PreprintArticleVersion 2This version is not peer-reviewed
An Open-Access Machine Learning Framework for Sustainable Tennis Ball Management: Optimizing Use and Reducing Environmental Impact for Tennis Stores and Clubs
Version 1
: Received: 8 October 2024 / Approved: 8 October 2024 / Online: 8 October 2024 (16:51:30 CEST)
Version 2
: Received: 8 October 2024 / Approved: 9 October 2024 / Online: 9 October 2024 (11:37:14 CEST)
How to cite:
Gadde, N.; Tallapragada, D.; Dey, S.; Mehta, L.; Chahal, J.; Yu, N. An Open-Access Machine Learning Framework for Sustainable Tennis Ball Management: Optimizing Use and Reducing Environmental Impact for Tennis Stores and Clubs. Preprints2024, 2024100609. https://doi.org/10.20944/preprints202410.0609.v2
Gadde, N.; Tallapragada, D.; Dey, S.; Mehta, L.; Chahal, J.; Yu, N. An Open-Access Machine Learning Framework for Sustainable Tennis Ball Management: Optimizing Use and Reducing Environmental Impact for Tennis Stores and Clubs. Preprints 2024, 2024100609. https://doi.org/10.20944/preprints202410.0609.v2
Gadde, N.; Tallapragada, D.; Dey, S.; Mehta, L.; Chahal, J.; Yu, N. An Open-Access Machine Learning Framework for Sustainable Tennis Ball Management: Optimizing Use and Reducing Environmental Impact for Tennis Stores and Clubs. Preprints2024, 2024100609. https://doi.org/10.20944/preprints202410.0609.v2
APA Style
Gadde, N., Tallapragada, D., Dey, S., Mehta, L., Chahal, J., & Yu, N. (2024). An Open-Access Machine Learning Framework for Sustainable Tennis Ball Management: Optimizing Use and Reducing Environmental Impact for Tennis Stores and Clubs. Preprints. https://doi.org/10.20944/preprints202410.0609.v2
Chicago/Turabian Style
Gadde, N., Jashan Chahal and Ning Yu. 2024 "An Open-Access Machine Learning Framework for Sustainable Tennis Ball Management: Optimizing Use and Reducing Environmental Impact for Tennis Stores and Clubs" Preprints. https://doi.org/10.20944/preprints202410.0609.v2
Abstract
Most of the management in retail and sports facilities about tennis balls is subjective and leads to early disposal, further contributing to environmental waste. This paper discusses a machine learning-based framework to optimize tennis ball usage by providing an accurate forecast of their lifetime, using data-driven insights. We develop and test several predictive models using Random Forest, Support Vector Machines, and Neural Networks based on holistic datasets that include data on bounce dynamics, material composition, usage frequency, and environmental conditions. The framework also allows real-time monitoring and decision-making for the replacement of the tennis balls, in addition to identification of the key factors that influence performance degradation. Our results show that this could significantly extend the life of tennis balls, which again can help reduce the overall waste and support sports industry sustainability efforts. The proposed open-access framework is scaling up a solution for tennis clubs/stores in order to improve operational efficiency with a low environmental impact while contributing to the circular economy at the same time.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
