• Ashutosh Salgar
• Prof. Shubhangi Khade
• Purushottam Kulkarni
• Anirudh Katkar
• Prathamesh Shelar

The prevalence of stress as a momentous health concern has profound implications for mental stability. The rise of various social media platforms has expanded opportunities for people to interact and share experiences, generating extensive datasets. These datasets are invaluable for identifying common traits among individuals experiencing depression. Machine learning algorithms can then be applied to discern these traits, enabling the arrangement of the severity of depression. Such classification is crucial for tailoring appropriate support, especially for individuals exhibiting suicidal thoughts. In the medical field, the integration of machine learning has introduced diagnostic tools that enhance accuracy and precision while reducing the burden of laborious tasks requiring human intervention. There is a growing body of evidence supporting the potential of machine learning technologies to detect and improve the treatment of complex mental disorders, including depression. The development of a framework named Artificial Intelligence Mental Evaluation (AiME) represents a promising approach. AiME involves a concise human-computer interactive evaluation coupled with deep learning, allowing for the prediction of depression with satisfactory performance. The simplicity of AiME makes it a valuable tool for mental health professionals, facilitating the swift identification of depression symptoms and enabling timely preventative involvements. Additionally, AiME may address the challenge of interpreting intricate physiological and behavioral biomarkers associated with depression, offering a more objective evaluation. This seminar aims to provide comprehensive insights into the application of machine learning techniques for the analysis of depression detection, emphasizing the potential of technology to transform mental health assessments.

Facial Expressions, K- Nearest Neighbor Classifier, Stress, Stress prediction.

"NEURA STRESS DETECTION MACHINE LEARNING AND IMAGE PROCESSING USING PYTHON", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.e253-e258, November-2023, Available :http://www.jetir.org/papers/JETIR2311436.pdf

"NEURA STRESS DETECTION MACHINE LEARNING AND IMAGE PROCESSING USING PYTHON", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppe253-e258, November-2023, Available at : http://www.jetir.org/papers/JETIR2311436.pdf