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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

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Mr. Shiv Kumar Mr. Devaprasanna Sinha Dr. Vipin Tyagi National Informatics Centre 73B, Ekdalia Road, Jaypee University of

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Region - VII

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SAIT, Indore Email : [email protected] Email : [email protected]


ContentsVolume No. 40 • Issue No. 3 • June 2016

CSI Communications

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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

at [email protected].

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 -

[email protected].

With kind regards

Sanjay MohapatraVice President, CSI

Mr. Sanjay Mohapatra, Bhubaneswar, [email protected]


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.


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]


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


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


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


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.


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


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


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)


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”.


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


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



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%


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


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


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


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



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


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



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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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


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


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


[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].


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


• 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


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


• Organizational Decision Support System– this type


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


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].


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


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


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



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


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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(Issued on the behalf of Editorial Board CSI Communications)

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Chief Editor



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 ‘” +


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


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


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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


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



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.


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.


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.


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


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


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];


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


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

1234

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!