Experimental Investigation on the Mechanical Behaviour of M30 Grade Self-Compacting Concrete Reinforced with Hybrid Steel and Polypropylene Fibres

Ganesh S Patil, Samrudh K and Brijbhushan S

International Journal of Scientific Research in Science and Technology, 2026, vol. 13, issue 4, 170-185

Abstract: Emotion plays a central role in human-to-human interaction, conveying sentiments through body language and voice tone. Such communication is especially important for speech- and hearing-impaired individuals, making the understanding of emotional systems essential in smart communication devices. With advances in artificial intelligence and deep learning, the field has moved away from hand-crafted feature systems toward more complex, data-driven approaches. The use of convolutional, recurrent, and transformer-based architectures has improved both accuracy and reliability of emotion recognition systems. This paper reviews the different methodologies used to analyse facial expressions and hand gestures for emotion recognition, with emphasis on systems being developed to support mute and deaf individuals. The review surveys recent datasets, models, and fusion strategies, and discusses how continued progress in this area can make communication devices more effective and inclusive for the mute and deaf community.

Keywords: Deep Learning; Emotion Recognition; Hand Gesture Recognition; Facial Expression Recognition; Human-Computer Interaction (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v13:y2026:i4:id:1741

DOI: 10.32628/IJSRST2613414

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