Srihari Varma Mantena

Work place: Department of Computer Science and Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram 534204, India



Research Interests: Computer Networks, Data Mining, Image Processing


Srihari Varma Mantena received his M.Tech in IT from Andhra University and PhD in CSE from Acharya Nagarjuna University, Guntur. He has around 10 years of teaching experience. He works as Associate Professor in the Department of Computer Science and Engineering, SRKR Engineering College, Bhimavaram, India. He has published 7 papers in refereed journals and conference proceedings. His research interests include Data Mining, Computer Networks and Security, Image Processing and Internet of Things.

Author Articles
Evaluation of Tennis Teaching Effect Using Optimized DL Model with Cloud Computing System

By Sai Srinivas Vellela M Venkateswara Rao Srihari Varma Mantena M V Jagannatha Reddy Ramesh Vatambeti Syed Ziaur Rahman

DOI:, Pub. Date: 8 Apr. 2024

Evidence from psychology and behaviour therapy shows that engaging in sports activities at home might help alleviate stress and depression during COVID-19 lockdown periods. A clever virtual coach that provides table tennis instruction at a low cost without invading privacy might be a great way to maintain a healthy lifestyle without leaving the house. In this article, we look at creating the second main constituent of the virtual-coach table tennis shadow-play training scheme: an evaluation system for the effectiveness of the forehand stroke. This research was carried out to demonstrate the efficacy of the suggested bidirectional long-short-term memory (BLSTM) model in assessing the table tennis forehand shadow-play sensory data supplied by the authors in comparison with LSTM time-series investigation approaches. Information was collected by tracking the rackets of 16 players as they performed forehand strokes and assigning assessment ratings to each stroke based on the input of three instructors. The scientists looked at how the hyperparameter values, which are chosen via an optimisation approach, affected the behaviour of DL models. The adaptive learning differential approach has been introduced to enhance the functionality of the standard dragonfly algorithm. Optimal BLSTM settings are selected with the help of the enhanced dragonfly algorithm (IDFOA).  
The experimental findings of this study indicate that the BLSTM-IDFOA is the most effective regression approach currently available.

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