Dr. R.BABY MUNIRATHINAM
@mallareddyecw.com
ASSOCIATE PROFESSOR, COMPUTER SCIENCE AND ENGINEERING
MALLA REDDY ENGINEERING COLLEGE FOR WOMEN
I AM MUNIRATHINAM. I HAVE COMPLETED M.C.A, M..
I HAVE COMPLETED MCA, AND M.PHIL IN MADURAI KAMARAJ UNIVERSITY AND COMPLETED PH.D IN ANNA UNIVERSITY, CHENNAI.
I AM HAVING EXPERIEENCE OF 18.5 YEARS AS AND ASSOCIATE PROFESSOR
I AM HAVING INDUSTRY EXPERIENCE OF 5 YEARS AS COMPUTER FACULTY .
EDUCATION
M.C.A., M..
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science Applications, Computer Science, Computer Science, Computer Networks and Communications
Scopus Publications
Scopus Publications
M. S. Sassirekha and S. Vijayalakshmi
Informa UK Limited
Graduate students are unaware of their final qualification for a course. Even though there were many models available, few works with feature selection and prediction with no control over the number of features to be used. As a result of the lack of an improved performance forecasting system, students are only qualified on the second or third attempt. A warning system in place could help the students reduce their arrear count. All students undertaking higher education should obtain the qualification at their desired level of education without delay to transit to their careers on time. Therefore, there should be a predictive system for students to warn during the course work period and guide them to qualify in a first attempt itself. Although so many factors were present that affected the qualifying score, here proposed a feature selection technique that selects a minimal number of well-playing features. Also proposed a model Supervised Learning Approach to unfold Student’s Academic Future Progression through Supervised Learning Approach for Student’s Academic Future Progression (SLASAFP) algorithm that recommends the best fitting machine learning algorithm based on the features dynamically. It has proven with comparable predictive accuracy.
V. Resmi and S. Vijayalakshmi
World Scientific Pub Co Pte Lt
In the current world, the software cost estimation problem has been resolved using various newly developed methods. Significantly, the software cost estimation problems can be dealt with effectively with the recently grown recurrent neural network (RNN) than the other newly developed methods. In this paper, an improved approach is proposed to software cost estimation using Output layer self-connection recurrent neural networks (OLSRNN) with kernel fuzzy c-means clustering (KFCM). The proposed OLSRNN method follows the basics of traditional RNN models for integrating self-connections to the output layer; thereby, the output temporal dependencies are better captured. Also, the performance of neural networks is improved using the kernel fuzzy clustering algorithm to enhance software estimation results. Ultimately, five publicly available software cost estimation datasets are adapted to verify the efficacy of the proposed KFCM-OLSRNN method using the validation metrics such as MdMRE, PRED (0.25) and MMRE. The experimental results proved the efficiency of the proposed method for solving the software cost estimation problem.
V Resmi and S Vijayalakshmi
Walter de Gruyter GmbH
Abstract In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of some soft computing techniques which rely on historical effort estimation data of the successfully completed projects to estimate the effort. So in a thorough study to improve the accuracy, models are generated for the clusters of the datasets with the confidence that data within the cluster have similar properties. This paper aims mainly on the analysis of some of the techniques to improve the effort prediction accuracy. Here the research starts with analyzing the correlation coefficient of the selected datasets. Then the process moves through the analysis of classification accuracy, clustering accuracy, mean magnitude of relative error and prediction accuracy based on some machine learning methods. Finally, a bio-inspired firefly algorithm with fuzzy analogy is applied on the datasets to produce good estimation accuracy.
V. Resmi, S. Vijayalakshmi, and R. Subash Chandrabose
Springer Science and Business Media LLC
Subramanian Sabitha Malli, Soundararajan Vijayalakshmi, and Venkataraman Balaji
Springer International Publishing
V. Balaji, P. Venkumar, M. S. Sabitha, S. Vijayalakshmi, and R. M. Rathikaa Sre
Springer International Publishing
Sabitha Malli Subramanian, S. Vijayalakshmi, Balaji Venkataraman, P. Venkumar, and R. M. Rathikaa Sre
Springer International Publishing
K. Palaniammal, M. Indra Devi, and S. Vijayalakshmi
IEEE
Semantic web is the technology which drives the syntactic search and there are a wide variety of applications available for tourism sector today which promotes the country's economic status. This paper concerned with the development of a model towards the semantic search and the result which is based on user's priority while searching the tourism domain of interest. From this proposed model, the conditional probability for the given input can be calculated and querying ontology to provide relevant information. This proposed model has been developed with use of Netica-J. The ontology is being created with Protégé which is the tool used as an ontology editor and the SPARQL is used for querying the ontology. The interface between the ontology and SPARQL is being made with the help of Jena.
