International Journal on Science and Technology

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Real Time Emotion Detection In Students With Hybrid Deep Learning Architecture Using Resnet And Zfnet

Author(s) Uppala Bharath Kumar, Ravula Rajeev Chowdary, Bakka Tarun Kumar, Rachamandi Sai Siva Teja, Ms. K.Thrilochana Devi
Country India
Abstract The realm of deep learning facilitates the automated extraction of features and the recognition of intricate patterns, rendering it exceptionally proficient for complicated tasks such as image categorization, natural language analysis, and sentiment recognition. Student motivation plays a pivotal role in achieving academic success; however, conventional techniques for evaluating engagement are rooted in subjective assessments. This Paper proposes a real-time sentiment detection system that harnesses a hybrid deep learning structure that fuses ZFNet and ResNet for precise facial expression identification. By utilizing the EmotiW2019 dataset, the model sorts student emotions into engagement-related classifications like "engaged" or "not engaged." The system interprets live webcam input, offering instant visual feedback through emojis and metrics on engagement. By merging cutting-edge computer vision and deep learning methodologies, this adaptable solution amplifies both physical and online learning settings, empowering data-informed interventions to enhance student involvement and educational outcomes. Experimental findings reveal impressive classification precision, instantaneous performance, and practical applicability in academic contexts.
Keywords Emotion Detection, Deep Learning, ResNet, ZFNet, Real-Time Processing, Student Engagement, Computer Vision.
Field Engineering
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-31
Cite This Real Time Emotion Detection In Students With Hybrid Deep Learning Architecture Using Resnet And Zfnet - Uppala Bharath Kumar, Ravula Rajeev Chowdary, Bakka Tarun Kumar, Rachamandi Sai Siva Teja, Ms. K.Thrilochana Devi - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2667
DOI https://doi.org/10.71097/IJSAT.v16.i1.2667
Short DOI https://doi.org/g9dgp7
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