R.Lakshmi

@klnce.edu

Associate Professor / Department of Computer Science and Engineering
K.L.N. College of Engineering

https://researchid.co/lakshmi

EDUCATION

Ph.D.,

RESEARCH INTERESTS

Information Retrieval, Algorithms, Data Mining and Agriculture

Scopus Publications

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

Efficient text document clustering with new similarity measures
R. Lakshmi and S. Baskar
Inderscience Publishers

Novel term weighting schemes for document representation based on ranking of terms and Fuzzy logic with semantic relationship of terms
R. Lakshmi and S. Baskar
Elsevier BV
Abstract Weighting and normalization are the most important factor that may affect the text representation significantly. This paper presents two novel term weighting schemes to represent text documents, namely, i). Term-weighting scheme for document representation based on Term Frequency - Ranking of Term Frequency (TF-RTF) and ii). Term-weighting scheme for document representation based on Term Frequency - Ranking of fuzzy logic with semantic relationship of terms (TF-RFST). The ranking of each term in a document provides its priority of the document and uses these priorities for document representation in TF-RTF. In TF-RFST, each term is represented based on its frequency and the frequency of semantic related terms for that term. Hence, the ranking of each term is based on the combined frequencies of the term and its semantic related terms with a specific weighting scheme. With appropriate weighting schemes such as TF-RFT and TF-RFST, the proposed methods provide better clustering performance in terms of accuracy, entropy, recall and F-Measure than previously suggested methods, such as word count, Term Frequency-Inverse Document Frequency (TF-IDF), Term Frequency-Inverse Corpus Frequency (TF-ICF), Multi Aspect TF (MATF), BM25 and BM25F. Experiments carried out on the Reuters-8, Reuters-52 and WebKB data sets with K-means and K-means++ clustering algorithms for demonstrate the effectiveness of the proposed term weighting schemes.

DIC-DOC-K-means: Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means for improving the effectiveness of text document clustering
R Lakshmi and S Baskar
SAGE Publications
In this article, a new initial centroid selection for a K-means document clustering algorithm, namely, Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means (DIC-DOC- K-means), to improve the performance of text document clustering is proposed. The first centroid is the document having the minimum standard deviation of its term frequency. Each of the other subsequent centroids is selected based on the dissimilarities of the previously selected centroids. For comparing the performance of the proposed DIC-DOC- K-means algorithm, the results of the K-means, K-means++ and weighted average of terms-based initial centroid selection + K-means (Weight_Avg_Initials + K-means) clustering algorithms are considered. The results show that the proposed DIC-DOC- K-means algorithm performs significantly better than the K-means, K-means++ and Weight_Avg_Initials+ K-means clustering algorithms for Reuters-21578 and WebKB with respect to purity, entropy and F-measure for most of the cluster sizes. The cluster sizes used for Reuters-8 are 8, 16, 24 and 32 and those for WebKB are 4, 8, 12 and 16. The results of the proposed DIC-DOC- K-means give a better performance for the number of clusters that are equal to the number of classes in the data set.

Intelligent crash detection and emergency communication system for two wheelers
Rayasam Lakshmi Satya, R. Kaviya, and R. Valarmathi
IEEE
An Effective Vehicular on board accident detection system aimed at helping the injured on accident of the two-wheeler leading to a near fatal accident. The accident is detected and if fatal. the location of the accident is triangulated and emergency services are alerted either as a normal message or as an app notification. The usage of cloud technology provides further application as it can be used to identify accident hot spots by identifying it using machine learning.

Analysis of sentiment in twitter using logistic regression

Effective lung cancer diagnosis: A survey
K. Prabhavathi and R. Lakshmi
Indian Society for Education and Environment
Background/Objectives: Lung cancer is one of the mostly deadliest cancers across the world. Various approaches have been used for diagnosis of lung cancer. This paper surveys various approaches used for lung cancer diagnosis. Methods/Statistical Analysis: This paper classifies techniques in the following ways, 1) Data mining approach, 2) Medical approach, 3) Biophotonic imaging approach. Also discusses the various pros and cons of these approaches. Findings: This paper surveys the different approaches used for lung cancer diagnosis. Improvements/Applications: It provides the efficient way for early detection of lung cancer. It reduces the death rate and increases the survival rate.

