https://gaexcellence.com/ijscol/issue/feed 2026-04-27T09:46:26+08:00 Haslinda haslinda@gaexcellence.com Open Journal Systems <p>The <strong>International Journal of Supply Chain, Operation Management and Logistics (IJSCOL)</strong> is published by <strong>Global Academic Excellence (M) Sdn Bhd (GAE)</strong> to serve academicians a platform of sharing and updating their knowledge and research outputs as well as information within the sphere of supply chain, operation management and logistics. <strong>IJSCOL </strong>invites researchers, academicians, practitioners and students for the submission of articles either in English or Malay. The publication for this refereed journal are<strong> quarterly (March, June, September and December).</strong> This journal uses <strong>double</strong>-<strong>blind review</strong>, which means that both the <strong>reviewer</strong> and <strong>author identities</strong> are concealed from the reviewers, and vice versa, throughout the review process. To facilitate this, authors need to ensure that their manuscripts are prepared in a way that does not give away their identity.</p> https://gaexcellence.com/ijscol/article/view/7481 2026-04-27T09:46:26+08:00 Nurin Hazwani Hamidi 214615@student.upm.edu.my Nur Ezlin Zamri ezlinzamri@upm.edu.my Nurin Kamalin Mohd Fadil 214516@student.upm.edu.my Farisya Husna Mansor 215191@student.upm.edu.my Nurul Huda Ahmad Rusli 214520@student.upm.edu.my Ameer Azamuddin Abdul Ghafar 200238@student.upm.edu.my

<p style="text-align: justify;">Nowadays, athletes are exposed to high performance demands and intense training loads, increasing their risk of injury. The absence of early detection systems often leads to late interventions, longer recovery periods and performance decline. This project addresses the need for an interpretable early detection model for athlete injury prevention. The proposed solution is built using Random 3-Satisfiability Reverse Analysis (RAN3SATRA) model enhanced through correlation-based attribute selection. By implementing logic mining, the systems able to extract human-readable rules that explain relationships between injury and performance factors, allowing domain experts to make transparent, data-driven decisions. The model was tested using the Athlete Injury and Performance Dataset and demonstrated strong and consistent performance across multiple metrics including accuracy, sensitivity, specificity, Matthews Correlation Coefficient and Negative Predictive Value. These results indicate that the proposed logic-based framework is affective in identifying injury risk patterns while maintaining interpretability. The logic mining process successfully identified key injury-related factors such as training load balance, ACL injury risk level, fatigue level, weekly recovery days and weekly training duration. These findings offer direct insight into injury prevention and enable the development of actionable monitoring systems for coaches, physiotherapists and sports scientists. This project reflects the potential of explainable AI in sports analytics, combining data science and logic reasoning to protect athlete health and optimise performance.</p> <p style="text-align: justify;">&nbsp;</p>

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