ItemArticle

NEUROSYMBOLIC AI FOR CONTEXT-AWARE RESOURCE MANAGEMENT IN 5G SMART HEALTHCARE NETWORKS

Title
NEUROSYMBOLIC AI FOR CONTEXT-AWARE RESOURCE MANAGEMENT IN 5G SMART HEALTHCARE NETWORKS
Creator
Sridevi, M.; Pandit, Karuna; Tripathis, Abhinav Priyadarshi; Dhaundiyal, Pankaj; Vekariya, Vipul; Gurpur, Shashikala; Lingaraj, K.; Philip, Jim Mathew
Description
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.
Source
Journal of Environmental Protection and Ecology;Volume;26;Issue;3;pp.1061-1071
Date
01-01-2025
Publisher
Scibulcom Ltd.
Subject
5G smart healthcare networks; context-aware resource management; Deep learning; neurosymbolic AI; quality of service; symbolic reasoning
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 13115065;
Format
online
Language
English
Type
Article

Citation

Sridevi, M.; Pandit, Karuna; Tripathis, Abhinav Priyadarshi; Dhaundiyal, Pankaj; Vekariya, Vipul; Gurpur, Shashikala; Lingaraj, K.; Philip, Jim Mathew, “NEUROSYMBOLIC AI FOR CONTEXT-AWARE RESOURCE MANAGEMENT IN 5G SMART HEALTHCARE NETWORKS,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23813.

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