Kavi Priya S
@mepcoeng.ac.in
Associate Professor, Computer Science and Engineering
Mepco Schlenk Engineering College
EDUCATION
M.E(CSE), PH.D
RESEARCH INTERESTS
Sensor Networks, Data Science
FUTURE PROJECTS
IoT Bra to sense breast cancer at earliest stage
Applications Invited
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Kavi Priya S. and Pon Karthika K.
Elsevier BV
S. Kavi Priya and K. Pon Karthika
Abstract Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.
S. Kavi Priya and N. Saranya
Computers, Materials and Continua (Tech Science Press)
S. Kavi Priya and K. Pon Karthika
Computers, Materials and Continua (Tech Science Press)
S. Kavi Priya and N. Saranya
Informa UK Limited
S. Kavi Priya, G. Shenbagalakshmi, and T. Revathi
Springer Science and Business Media LLC
S. Kavi Priya, S. Naveen Kumar, K. Sathish Kumar, and S. Manikandan
Springer International Publishing
J. Relin Francis Raj, K. Vijayalakshmi, and S. Kavi Priya
Elsevier BV
S. Kavi Priya, G. Shenbagalakshmi, and T. Revathi
IEEE
With the recent advancement in the communication technologies, the real time in-pipe water quality monitoring system is gaining more importance. This work describes the recent development in the field of in-pipe real time contamination detection system. In addition, a contamination detection system is developed based on the emerging Internet of Things technology. The system samples the water at regular time intervals supplied through pipelines to the consumers/public. The real time data are analyzed using fuzzy synthetic evaluation and uploaded over the internet/cloud. When contamination is detected in the water, the system sends an alarm/alert to the consumers regarding the water quality parameters and prevents the further flow of water in the contaminated region in the pipe using a solenoid valve. The other region which supplies quality water to the consumers in the water distribution network remains flowing. The results demonstrate that the developed system is capable of analyzing the water quality parameters in real time and can successfully process, transmit data to the cloud and intimate the users about the contamination in the particular region.
Kavi Priya S., Revathi T., and Muneeswaran K.
Elsevier BV
S. Kavi Priya, T. Revathi, K. Muneeswaran, and K. Vijayalakshmi
Elsevier BV
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Received and completed a project titled "Automation of Hygienic Drinking Water Supply System using Wireless Sensor Networks (AHDWS)" funded by DST(WTI) as Principal Investigator during 31/12/2014 to 31/03/2018 for , 32708
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