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Published in:

Volume 10 Issue 11
November-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2311436


Registration ID:
528502

Page Number

e253-e258

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Title

NEURA STRESS DETECTION MACHINE LEARNING AND IMAGE PROCESSING USING PYTHON

Abstract

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.

Key Words

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

Cite This Article

"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

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"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

Publication Details

Published Paper ID: JETIR2311436
Registration ID: 528502
Published In: Volume 10 | Issue 11 | Year November-2023
DOI (Digital Object Identifier):
Page No: e253-e258
Country: Pune, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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