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 Ahmad, Akram; Singh, Vaishali; Upreti, Kamal Title A name given to the resource AI-driven emotion recognition systems for sustainable mental health care: an engineering perspective 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 International Journal of Advances in Applied Sciences;Volume;14;Issue;4;pp.1111-1117 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijaas.v14.i4.pp1111-1117" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijaas.v14.i4.pp1111-1117</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026990261?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026990261?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 Ahmad A., Maharishi University of Information Technology, Lucknow, India; Singh V., Maharishi University of Information Technology, Lucknow, India; Upreti K., CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India Description An account of the resource Emotion recognition systems are transforming human-computer interaction (HCI) applications by enabling AI-driven, adaptive, and responsive mental health interventions. This study explores AI-based emotion recognition technologies using facial expressions, voice analysis, text-based sentiment processing, and physiological signals to develop scalable, real-time mental health support systems. Utilizing datasets such as FER2013, JAFFE, and CK+, our research examines deep learning models, including EfficientNet-XGBoost, which achieved over 90% accuracy across key evaluation metrics. Unlike traditional mental health interventions, AI-driven systems provide cost-effective, accessible, and sustainable solutions through telemedicine, wearable biosensors, and virtual counselors. The study also highlights critical challenges such as algorithmic bias, ethical AI compliance, and the energy consumption of deep learning models. By integrating machine learning, cloud-based deployment, and edge computing, this research contributes to the development of sustainable, ethical, and user-centric AI solutions for mental health care. Future directions include AI model optimization for energy-efficient deployments and the creation of diverse, inclusive datasets to improve performance across global populations. 2025, Intelektual Pustaka Media Utama. All rights reserved. Subject The topic of the resource Environmental awareness; Human-computer interaction; Machine learning; Mental illness; Predictive modeling; Sustainable engineering Publisher An entity responsible for making the resource available Intelektual Pustaka Media Utama Relation A related resource ISSN: 22528814; 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