https://journals.tultech.eu/index.php/jtse/issue/feed 2025-07-04T17:42:15+02:00 Assoc. Prof. Dr. Klodian Dhoska kdhoska@upt.al Open Journal Systems <p><strong>Journal of Transactions in Systems Engineering (JTSE)</strong> is an open-access and peer-reviewed journal that provides the latest research and developments in all theoretical and practical aspects and fields of engineering applications, informatics, and engineering systems design. The journal publishes three times a year (January, June, and October). All the content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the DOAJ and BOAI definition of open access..</p> https://journals.tultech.eu/index.php/jtse/article/view/272 2025-07-04T17:42:15+02:00 Ahmed T. Alhasani ahmed.alhasani@atu.edu.iq

<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>This work investigates the combination of unsupervised machine learning with blockchain- influenced data integrity aspects on multivariate time series (MTS) sports activity data. Using the SportData MTS dataset with complex physiological and movement parameters such as heart rate, speed, and altitude, we used K-Means clustering to uncover hidden patterns in the data and incorporated blockchain-influenced hash chains for traceability and integrity of data. Each of the datasets was standardized to ensure equal scaling, and three clusters were identified using silhouette score and elbow method evaluation. The result confirms K-Means to effectively cluster the data into tightly separated groups, with principal component analysis (PCA) plots confirming that there is substantial separation. Silhouette score analysis also confirmed the compactness and separability of groups. In addition, blockchain-inspired hashing was applied to each record to simulate tamper- evidence, providing a firm grounding for secure machine learning pipelines. The end-to-end solution not only reveals the inherent structure in sports activity data but also hints at maintaining data integrity to provide sound and transparent machine learning results, paving the way for future work in secure sports analytics, activity recognition, and anomaly detection.</p> </div> </div> </div>

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