Anxiety Recognition of College Students using Deep Learning with Fuzzy Logic

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

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{176663,
        author = {Dinesh Mali and Durvang Rawool and Onkar Shahagadkar and Aditya Gaikwad},
        title = {Anxiety Recognition of College Students using Deep Learning with Fuzzy Logic},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7438-7443},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176663},
        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.},
        keywords = {Anxiety, Deep learning, Fuzzy Logic, SentiWordNet, Natural Language Processing, Mental health.},
        month = {April},
        }

Cite This Article

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

Anxiety Recognition of College Students using Deep Learning with Fuzzy Logic

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