An implementation of clustering project proposals on ontology based text mining approach
T. Preethi and R. Lakshmi
IEEE
The NSFC is the largest government funding agency in China, with the primary aim to fund and manage basic research. The agency is made up of seven scientific departments, four bureaus, one general office, and three associated units. The scientific departments are the decision-making units responsible for funding recommendations and management of funded projects. Selection of research projects is an important and recurring activity in many organizations such as government research funding agencies. Current method of grouping proposals are based on manual matching of similar research discipline areas but it fails to be accurate. Text clustering methods those are not having semantic approach provide less accuracy. A novel ontology based text mining approach to cluster proposals is proposed. Research project selection is an important task for government and private research funding agencies. When a large number of research proposals are received, it is common to group them according to their similarities in research disciplines. The grouped proposals are then assigned to the appropriate experts for peer review. The review results are collected, and the proposals are then ranked based on the aggregation of the experts' review results.

RECENT SCHOLAR PUBLICATIONS

LEVERAGING MACHINE LEARNING AND NLP FOR PERSONALIZED MENTAL HEALTH ANALYSIS FROM SOCIAL MEDIA INSIGHTS
DR Lakshmi, SR Sree, BJAR Priya, RP Priyanka
International Research Journal of Engineering and Technology 11 (10), 761-765 2024

A Novel Method for Plant Disease Detection using CNN
R Lakshmi, AC Jenifer, JPE Bharathi
International Journal of Creative Research Thoughts 12 (11), 603-610 2024

Classification Of Student Academic Performance Using Machine Learning Algorithms
L R, G JJ, D VS
QIS Science Journal 11 (4), 680 - 683 2024

AN AUTOMATED SUBJECTIVE E-EVALUATION APPROACH USING ARTIFICIAL INTELLIGENCE
R Lakshmi, K Usharani, P Divya Bharathi, R Priyanka @ Poornima
Journal of Emerging Technologies and Innovative Research (JETIR) 11 (3 2024

EFFICIENT TEXT DOCUMENT CLUSTERING WITH NEW SIMILARITY MEASURES
R Lakshmi, S Baskar
International Journal of Business Intelligence and Data Mining 18 (1), 49-72 2021

Novel term weighting schemes for document representation based on ranking of terms and Fuzzy logic with semantic relationship of terms
R Lakshmi, S Baskar
Expert Systems with Applications 137, 493-503 2019

Enhanced partition aware engine for efficient load balancing computing using queue model
A Saranya, R Lakshmi
Advances in Natural and Applied Sciences 10 (10 SE), pp. 200 – 204 2016

Fp-growth association rule mining based lung cancer identification in real time database
K Prabhavathi, R Lakshmi
Advances in Natural and Applied Sciences 10 (10 SE), 249+ 2016

Effective Lung Cancer Diagnosis: A survey
K Prabhavathi, R Lakshmi
Indian Journal of Science and Technology 9 (8), 1-5 2016

A Secure Decision Making Process in Health Care System Using Naive Bayes Classifier
KMR Malini, R Lakshmi
INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING AND TECHNOLOGY 4 (1), 46-49 2015

A SECURE DECISION SUPPORT ESTIMATION USING GAUSSIAN BAYES CLASSIFICATION IN HEALTH CARE SERVICES
KMR Malini, R Lakshmi
International Journal of Emerging Technology in Computer Science 2015

Face Annotation with Weakly Labeled Web Facial Images
S Swathika, R Lakshmi, S Sindu Priyadharshini
International Journal of Advance Research and Innovation 3 (1), 242 - 244 2015

An implementation of clustering project proposals on ontology based text mining approach
T Preethi, R Lakshmi
International Conference on Information Communication and Embedded Systems 2013

MOST CITED SCHOLAR PUBLICATIONS

EFFICIENT TEXT DOCUMENT CLUSTERING WITH NEW SIMILARITY MEASURES
R Lakshmi, S Baskar
International Journal of Business Intelligence and Data Mining 18 (1), 49-72 2021
Citations: 22

An implementation of clustering project proposals on ontology based text mining approach
T Preethi, R Lakshmi
International Conference on Information Communication and Embedded Systems 2013
Citations: 9

Effective Lung Cancer Diagnosis: A survey
K Prabhavathi, R Lakshmi
Indian Journal of Science and Technology 9 (8), 1-5 2016
Citations: 2