Decision Support System for Optimizing Tactics and Strategies of Sports Competition Using Reinforcement Learning Algorithm

Abstract

This paper presented an augmented reality secure edge machine learning dynamic optimization model for detecting volleyball movement recognition using a decision support system. The proposed Edge Augmented Decision Support System Blockchain (EdgeAu-DSSBC) model aims to address the limitations of centralized machine learning models, such as high latency, network congestion, and data privacy concerns, by utilizing edge computing and dynamic optimization techniques. The proposed EdgeAu-DSSBC system consists of two main components: an augmented reality interface that allows users to interact with the system and an edge machine learning algorithm that performs the recognition task. The system uses dynamic optimization techniques to optimize the parameters of the machine learning algorithm in real time based on the feedback received from the users. The decision support system provides additional guidance to the users and helps them to make informed decisions based on the recognition results. The EdgeAu-DSSBC model was evaluated using a dataset of volleyball movement videos and compared to existing centralized and edge-based machine-learning models. The experimental results demonstrate that the EdgeAu-DSSBC exhibits improved performance with an accuracy of 99%.