Dr.SATYAVATHY G

EDUCATION

M.C.A.,M..,

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Science Applications, Information Systems, Software

Scopus Publications

A Study on the Impact of Social Media and Cyberbullying on Teen Girls in India
M. P. Swapna and G. Satyavathy
Chapman and Hall/CRC



DEEC-Based Meta-heuristic Unequal Clustering Scheme for Energy Sustenance of Heterogeneous Nodes in WSN
M. P. Swapna and G. Satyavathy
Springer Nature Singapore



Aquila Optimized Localization of Mobile nodes in Heterogeneous WSN with Reduced Complexity using MCL Square
Swapna M P and Satyavathy G
Seventh Sense Research Group Journals



Energy-Aware Optimal Clustering and Secure Routing Protocol for Heterogeneous Wireless Sensor Network
Swapna M P and G. Satyavathy
EverScience Publications
Wireless Sensor Network (WSN) is a collection of low energy sensor nodes deployed in hostile complex environments. Their functionality gathers requisite data from the environment and transmits it to the base station for further processing. To enhance the performance of WSN, sensor nodes with different energy levels, capabilities and functionalities are deployed, leading to Heterogeneous WSN (HWSN). The initial energy, energy consumption rate, and residual energy differ for each node in a heterogeneous WSN. Many algorithms were proposed to accomplish an energy-efficient steady HWSN, but the performance level is not satisfactory. This paper presents a novel integrated approach, Energy-Aware Optimal Clustering & Securing Routing (EAOCSR). The algorithm amalgamated three techniques optimal clustering, reliable routing and secured transmission, considering energy retention and network lifetime as the vital parameters. Unequal clustering scheme, trust-based reliable and secure routing forms the core of EAOCSR. The performance of EAOCSR is analyzed using MATLAB simulations. It reveals that the proposed routing protocol EAOCSR has superior performance to existing protocols regarding energy utilization, throughput, network lifetime, stability and security. Index Terms – HWSN, Unequal Clustering, Trust, Blockchain, Stability, Security.


Grey level co-occurrence matrix (GLCM) and multi-scale non-negative sparse coding for classification of medical images
P. Dhanalakshmi and Dr.G. Satyavathy
Institute of Advanced Scientific Research



Efficient noise removal based relevant vector machine to detect lung disease
P. Dhanalakshmi and G. Satyavathy
American Scientific Publishers
The quality of images is decreased by noises. There exist several chances for the noises to occur while capturing and transmission the image. Noise removal becomes a thrust area of research in image processing. The outcome of the noise removal shows the quality of digital image processing techniques. Noises in image lead to the semantic gap problem in medical image processing. Semantic gap problem becomes a serious issue in the classification of the medical image. With the aim to overcome this issue, this research work proposes an efficient noise removal method based on relevant vector machine. Instead of using unsuited linear filters to detect noises, this research work uses the nonlinear filters which suit well to detect noises in multiple scale layers. The proposed method is applied to ADL dataset for the detection of lung cancer. The results clearly show that the proposed noise removal based relevant vector machine performs better in terms of accuracy.

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