Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 0976-3503
Online ISSN:
0976-2930


  About JET
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
International Journal of Computational Linguistics Research (IJCL)
International Journal of Web Application (IJWA)

 

 
Journal of E-Technology

Design of a Deep Learning-Based Psychological Counseling System for College Students’ Mental Health
Ling Pan, Haili Ma, Yongqiang Li
Zhengzhou Railway Vocational and Technical College Zhengzhou, Henan, 451460, China
Abstract: In response to the necessity of psychological counselling for college student’s mental health, this paper proposes a psychological counselling and assessment system based on PGCapsNet. The system aims to achieve both college students’ psychological health assessment and real-time online counseling, while improving the accuracy of predicting their mental health status. Having an experienced psychological counsellor is crucial for college students’ mental health and crisis management. The simulation results demonstrate that compared to other models, using the PGCapsNet model can effectively improve the levels of precision, recall, F1-measure, and accuracy of the college student psychological counseling system, achieving 70%, 75%, 72%, and 74%, respectively. This validates the feasibility and superiority of this design. This paper discusses a novel algorithm that combines capsule networks and convolutional neural networks, incorporating dynamic routing algorithms and deep learning capabilities.
Keywords: Deep Learning, Psychological Counselling, College Students Design of a Deep Learning-Based Psychological Counseling System for College Students’ Mental Health
DOI:https://doi.org/10.6025/jet/2024/15/1/1-7
Full_Text   PDF    Download:   123  times
References:

[1] Varga, M. A., Lanier, B., Biber, D., et al. (2021). Holistic Grief Effects, Mental Health, and Counseling Support in Bereaved College Students. College Student Affairs Journal, 39(1), 1- 13.
[2] West, E. M., Moate, R., Baltrinic, E. R., et al. (2021). Counselor educators’ perspectives on helpful learning for clinical mental health counseling students. Counselor Education and Supervision, 60(3), 235-250.
[3] Liu, Y. (2021). Research on Mental Health Intervention of College Students Based On Music Therapy. Revista Brasileira de Medicina do Esporte, 27(spe), 40-42.
[4] Ospina-Pinillos, L., Davenport, T. A., Ricci, C. S., et al. (2018). Developing a mental health eClinic to improve access to and quality of mental health care for young people: using participatory design as research methodologies. Journal of Medical Internet Research, 20(5), e188.
[5] Du, C., Yu, C., Wang, T., et al. (2022). Impact of virtual imaging technology on film and television production education of college students based on deep learning and Internet of Things. Frontiers in Psychology, 12, 766634.
[6] Li, X., Shi, X. (2021). Design and Application of Mental Health Intelligent Analysis System for College Students Majoring in Physical Education. Journal of Physics: Conference Series, 1852(3), 032049 (7pp).
[7] Wang, T., Park, J. (2021). The Design and Execution of a Cognitive Sports Training System for the Mental Health Education of College Students. Frontiers in Psychology, 12, 634978.
[8] Wang, P. L., Li, Q., Guo, H. (2019). A research on deep learning model for face emotion recognition based on Swish activation function. Journal of Image and Signal Processing, 8(3), 110-120. recognition based on Swish activation function. Journal of Image and Signal Processing, 8(3), 110-120.
[9] Lei, Y., Belkacem, A. N., Wang, X., et al. (2022). A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders. Biomedical Signal Processing and Control, 72, 103370.
[10] Pandey, S., Sharma, S., Wazir, S. (2022). Mental healthcare chatbot based on natural language processing and deep learning approaches: Ted the therapist. International Journal of Information Technology, 14(7), 3757-3766.
[11] Du, F., Zhu, J. (2022). Mobile Terminal-Based Remote Counseling Education System for Middle School Students’ Mental Health. In International Conference on E-Learning, E-Education, and Online Training. Cham: Springer Nature Switzerland, 351-364.
[12] Trappey, A. J. C., Lin, A. P. C., Hsu, K. Y. K., et al. (2022). Development of an empathycentric counseling chatbot system capable of sentimental dialogue analysis. Processes, 10(5), 930.
[13] Zhao, L. (2018). Design and implementation of psychological counseling service system based on android. In: Proceedings of the 2018 1st International Conference on Internet and e-Business, 193-197.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info