Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Prince; Sharma, Nandini; Bushra, Seema; Chinnaiyan, R.; Khanna, Vimal Kumar; Sabarmathi, G. Title A name given to the resource Approaches To Improve Performance of K-Means Clustering Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAMS65118.2025.11234167" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAMS65118.2025.11234167</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030103570?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030103570?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar P., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Sharma N., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Bushra S., AI/ML Lab, Centre of Excellence, School of Commerce &amp; Management, Lingaya's Vidyapeeth, Haryana, Faridabad, India; Chinnaiyan R., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Khanna V.K., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Sabarmathi G., School of Computer Science, Christ University, Bangalore, India Description An account of the resource In this research, we present an enhanced K-Means clustering approach utilizing Neural Engine processors integrated within distributed smartphone networks. Each smartphone runs the K-Means algorithm locally using its Neural Engine to compute centroids efficiently, and these local centroids are then combined to form global clusters on a cloud server. Our implementation significantly reduces computation time while maintaining high clustering accuracy. Experimental evaluation on large datasets demonstrates improved performance over traditional K-Means, proving its suitability for big data analytics in healthcare, IoT, and smart mobile applications. This approach ensures faster processing, lower energy consumption, and effective resource utilization within distributed environments. Further, the proposed method addresses challenges in data privacy by performing local computation and only sharing centroid information. The results indicate potential for scalable clustering solutions in real-time scenarios, opening new directions for edge-cloud integrated machine learning frameworks that harness device-level AI accelerators for complex data-driven tasks efficiently. 2025 IEEE. Subject The topic of the resource Genetic Algorithm; K Means Clustering; KM-SML; Neural Engine Processor; Principal Components Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159610-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online