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@article{171534,
        author = {Ms. Swati Suryawanshi and Mrs. Roomana Hasan and Mr. Kantilal Chandre},
        title = {AI-Enhanced Emotional Assessment: Detecting Depression Levels through Visual and Vocal Expressions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {7},
        pages = {3924-3928},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171534},
        abstract = {In Automatic depression assessment supported visual and vocal cues may be a rapidly growing research domain. This exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual indication of depression, many proceed used for data gathering, and existing datasets are reviewed. The article describes techniques and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification, and regression approaches, also as different fusion strategies. A quantitative meta-analysis of reported results, counting on performance metrics robust to chance, is included, identifying general trends and key pending issues to be treated in future studies of automatic depression assessment utilizing visual and vocal cues alone or together with cues. The proposed work also administered to predict Depression levels consistent with the current input of videos using deep learning also as NLP.},
        keywords = {Image Processing, Machine Learning, Classification Rule, Convolution Neural Networks, NLP etc},
        month = {January},
        }