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 Conference Paper Faculty Publications- Conference Papers 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 Thomas, Aksa Maria; Kirubanand, V.B. Title A name given to the resource Emotion-Aware Sign Language Recognition Using CNN and UNET Architectures 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 Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCIST67338.2025.11438577" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCIST67338.2025.11438577</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037453582?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037453582?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 Thomas A.M., CHRIST (Deemed to be University), Department of Computer Science, India; Kirubanand V.B., CHRIST (Deemed to be University), Department of Computer Science, India Description An account of the resource This paper proposes an AI-based system for the recognition of sign language with the detection of emotions for more expressive communication among speech-impaired and hearing individuals and others. Traditional sign language systems focus mainly on the aspect of hand gestures and neglect the signs for emotions that add meaning and context. In order to overcome the limitation, the project proposes a system that utilizes Convolutional Neural Nets (CNNs) for the recognition of hand signs and UNET for the segmentation of the picture so that the area of the hands can be discriminated from the background. Facial Emotion Recognition (FER) is also incorporated in order to detect signs such as happiness, sadness, or anger. Overall, the parts together constitute a multimodal recognition system that can read signs and emotions and produce more natural and expressive outputs. The paper delves into architecture, dataset challenges, and implementation concepts with publicly available databases such as RWTH-PHOENIX-Weather 2014T. The combined approach can enhance inclusivity and access in learning, communication, and assistive technology. 2025 IEEE. Subject The topic of the resource Convolutional Neural Network (CNN); Deep Learning; Facial Emotion Recognition (FER); Human-Computer Interaction (HCI); Sign Language Recognition (SLR); UNET Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159676-7; Language A language of the resource English Type The nature or genre of the resource Conference paper 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