Abstract: This study proposed a novel approach to handle mental health, particularly, depression among college students, called CRADDS A Comprehensive Real-time Adaptive Depression Detection System. The novel CRADDS combined advanced tensor fusion networks which is able to analyze emotions using audio, text and video data more accurately, this is possible due to the strength of deep learning and multimodal approaches. This system is constructed with a hybrid algorithm framework that combines SVM (Support Vector Machines), CNN (Convolutional Neural Network) and (Bidirectional Long-Term Short-Term Memory) BiLSTM techniques. To address the limitations identified in earlier research, CRADDS increasing its feature set and using effective machine learning algorithms to reduce false positives and negatives. Further, it includes the advanced IoT devices to collect real time data from various range of public and private sources. The depression symptoms may be continuously monitored in real time, which helps to identify depressions in early stages and guaranteed the perfect well-being of students. Additionally, the model has the ability to adjust based on the interaction features, which helps to provide psychological support using the automatic responses observed from the verbal and nonverbal clues. Experiments show that the proposed CRADDS obtained an impressive accuracy based on the features of text, audio and video, when compared with the existing models. Overall, CRADDS is a useful tool for mental health professionals and educational institutions because it not only identifies depression but also helps to treat it earlier, and guarantees good academic scores and general well-being. The proposed validation accuracy increases from 63.04% to 86.08% which is higher than compared existing SVM model.

Liyan Wang. “Deep Learning-Based Depression Analysis Among College Students Using Multi Modal Techniques”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150793

@article{Wang2024,
title = {Deep Learning-Based Depression Analysis Among College Students Using Multi Modal Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150793},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150793},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Liyan Wang}
}