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Fusion of Multimodal Educational Data for the Identification of Student’s Mental Health

Dr.N.B. Kadu, Tejaswini Chikane, Gayatri Kharde, Swaleha Shaikh

Abstract


Mental problems among students are becoming a growing concern in schools. The inclusion of technology in education offers new avenues for the early diagnosis of mental health problems. Multimodal information such as academic work, social media, biometric readings, and behavioral information can be combined to build effective mental health detection models. This paper provides a survey of multimodal educational data fusion methods for identifying students' mental health problems and suggests an ideal resource management framework to manage large-scale data analysis. The study also addresses issues in virtual machine (VM) migration while implementing these models in cloud-based systems for real-time mental health tracking.Mental health problems among students have become a rising issue in schools. Utilizing multimodal information from scholarly records, wearable sensors, social media, and behavioral data provides a new challenge for early mental health detection. This paper introduces a cloud-based system incorporating multimodal data fusion methods and machine learning models for real-time mental health detection. The research also delves into the significance of efficient resource management and outlines research challenges in virtual machine (VM) migration that are essential for scalable deployment.

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References


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