Decision support system for effective action recognition of track and field sports using ant colony optimization

Abstract

Based on emerging technologies like arti cial intelligence, machine learning, the Internet of Things, and virtual reality, various Decision Support Systems (DSS) are being employed for the revolution in the sports industry. The coach can now make very precise and unbiased decisions related to the players' skills and selection. It is now very convenient to improve the skills and performance of the players through the implementation of various computer-grounded methodologies. Professionals can recognize the unwanted behavior of players in time during sports and hence can ensure a peaceful atmosphere during sports. The recognition of non-standard actions by the players can help in the avoidance of serious injuries or illness. The DSS can predict the nature of the weather and the sports personnel can take decisions regarding the carrying out of games. The players can do their training without any restrictions on space and time. The real-time analysis of already existing videos of games can help the newcomers learn and improve their skills and performance. The trainers can check the physical tness of the sportsmen very e ciently and provide them with useful and valuable recommendations related to their tness level. The proposed study has used the Ant Colony Optimization to recognize and track the optimal features of athletes to enhance individual as well as team performance in sport competitions.

Improved Decision Support System for College Sports Training Based on Id3 Algorithm

“…The ID3 algorithm, a foundational component of decision tree learning, is particularly well-suited for this task due to its ability to discern patterns and make informed decisions based on input data. In the context of college sports training, the DSS powered by the ID3 algorithm can analyze various factors such as athlete attributes (e.g., physical capabilities, skill levels), training methodologies, injury histories, nutritional needs, and even external variables like weather conditions or competition schedules [2]. By inputting these diverse data points into the system, coaches and trainers can generate insights and recommendations tailored to individual athletes or entire teams [3].…”