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Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance

Anwar, Khalid; Zafar, Aasim; Iqbal, Arshad; Sohail, Shahab Saquib; Hussain, Amir; Karaca, Yeliz; Hijji, Mohammad; Saudagar, Abdul Khader Jilani; Muhammad, Khan

Authors

Khalid Anwar

Aasim Zafar

Arshad Iqbal

Shahab Saquib Sohail

Yeliz Karaca

Mohammad Hijji

Abdul Khader Jilani Saudagar

Khan Muhammad



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.

Journal Article Type Article
Acceptance Date Dec 22, 2022
Online Publication Date Oct 28, 2023
Publication Date 2023
Deposit Date Jan 8, 2024
Publicly Available Date Jan 8, 2024
Journal Fractals
Print ISSN 0218-348X
Electronic ISSN 1793-6543
Publisher World Scientific Publishing
Peer Reviewed Peer Reviewed
Volume 31
Issue 10
Article Number 2340149
DOI https://doi.org/10.1142/s0218348x23401497
Keywords Fractal AI, OWA, Multi-Criteria Decision-Making (MCDM), Data Analysis, Artificial Intelligence, Sports, Surveillance
Public URL http://researchrepository.napier.ac.uk/Output/3367785

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