Sai Srinivas Vellela

Work place: Department of CSE-Data Science, Chalapathi Institute of Technology, Guntur 522016, India



Research Interests: Data Structures, Cloud Computing, Data Mining


Sai Srinivas Vellela is working as Assistant Professor in the Department of Computer Science and Engineering (Data Science), Chalapathi Institute of Technology, Guntur, India. He has earned his M.Tech from Godavari Institute of Engineering and Technology, affiliated to JNTU Kakinada. He is currently pursuing his doctorate in the field of Cloud Computing from Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai. He has over 12 years of teaching and research experience. His areas of interest include Cloud Computing, Data Warehousing and Mining, Data Structures, Distributed Systems, etc.

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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