Anxiety Recognition of College Students using Deep Learning with Fuzzy Logic

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Dinesh Mali; Durvang Rawool; Onkar Shahagadkar; Aditya Gaikwad

Anxiety Recognition of College Students using Deep Learning with Fuzzy Logic

Authors: Dinesh Mali, Durvang Rawool, Onkar Shahagadkar, Aditya Gaikwad

Unique Paper ID: 176663
Volume: 11
Issue: 11
PageNo: 7438-7443

Keywords: Anxiety Deep learning Fuzzy Logic SentiWordNet Natural Language Processing Mental health.

Abstract:
In today's fast-paced digital world, anxiety has become a significant mental health issue, particularly among college students facing various pressures. This project aims to develop an innovative system for recognizing anxiety levels through the analysis of anxiety related data. By leveraging deep learning techniques integrated with fuzzy logic, it can process text data fetched from popular social media platforms like Twitter and Facebook. Advanced Natural Language Processing (NLP) techniques are employed to extract meaningful features indicative of anxiety, using linguistic resources like SentiWordNet for sentiment analysis. Deep learning model that classifies user’s emotional states based on the extracted features. It integrates with fuzzy logic enhances the model’s ability to handle uncertainties in language, providing a nuanced understanding of anxiety expressions. The realtime reports and analytics that offer mental health professional’s insights into students’ emotional well-being.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 7438-7443

Anxiety Recognition of College Students using Deep Learning with Fuzzy Logic

Available:https://ijirt.org/article?manuscript=176663

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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