CENTRE FOR WEB SERVICES AND DEVELOPMENT

(SONA WEB)

TEAM MEMBERS

Centre Head: 

Dr. J.Akilandeswari,  Professor and Head /IT 

Team Members :       

Dr. V. Mohanraj, Prof./IT

Dr. J. Jeba Emilyn, Asso.Prof./IT

Mr. J. Dhayanithi, AP/CSE

Ms. Anitha Elavarasi, AP/CSE

Mr. U.K. Balaji Saravanan, AP/IT

Ms. G. Jothi, Research Associate/IT

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OBJECTIVE

The centre addresses the challenges of research problems such as

summarizing and analyzing the sentiment of the vast amounts of user-

generated content

Analyzing the information in social networking websites 

Online recommendation system

Ontology Ranking

Dynamic Web service composition

Dynamic data fusion of heterogeneous attributes

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I. Mining Frequent Patterns without Tree Generation

The frequent patterns are generated without any candidate set or tree

construction.

This methodology encodes the database using numerical approach and

reduces the size of the encoded database to a great extent. .

This algorithm leads to the reduction in both space and time complexity.

Journal Publication

Shanthi K.V, Akilandeswari J and Jothi G, “Mining Frequent Patterns

Without Tree Generation”, International Journal of Data Mining,

Modelling and Management.(Paper Accepted)

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Mining Frequent Patterns without Tree Generation

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II. Sentiment Analysis In Twitter Using Scoring

Model Twitter is one of the most fashionable microblogging that permits users to

express their views on sports, politics, modern technologies, movies and

spirituality and so on.

It is hard to place the sentiment polarity of the tweets. In this paper new

scoring methodology to find the sentiment polarity of the twitter messages

is proposed.

Emotions and shortened words are integrated to increase the significance of

the proposed scoring technique.

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II. Sentiment Analysis In Twitter Using Scoring

Model The following work has been done

We are now monitoring food-price related tweets from January 2015 around 5000 tweets and

analyze the impact of food price crises.

Statistical scoring model is used to classify the relevant tweets, depending on the sentiment

they express (i. e. “positive tweet” “negative tweet” and “neutral tweet”). Sample of tweets

was then used to train to classify the tweets in the correct category and identify the sentiment

of new tweets.

Data mining techniques are used to extract the relevant keywords/ features related to food

price crises. From the twitter conversations, these keywords/features are analyzed on how to

correlate the impact of food price crises – developing an efficient new algorithm for

clustering the features

Journal Publication

Akilandeswari J and Jothi G, “Sentiment Classification of Tweets using a Scoring Model

Incorporating Language & Non-language Features”, Applied for International Journal of

Information Retrieval Research.

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DATA ACQUISITION

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EXPERIMENTAL RESULTS

The overall score of the tweet is greater than zero, the tweets is classified as a positive tweet, less than zero then classified as a negative tweet and closer to zero means neutral tweet. Some sample tweets with the sentiment orientation is presented in the Table 1.

Table 1. Sample tweets and Sentiment Polarity

Sl. No. Tweet Score Sentiment Polarity

1 Election this could get nasty -0.2000 Negative

2 I never reject this work 0.2200 Positive

3 The book is gud 0.1667 Positive

4 RT annual Spring Game is set for Saturday April at pm

ET in Commonwealth Stadium0.0100 Neutral

5 With no respect for Indian cricket should be barred

from Villiers should go from IPL or be thrown out-0.0111 Negative

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III. Elimination of Redundant Association Rules – An Efficient Linear Approach

Association rule mining plays an important role in data mining and knowledge

discovery.

Traditional association rule mining algorithms generate lots of rules based on the

support and confidence values, many such rules thus generated are redundant.

The eminence of the information is affected by the redundant association rules.

The proposed algorithm removes redundant association rules to improve the quality

of the rules and decreases the size of the rule list.

It also reduces memory consumption for further processing of association rules.

Publication

J. Akilandeswari and G. Jothi, “Elimination of Redundant Association Rules – An

Efficient Linear Approach”, submitted the paper to the International Conference on

Computational Intelligence, Cyber Security and Computational Models

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CONSULTANCY

Consultancy – SES – Smart Evaluation System – e-learning environment – IIT,

Bombay

Website upgradation for JSW Salem Works

Website upgradation for Co-Efficient Consulting Management, Netherlands

Web Portal construction for Gujarati Samaj, Coimbatore

Web site Construction for Special school for children (SMILE), Salem.

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PATENT AND PUBLICATIONS

Patent:

"A Method for Securing a Protocol" Application No: 3822/CHE/2014

Book Publications:

Released Second Edition of "Web Technology- A Developer's Perspective" during July 2014 by Prentice Hall of India

No. of International Journal Publications :

No. of National Journal Publications :

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RECOMMENDATION SYSTEM

Objective

- Capture the user intent and recommend the web pages the contains user expected information.

- An important challenge of such system must include a need of being self-adaptive because the needs of online user may change dynamically and also design an accurate classifier for improving the accuracy of recommendation system.

Publication: “Ontology Driven Bee’s Foraging Approach based Self Adaptive Online Recommendation System”, Journal of Systems and Software, Elsevier, Vol. 85, No.11, pp. 2439-2450, 2012. Impact Factor: 1.352 and 5 – Year Impact Factor: 1.485

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Current Progress in Recommendation System

• Personalized Recommendation using CF and CBF methods in conjunction with Social Networks Data and Geo-tag. Handling cold start problem in CF based Recommendation system.

- Received the consent for INDIA-TAIWAN project for the year • 2017 from• Prasan Kumar Sahoo, PhD(CSE), PhD(Math), MTech(IIT, Kgp) 

Director, International Academic Affairs Center  Dept. of Computer Science and Information Engineering,  Chang Gung University 259, Wen-Hwa 1st Road, Guie-Shan, 3302, TAIWAN

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DATA MINING IN BIOLOGICAL DATA

Objective

Design a rough set based Bi clustering algorithm for efficiently finding useful pattern in gene expression.

Publication:

• “Community Detection And Identifying Leaders And Followers In Online Social Networks”,  International Journal Of Scientific & Engineering Research, V0l-6, Apr-2015

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CONTACT 

Contact Details:

Dr. J. Akilandeswari,

Professor and Head,

Department of Information Technology,

Sona College of Technology,

Salem.

Ph : 0427 4099755

Mobile: 9894777003

Email: akilandeswari@sonatech.ac.in