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 Poornima, Sharon; Immanuel, Ashok V. Title A name given to the resource Intelligent self-organizing microservice composition using hybrid learning for neonatal ward Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived IAES International Journal of Artificial Intelligence;Volume;15;Issue;2;pp.1097-1108 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijai.v15.i2.pp1097-1108" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijai.v15.i2.pp1097-1108</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036029784?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036029784?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 Poornima S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Immanuel A.V., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource This research presents an innovative self-organizing microservice composition model specifically tailored for dynamic and time-sensitive healthcare environments such as neonatal intensive care units (NICU). A hybrid machine learning classifier detects neonatal conditions and assigns treatment plans based on real-time vitals. The composition process is guided by a deep learning agent that combines unsupervised and reinforcement learning to develop intelligent bonding strategies. Microservices act as autonomous agents, supporting decentralized service choreography within the self-organizing framework. The bonding strategies of direct bonding and shared bonding are implemented for single conditions and coexisting conditions, respectively. The simulation results are based on actual NICU data, demonstrating the ability of the model to dynamically compose services while ensuring optimal resource utilization. The model demonstrates an adaptive and dynamic composition through emergence and continuous learning for changing clinical conditions, and demonstrates emergent behavior through reinforcement learning. The model's predictive capabilities enable anticipatory service loading, providing context-aware treatment in critical healthcare scenarios. This self-organizing architecture model offers a scalable and robust solution for autonomous, decentralized service choreography in critical healthcare environments. This is an open access article under the CC BY-SA license https://creativecommons.org/licenses/by-sa/4.0/. Subject The topic of the resource Critical care; Health care; Hybrid learning; Microservice composition; Self-organization Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20894872; 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 All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online