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Technical Case Study

Designing a Multimodal AI System for Mental Health Screening

The Core Objective

Early detection of mental health indicators requires combining emotional facial cues with textual sentiment signals.

01.Engineering Approach

Developed a multimodal AI pipeline integrating computer vision (OpenCV) with NLP-based sentiment analysis using Transformer-LSTM ensembles.

02.System Architecture

Emotion detection module processes facial inputs, NLP module evaluates sentiment, and ensemble learning combines both outputs for final prediction.

03.Lessons Learned

Multimodal AI systems provide stronger predictive signals than unimodal models.
Ensemble architectures increase reliability in sensitive AI applications.