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Predicting Mental Health Conditions Using Student Demographic Information
Ashwith Y.
With this, we will train the data to extrapolate what symptoms the user has based on their demographics and academic/social life. This can help understand what symptoms a person has of certain characteristics.
We are working with supervised data and this is classification data. With this, we will train the data to extrapolate what symptoms the user has based on their demographics and academic/social life. This can help understand what symptoms a person has of certain characteristics. Age, gender, course year, course study, cgpa, and marital status are the inputs of the data. Whether the person is more prone to depression, anxiety, or panic attacks is the output of the model.
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Ashwith Y.
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VisionAssist: Enhancing Accessibility for Individuals with Visual Impairment Through AI
This project explores how AI can support individuals with visual impairments by developing a system that converts images containing text or math into audio and Braille in near real-time. Using a fine-tuned OCR model, the system achieves high accuracy and low latency, demonstrating that AI can be a powerful tool for improving accessibility to educational content.
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