving details of the programme may be submitted to corresponding chapter/ region/division, at-least
06 months in advance.
Computer Society of India
CSI Communications | June 2016 | 51
C S I C A L E N D A R 2 0 1 6
Sanjay Mohapatra, Vice President, CSI & Chairman, Conf. Committee, Email: [email protected]
Date Event Details & Contact Information
01-02 July 2016 Second Intl. Conference on Information and Communication Technology for Sustainable Development (ICT4SD 2016) at Hotel Vivanta, Goa www.ict4sd.in/2016 Contact : [email protected], Mr. Amit Joshi 09904632888
22-23 July 2016 4th International Conference on Innovations in Computer Science & Engineering Venue: Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Hyderabad, Website: www.icicse2016.orgContact : Dr. H. S. Saini, [email protected], Dr. D. D. Sarma, [email protected] [email protected], [email protected]
18-19 August 2016 International Conference on “Internet of Things”, Venue : APS College of Engineering, Bangalore Contact : [email protected]
16-17 Sept. 2016 International Conference on “Computational Systems and Information Technology for Sustainable Solution [CSITSS-2016]”Organized by CSE & ISE & MCA - R.V. College of Engineering, Bengaluru -560059. www.rvce.edu.in; Contact : fi [email protected]
6-8 Oct. 2016 2016 International Conference on Frontiers of Intelligent Computing: Theory and applications (FICTA), KIIT University, Bhubneswar. www.fi cta.in Contact : [email protected]; Ph: 080-67178183, 8180;
28-29 Oct. 2016 Third International Conference on Computer & Communication Technologies (IC3T - 2016) at Devineni Venkata Ramana & Dr. Hima Sekhar MIC College of Technology, Vijayawada, Andhra Pradesh, India. http://www.ic3t.mictech.ac.in/ Contact : Dr. S.C. Satapathy, 9000249712, [email protected], Dr. K. Srujan Raju, 91-9246874862 , [email protected] Prof. Vikrant Bhateja, 91-9935483537, [email protected]
11-12 Nov. 2016 International Conference on Advances in Computing and Data Sciences (ICACDS-2016). Proceedings by Springer CCIS/LNCS (Approval in Process) Organized by Krishna Engineering College (KEC), Ghaziabad. http://icacds2016.krishnacollege.ac.in/ Contact : Dr. Mayank Singh, [email protected]. Mob: 09540201130
22-25 Nov. 2016 Special session on “Smart and Ubiquitous Computing for Vehicle Navigation Systems” at IEEE TENCON 2016, Marina Bay Sands, Singapore (http://site.tencon2016.focalevents.sg/)Contact : Dr. P.K. Gupta [email protected], Prof. Dr. S. K. Singh [email protected]
8-10 Dec. 2016 CSI Annual Convention (CSI-2016): Theme: Digital Connectivity - Social Impact; Organized by CSI Coimbatore Chapter; Pre-Conference Tutorial on 7th Dec 2016 Venue: Hotel Le Meridien Contact : Dr. Ranga Rajagopal, Convener, 9442631004 [email protected]
CeBIT INDIA 2016 – Global Event for Digital Business in association with CSI Venue: BIEC, Bengaluru www.cebit-india.comContact : Mohammed Farooq, [email protected], +91 9004691833
23-24 Dec. 2016 8th Annual IEEE International Conference on Computational Intelligence and Communication Network CICN-2016. Venue : Gyan Ganga Institute of Technology & Sciences, Jabalpur Contact : Dr. Santosh Vishwakarma [email protected]
11-12 Feb. 2017 International conference on Data Engineering and Applications-2017 (IDEA-17) at Bhopal (M.P.), http://www.ideaconference.in Contact : [email protected]
Registered with Registrar of News Papers for India - RNI 31668/1978 If undelivered return to : Regd. No. MCN/222/20l5-2017 Samruddhi Venture Park, Unit No.3, Posting Date: 10 & 11 every month. Posted at Patrika Channel Mumbai-I 4th fl oor, MIDC, Marol, Andheri (E). Mumbai-400 093 Date of Publication: 10th of every month
- COMPUTATIONAL INTELLIGENCE
- IT FOR SOCIETY
- SOFTWARE ENGINEERING
- NETWORK, COMPUTING &
INFORMATION SCIENCE
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Inviting papers in
emerging areas:
Important Dates- Submission of Manuscript 30th July 2016- Acceptance notification 31st Aug 2016- Camera ready paper 15th Sep 2016
Authors are invited to submit their original and unpublished work in the areas including but not limited to these areas.
INSPIRE. INNOVATE. MAKE A DIFFERENCE.
51st Annual Convention of Computer Society of IndiaDigital Connectivity – Social Impact
Technology - more specifically Digital Connectivity permeate all aspects of daily life. Social media and emerging mobile technologies have forever changed the landscape of human interaction. A person's digital presence is regarded as his digital interactions, and traces through a multitude of online platforms and media. It is not difficult to see that with Digital Connectivity transforming our experience with the world, our world will function quite differently 10 - 15 years from now.
Hence the theme of the convention aims to draw the attention of academician, corporates, researchers, government and every stakeholder to help society navigate the impacts of the shifts to come. It aims to tap into talent and the passion of people who are already working on innovative solutions to various issues. Its objective is to bring out state of the art solutions to challenges related to Digital Connectivity that can...
Impact the economyImpact Life style of each citizenand ensure we build societies that are Happy Societies to live in, in a Digitally Connected World!!
Conference Highlights• CSI is one of the largest forums to present research papers• Selected papers to be published in Springer CCIS series• High profile speakers from various industries and institutes of repute• Numerous networking opportunities to convert your ideas into reality
Visit www.csi-2016.org for more details and tosubmit paperE : [email protected]
Contact : Third floor, Vyshnav Building,95A, Race course,Coimbatore 641018.P : +91 422 2200695E : [email protected]
Scan to visit website: www.CSI-2016.org
DIGITAL CONNECTIVITY – SOCIAL IMPACT
CALL FOR PAPERS!