Mariappan A K
@srmeaswari.ac.in
Professor in Information and Technology
Easwari Engineering College
RESEARCH INTERESTS
Data mining, Search engines and NLP
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
A. K. Mariappan and J. Austin Ben Das
IEEE
Rice is the staple food of Tamil Nadu from time immemorial. Tamil Nadu produces the most varieties of Rice. Water for the agriculture purpose of Tamil Nadu is dependent on the river basins and the seasonal rainfall for its accomplishment. The major sources of irrigation are through the 17 major river basins spread throughout Tamil Nadu. The river basin data abets in agricultural rotation planning and crop decision. The yield outcome of the crop is dependent on various factors such as soil, climate, fertiliser and irrigation that are essential to agriculture. The rice production and its yield in Tamil Nadu is studied and a paradigm that pilots the prediction of rice yield based on the parameters that affects the rice yield is framed. This paradigm can enable the policy makers to make fact based decisions.
R. Reeta and A.K. Mariappan
IEEE
The main concept of dynamic reconfiguration is to make a system to change from one configuration to another during run time, without shutting down and rebooting the system. This is very helpful to avoid downtime during software maintenance. Providing various quality of services (QoS) namely, adaptability, availability and maintainability are the major challenges faced during dynamic reconfiguration for component based systems. This work argues that the main benefit to make dynamic reconfiguration for component systems is to minimize application disruption. A quantitative evaluation for QoS assurance to the proposed work is conducted in two steps. First, reconfigurable component model is implemented using reconfiguration strategies. Second, each reconfiguration strategy is tested using reconfiguration benchmark results and then, testing results are evaluated. Thus the QoS characteristics can be achieved using some constraints under some acceptable environment.
Hridya Sobhanam and A. K. Mariappan
IEEE
Number of people who uses internet and websites for various purposes is increasing at an astonishing rate. More and more people rely on online sites for purchasing rented movies, songs, apparels, books etc. The competition between numbers of sites forced the web site owners to provide personalized services to their customers. So the recommender systems came into existence. Recommender systems are active information filtering systems and that attempt to present to the user, information items in which the user is interested in. The websites implement recommender systems using collaborative filtering, content based or hybrid approaches. The recommender systems also suffer from issues like cold start, sparsity and over specialization. Cold start problem is that the recommenders cannot draw inferences for users or items for which it does not have sufficient information. This paper attempts to propose a solution to the cold start problem by combining association rules and clustering technique.