S. Vijayalakshmi, V. Mohan, M. S. Sassirekha, and O. R. Deepika
IEEE
Abstract-Finding Frequent Sequential Pattern (FSP) is an important problem in web usage mining. In this paper, we systematically explore a pattern-growth approach for efficient mining of sequential patterns in large sequence database. The approaches adopts a (divide and conquer) pattern-growth principle as follows: Sequence databases are recursively projected into a set of smaller projected databases based on the current sequential pattern(s), and sequential patterns are grown in each projected databases by exploring only locally frequent fragments. Our proposed method combines tree projection and prefix growth features from pattern-growth category with position coded feature from early-pruning category, all of these features are key characteristics of their respective categories, so we consider our proposed method as a pattern growth / early-pruning hybrid algorithm that considerably reduces execution time. These approaches were implemented in hybrid concrete method using algorithms of sequential pattern mining.
Vijayalakshmi
Science Publications
Problem statement: To find frequently occurring Sequential patterns from web log file on the basis of minimum support provided. We introduced an efficient strategy for discovering Web usage mining is the application of sequential pattern mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Approach: The approaches adopt a divide-and conquer pattern-growth principle. Our proposed method combined tree projection and prefix growth features from pattern-growth category with position coded feature from early-pruning category, all of these features are key characteristics of their respective categories, so we consider our proposed method as a pattern growth, early-pruning hybrid algorithm. Results: Our proposed Hybrid algorithm eliminated the need to store numerous intermediate WAP trees during mining. Since only the original tree was stored, it drastically cuts off huge memory access costs, which may include disk I/O cost in a virtual memory environment, especially when mining very long sequences with millions of records. Conclusion: An attempt had been made to our approach for improving efficiency. Our proposed method totally eliminates reconstructions of intermediate WAP-trees during mining and considerably reduces execution time.
S. Vijayalakshmi and S. Suresh Raja
Springer Berlin Heidelberg
RECENT SCHOLAR PUBLICATIONS
Publications
1. R. Baby Munirathinam & Dr. S.Vijayalakshmi, 2018, “Packet Hiding Methods for Preventing Selective Jamming Attacks” which is published in the Journal of Web Engineering, , No.6 (2018) pages 2806-2821. ISSN No. 1540-9589. ©Journal of Web Engineering, publisher (Rinton Press –Publisher in Science and Technology) Indexed in 2018 (Anna University – Anna University List of Journals)
2. R. Baby Munirathinam & Dr. S. Vijayalakshmi, 2018, “Enhancing The Quality of Services For Manets By Using Aeaack” is published in Taga Journal of Graphic Technology ISSN: 1748-0345 (Online) in Volume 14, pages 523-536 (2018) © Taga Journal of Graphic Technology – Publisher (Swansea Printing Technology Limited ) (Anna University – Updated list of Journals in Anna University). 192
3. R. Baby Munirathinam & Dr. S. Vijayalakshmi, 2016, “Watch Dog timer (WDTN) based Sensor node- Master Operations in wireless Sensor Networks (WSN)” published in Asian Journal of Research in Social Sciences and Humanities, Vol 6, No.9 Sept 2016, pages 2176- 2190 having ISSN 2249-7315. Publisher – Asian Research Consortium (Anna University - Updated Journal of Annexure I).
4. R. Baby Munirathinam & Dr. S. Vijayalakshmi, 2016, Scheduling in Autonomous Mobile Mesh Networks” having published in Advances in Natural and Applied Sciences, AmericanEurasian Network for Scientific Information - (AENSI-Publications), EISSN: 1998-1090, http:// / ANAS 2016 Special 10(10)
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
5. R. Baby Munirathinam & Dr. S. Vijayalakshmi, 2016, “Improving Energy Efficient using Novel Sleep Scheduling Approach for Wireless Adhoc-networks” published in IEEE Digital Library having ISBN. Information: DOI” 10/1109/ICCSP. 2016. Pages (2059-2065) Publisher: IEEE Digital Library.
INDUSTRY EXPERIENCE
I HAVE WROKED AS COMPUTER FACULTY IN VARIOUS COMPUTER CENTERS(1.1.2001 TO 25.8.2006)