Qualification: 
MBA, MPhil, MSc
asmahesh@asas.kh.amrita.edu

A. S. Mahesh currently serves as Assistant Professor in the Department of Computer Science and I.T., School of Arts & Sciences, Amrita Vishwa Vidyapeetham, Kochi. 

Qualification: M. Sc. (CS), M.B.A. (Systems and Marketing), M.Phil. (CS), PGDCA, ‘O’ Level

Publications

Publication Type: Journal Article

Year of Publication Title

2019

Aiswarya Vijayakumar and A. S. Mahesh, “Quality Assessment of Ground Water in Pre and Post-Monsoon Using Various Classification Technique”, International Journal of Recent Technology and Engineering (IJRTE) , vol. 8, no. 2, 2019.[Abstract]


Quality assessment of water is one of the basic points which have pulled in a lot of thought in the progressing years. Diverse kinds of classification system are most convenient for the examination in this field of study. The present examination investigates the quality of ground water in Agastheeswaram which is located in Tamilnadu. Totally 138 water samples was accumulated in the midst of pre-monsoon (PRM) and post-monsoon (PSM) from the year of 2011 to 2012.The water quality (WQ) evaluation was carried out by assessing chemical parameters for both the seasons. This paper explores various classifier models such as DT, KNN and SVM to achieve prediction of groundwater quality. The classification is done based on the WQI of each sample. A near investigation of characterization systems was done dependent on the confusion matrix, accuracy, f1 score, precision and recall. The outcomes propose that SVM is a better method having high accuracy rate than other models..

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2019

Aiswarya Vijayakumar and A. S. Mahesh, “Quality Assessment of Ground Water on Small Dataset”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 5, 2019.[Abstract]


Quality assessment of water has a lot of attractions during recent years. Diverse kinds of classification and monitoring techniques were used in this field of study. The present examination investigates the quality of ground water in Kudankulam which is situated Tirunelveli district of Tamil Nadu. A total of 19 samples was accumulated in this region typically from the coastal area during 2011-2012.The evaluation was done on the basis chemical parameters of each samples. This paper explores various classifier models such as KNN, NB and SVM to achieve prediction of groundwater quality. The classification is done based on the Water Quality Index (WQI) of each sample. A near investigation of characterization systems was done dependent on the confusion matrix, accuracy, f1 score, precision and recall. The outcomes propose that SVM is a better method having high accuracy rate than other models.

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