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 Book Chapter Faculty Publications- Book Chapter 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 Lakshmi, Dara Vijaya; Srinivasan, T.R.; Bhasker, B.; Thillaiarasu, N. Title A name given to the resource A Cognitive Workload-Aware Machine Learning Model for Performance Enhancement in Cyber-Physical Systems 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 Springer Series in Reliability Engineering;Volume;Part F1359;pp.31-50 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-09917-4_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-09917-4_2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029858227?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029858227?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 Lakshmi D.V., School of Business Management, Christ University, Karnataka, Bangalore, India; Srinivasan T.R., School of Computer Science and Engineering, Presidency University, Karnataka, Bangalore, India; Bhasker B., Department of Information Science and Engineering, Nitte (Deemed to be University), Bengaluru, India; Thillaiarasu N., School of Computing and Information Technology, REVA University, Karnataka, Bangalore, India Description An account of the resource Cyber-Physical Systems increasingly demand seamless coordination between human operators and autonomous processes, which increases the complexity. High cognitive workload in those environments amounts to a degradation of performance, decision fatigue, and increased susceptibility to system failure and cyber threats. To address these challenges, we propose a Neuro-inspired Cognitive Workload Optimizer (NCO), a novel machine-learning-based model for the monitoring, prediction, and optimization of cognitive workload for CPS performance improvement. The NCO framework employs neuro-inspired deep learning techniques, with LSTM networks coupled with an attention mechanism for assessing workload patterns dynamically in time. The adaptive operation of the system depends on executing a contextual analysis of system data and operator interaction metrics, whereby NCO recognizes fluctuations in workload and adjusts the operations of the system in real-time to maintain an optimal state for cognitive functioning. Thus, the model implements an adaptive feedback loop that prioritizes task distribution, resource allocation, and security management based on cognitive load estimations. In this way, CPS environments are hereby enabled to proactively mitigate operator overloads, minimize latencies, and enhance accuracy in decision-making, all while ensuring this is happening under dynamic conditions ensuring robust system performance. Experimental results on simulated CPS datasets indicate that NCO can reduce workloads peaks by 35%, improve system throughput by 28%, and provide better anomaly detection performance in conditions of high stress. The NeuroCPS-Optimizer thus opens up a new paradigm for cognitive-aware CPS management, ensuring that human and machine components are kept within safe and efficient bounds. This research thereby advances the creation of resilient and intelligent CPS that can self-adjust and sustain performance levels in complex and demanding environments. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Cognitive workload optimization; Cyber-physical; Deep learning; Neuro-inspired; Real-time adaptation and performance enhancement Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 16147839; Language A language of the resource English Type The nature or genre of the resource Book chapter 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