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 Article Faculty Publications -Articles 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 Sridevi, M.; Pandit, Karuna; Tripathis, Abhinav Priyadarshi; Dhaundiyal, Pankaj; Vekariya, Vipul; Gurpur, Shashikala; Lingaraj, K.; Philip, Jim Mathew Title A name given to the resource NEUROSYMBOLIC AI FOR CONTEXT-AWARE RESOURCE MANAGEMENT IN 5G SMART HEALTHCARE NETWORKS 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 Journal of Environmental Protection and Ecology;Volume;26;Issue;3;pp.1061-1071 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105009388501?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009388501?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 Sridevi M., Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Tamil Nadu, Chennai, 602 105, India; Pandit K., Department of Information Science and Engineering, NITTE (Deemed to be University), NMAM Institute of Technology, Karnataka, 574 110, India; Tripathis A.P., School of Business and Management, Christ University, NCR Campus, Uttar Pradesh, Bangalore, 201 003, India; Dhaundiyal D., School of Business and Management, Christ University, NCR Campus, Uttar Pradesh, Bangalore, 201 003, India; Vekariya V., Department of Computer Science and Engineering, Parul Institute of Engineering and Technology, Gujarat, 391 760, India; Gurpur S., Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School, Pune, Symbiosis International (Deemed University), Maharashtra, Pune, 411 014, India; Lingaraj K., Department of Computer Science and Engineering, Rao Bahadur Y. Mahabaleswarappa Engineering College, Karnataka, 583 104, India; Philip J.M., Department of Computer Science and Engineering, Sri Ramakrishna Institute of Technology, Tamil Nadu, Coimbatore, India Description An account of the resource Context-aware resource management in 5G Utilising neurosymbolic AI has growing impacts in the next-generation healthcare systems as smart healthcare networks to overcome the crucial issues of optimal service delivery and dynamic resource allocation. The 5G technology is adopted in healthcare networks for real-time processing, low latency, and high reliability that supports a vital application as telemedicine and remote patient monitoring. The resource management process of existing model rapidly fails in complicated, context-dependent situation with dynamic demands. To overcome these limitations, we proposed improved framework that combines a deep learning (DL) models for context extraction and symbolic reasoning process for decision-making. To determine the contextual patterns, the DL component analyses the multi-source data, as patient vitals, network conditions, and device status by utilising Transformer and Graph Neural Networks (GNNs). These data fed into symbolic reasoning module employ a knowledge graph and a rule-based system to dynamically allocate and distribute the resources based on the predetermined healthcare policies and requirements. Experimental results of this study showcase the improvements by attaining a reduction in latency, enhances in resource utilisation efficiency, and improved Quality of Service (QoS) for essential healthcare applications. In 5G-enabled smart healthcare systems, the results ensure a proposed model potential to transforms a resource management and ensure context-aware, versatile, and dependable service delivery for enhanced patient outcomes. 2025, Scibulcom Ltd.. All rights reserved. Subject The topic of the resource 5G smart healthcare networks; context-aware resource management; Deep learning; neurosymbolic AI; quality of service; symbolic reasoning Publisher An entity responsible for making the resource available Scibulcom Ltd. Relation A related resource ISSN: 13115065; Language A language of the resource English Type The nature or genre of the resource Article 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