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 Eugine Prince, M.; Rathi Devi, T.; Anu Disney, D.; Kannan, K.; Ramesh, K.; Sujatha Therese, P. Title A name given to the resource Quantum-enhanced neuro-fusion framework for intelligent decision-making in smart home IoT surveillance 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 Franklin Open;Volume;15;Issue;;Article No.;100561; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.fraope.2026.100561" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.fraope.2026.100561</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032190667?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032190667?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 Eugine Prince M., Department of Physics, S. T. Hindu College, Tamil Nadu, Nagercoil, India; Rathi Devi T., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Anu Disney D., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Tamil Nadu, Chennai, India; Kannan K., Department of Electronics and Communication Engineering, R. M. K. College of Engineering and Technology, Tamil Nadu, Thiruvallur, India; Ramesh K., Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan College of Engineering and Technology, Tamil Nadu, Chennai, India; Sujatha Therese P., Department of Electrical and Electronics Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India Description An account of the resource Smart-home surveillance systems increasingly rely on heterogeneous IoT data streams, requiring efficient fusion, scalability, and robustness under noisy sensing conditions. This paper proposes a Quantum-Inspired Deep Neuro-Fusion Architecture (QDNFA) for anomaly detection in edgecloud IoT environments. The framework integrates modular encoders, temporal alignment, and a quantum-inspired optimisation mechanism to support multi-modal data processing while maintaining real-time performance. Experimental evaluation is conducted on the CASAS Smart Home dataset to validate sensor-centric anomaly detection, scalability across multiple devices, and edgecloud inference efficiency. While the architecture is designed to support audio and video modalities, the present study focuses on low-dimensional sensor data, and large-scale benchmarking on audiovisual surveillance datasets is identified as future work. Results demonstrate improved detection accuracy and reduced latency compared to baseline methods in sensor-driven smart-home scenarios. 2026 The Author(s). Subject The topic of the resource Adversarial resilience; AI-driven IoT systems; Anomaly detection; Deep neuro-fusion; Edge computing; Internet of things; Multi-modal data fusion; Quantum-inspired optimization; Real-time surveillance; Smart home security Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 27731871; 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; Green Open Access Format The file format, physical medium, or dimensions of the resource online