Abstract :

Depression Is One Of The Most Prevalent Mental Health Disorders Globally, Often Going Undetected Due To The Subjective Nature Of Emotional Assessment And Limited Access To Timely Psychological Evaluation. To Address This Challenge, This Study Presents An Enhanced Emotional Segmentation System For Depression Detection Using Artificial Neural Networks (ANN). The Proposed System Analyzes Multimodal Data Including Speech Signals, Facial Expressions, And Textual Inputs To Identify Emotional Patterns Indicative Of Depressive States. The Proposed Method Involves Data Acquisition And Preprocessing, Where Audio, Visual, And Textual Datasets Are Collected And Normalized. Emotional Feature Extraction, Using Mel-Frequency Cepstral Coefficients (MFCCs), Facial Landmark Detection, And Sentiment Embedding Techniques To Capture Affective Cues, And ANN-Based Emotional Segmentation And Classification, Where The Model Learns Complex Relationships Between Emotional Indicators And Depression Levels Through Multiple Hidden Layers. Experimental Evaluation On Standard Emotional And Depression Datasets Demonstrates That The Proposed ANN Model Achieves Superior Accuracy, Precision, And Sensitivity In Detecting Depression Compared To Conventional Machine Learning Techniques. The System Effectively Distinguishes Between Normal, Mildly Depressed, And Severely Depressed Emotional States, Ensuring Early And Reliable Identification. Overall, The Enhanced Emotional Segmentation Framework Provides A Robust, Adaptive, And Intelligent Approach For Automated Depression Detection, Supporting Clinicians And Mental Health Practitioners In Improving Diagnosis And Personalized Treatment Planning. Keywords: Machine Learning, Depression Detection, Artificial Neural Network, Facial Landmark Detection.

Published:

11-3-2025

Issue:

Vol. 25 No. 3 (2025)

Page Nos:

235-241

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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