ARTIFICIAL INTELLIGENCE-DRIVEN APPROACH TO IDENTIFY AND RECOMMEND THE WINNER IN A TIED EVENT IN SPORTS SURVEILLANCE

Khalid Anwar, Aasim Zafar, Arshad Iqbal, Shahab Saquib Sohail, Amir Hussain, Yeliz Karaca, Mohammad Hijji, Abdul Khader Jilani Saudagar and Khan Muhammad
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Khalid Anwar: Department of Computer Science, Aligarh Muslim University, Aligarh, India†School of Computer Science, Engineering and Technology, Bennett University, Greater Noida, India
Aasim Zafar: Department of Computer Science, Aligarh Muslim University, Aligarh, India
Arshad Iqbal: ��KA Nizami Centre for Quranic Studies, Aligarh Muslim University, Aligarh, India
Shahab Saquib Sohail: �Department of Computer Science and Engineering, SEST, Jamia Hamdard, New Delhi, India
Amir Hussain: �Edinburg Napier University, Edinburg, UK
Yeliz Karaca: ��University of Massachusetts Chan Medical School (UMASS), Worcester, MA 01655, USA
Mohammad Hijji: *Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Abdul Khader Jilani Saudagar: ��†Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Khan Muhammad: ��‡Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea

FRACTALS (fractals), 2023, vol. 31, issue 10, 1-17

Abstract: The proliferation of fractal artificial intelligence (AI)-based decision-making has propelled advances in intelligent computing techniques. Fractal AI-driven decision-making approaches are used to solve a variety of real-world complex problems, especially in uncertain sports surveillance situations. To this end, we present a framework for deciding the winner in a tied sporting event. As a case study, a tied cricket match was investigated, and the issue was addressed with a systematic state-of-the-art approach by considering the team strength in terms of the player score, team score at different intervals, and total team scores (TTSs). The TTSs of teams were compared to recommend the winner. We believe that the proposed idea will help to identify the winner in a tied match, supporting intelligent surveillance systems. In addition, this approach can potentially address many existing issues and future challenges regarding critical decision-making processes in sports. Furthermore, we posit that this work will open new avenues for researchers in fractal AI.

Keywords: Fractal AI; OWA; Multi-Criteria Decision-Making (MCDM); Data Analysis; Artificial Intelligence; Sports; Surveillance (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401497

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