S. Murugavalli

11 papers Journal 11
YearRankTypeTitle / Venue / Authors
2025 J jnl
Novel hybrid optimization based adaptive deep convolution neural network approach for human activity recognition system.
Multim. Tools Appl.
M. Ashwin, D. Jagadeesan, M. Raman Kumar, S. Murugavalli, A. Chaitanya Krishna, Veeraswamy Ammisetty
2025 J jnl
Sign language video to text conversion via optimised LSTM with improved motion estimation.
J. Exp. Theor. Artif. Intell.
Subburaj S, S. Murugavalli, B. Muthusenthil
2024 J jnl
IFATA-Deep net: Improved invasive feedback artificial tree algorithm with deep quantum neural network for root disease classification.
Concurr. Comput. Pract. Exp.
C. Jackulin, S. Murugavalli, K. Valarmathi
2023 J jnl
Adaptive neuro-fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs.
IET Commun.
V. Thiruppathy Kesavan, S. Murugavalli, Premkumar Manoharan, Shitharth Selvarajan
2023 J jnl
RIFATA: Remora improved invasive feedback artificial tree algorithm-enabled hybrid deep learning approach for root disease classification.
Biomed. Signal Process. Control.
C. Jackulin, S. Murugavalli, K. Valarmathi
2022 J jnl
Autonomous detection of malevolent nodes using secure heterogeneous cluster protocol.
Comput. Electr. Eng.
C. Kotteeswaran, Indrajit Patra, Regonda Nagaraju, D. Sungeetha, Bapayya Naidu Kommula, Yousef Methkal Abd Algani, S. Murugavalli, B. Kiran Bala
2021 J jnl
Numerical Analysis of Thermal Management of On-Chip Circuits.
Wirel. Pers. Commun.
B. R. Ramesh Bapu, S. Kayalvizhi, S. Murugavalli
2017 J jnl
Privacy preservation and protection for cluster based geographic routing protocol in MANET.
Wirel. Networks
Balasubramanian Muthusenthil, S. Murugavalli
2016 J jnl
Brain tumour classification using two-tier classifier with adaptive segmentation technique.
IET Comput. Vis.
V. Anitha, S. Murugavalli
2014 J jnl
Brain tumor Classification based on Clustered discrete cosine transform in Compressed Domain.
J. Comput. Sci.
V. Anitha, S. Murugavalli
2014 J jnl
Image Retrieval using certain Block based difference of inverse Probability and certain Block based variation of Local Correlation coefficients Integrated with wavelet moments.
J. Comput. Sci.
B. Thenkalvi, S. Murugavalli