Qualification: 
M.E, BE
t_sasikala@blr.amrita.edu

Sasikala T. is serving as Assistant Professor (Sr. Gr.) in the Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru. Currently she is pursuing Ph.D. in the area of Sentiment Analysis using Machine Learning Techniques.

EDUCATION

  • 2003: B. E. (CSE)
    Mahendra Engineering College, Periyar University
  • 2009: M. E. (CSE)
    Kongu Engineering College, Anna University, Chennai
     

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

G. B. R., Dr. Deepa Gupta, and Sasikala T, “Grammar Error Detection Tool for Medical Transcription using Stop Words – POS Tags ngram Based Model ”, in 2nd International Conference on Computational Intelligence and Informatics(ICCI’17), JNTU, Hyderabad , 2017.[Abstract]


Medical transcription is the process of conversion of audio files, dictated by medical experts, to electronic
data files in a predetermined format. The doctor ‘s thoughts are documented, covering medical procedures carried
out on a patient starting from the time the patient enters the clinic or hospital, up until the ailment is treated.
A grammar checker is an important asset to hospitals to scrutinize medical transcripts. The transcripts are important
to track a patient’s medical history and need to be error free. The available existing tools are specifically designed to
detect faulty grammatical constructs in the generic English language. It is important to improve the intelligence of a
grammar checker in a relatively unknown domain and to improve the level of accuracy set by the existing tools which
mostly rely on a set of non-exhaustive rulesets. These are the driving factors to propose a new approach to an old
problem. Stop words are most commonly occurring words in any language. By exploiting the fact that stop words
form the backbone of a sentence and by figuring out the common parts-of-speech tags which surround them,
a sentence’s grammatical structure can be better understood using statistical methods.

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2017

Conference Paper

Sasikala T and Krishna, K. Sandeep, “Prognostication of Students Performance and Suggesting Suitable Learning Style for Under Performing Students ”, in 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru , 2017.

2017

Conference Paper

A. Aki, D, K. Mohan Redd, Y, K. Reddy, R., K. C., and Sasikala T, “Analyzing the real time electricity data using data mining techniques”, in International Conference On Smart Technologies For Smart Nation (SmartTechCon2017), Reva University, Bengaluru, 2017.

2017

Conference Paper

A. P. Valli, .Uma, M., S.Pravallika, K. R., and Sasikala T, “Tracing out various diseases by analyzing twitter data applying data mining techniques”, in International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS 2017) , Chennai, 2017.

Publication Type: Journal Article

Year of Publication Publication Type Title

2015

Journal Article

Sasikala T and K, R., “Matrix Approach of Apriori Algorithm using Subset Count Technique”, International Journal of Applied Engineering Research, vol. 10, 12 vol., pp. 32151-32159, 2015.[Abstract]


In present days, since the size of the datasets is being increased day by day, extracting knowledge from this huge data set has become a very big issue. Particularly speaking about transactional data set, there were many approaches to find the frequent item sets. Many classical algorithms and techniques of Apriori have been proposed and used to find the frequent item sets from the huge transactional dataset. The classical algorithms scans the database repeatedly for finding frequent item sets which generally takes more execution time. In this paper we propose a new improved matrix approach with subset count to find the frequent item sets. First the given data is converted to matrix form where the items with less than minimum support count and duplicate transactions are removed, secondly each transactions are scanned for item sets and if this item set is not present in frequent item sets list the we find the subsets of this item set and add them to subset count list by incrementing the count of a particular subset. If the count of any subset is greater than minimum support then the subset is added to frequent item sets list. In this paper we have compared the existing apriori algorithm with improved matrix approach based on execution time.

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2015

Journal Article

Sasikala T, K, A. N., and R, G., “Software Based Prototype for Data Confidentiality in Databases”, International Journal of Applied Engineering Research, vol. 10, no. 14, pp. 34369-34371, 2015.[Abstract]


In recent times huge amount of data is getting generated and is stored in the databases. These databases are either outsourced or maintained by the companies. The data stored can be highly confidential, but there is no guarantee whether that data is secure or not [4][6]. The databases can be attacked and sensitive information can be leaked. So to provide security for data in databases we came up with a software prototype which is cost effective, efficient and ensures high data security. This prototype encrypts the data before storing in the database with a secret using strong cryptographic algorithms and hence ensures data security.

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