2.5

CiteScore

8.8

Global Impact Factor

A STUDY ON ANXIETY PROBLEMS AMONG STUDENTS AT ST. JOSEPH’S COLLEGE OF ARTS AND SCIENCE - CUDDALORE


Paper ID: EIJTEM_2026_13_1_149-155

Author's Name: Vanathi Selvaraj, Perkish Salomina, Durai Raj

Volume: 13

Issue: 1

Year: 2026

Page No: 149-155

Abstract:

Anxiety is the feeling of fear or panic. Most people feel anxious, panicky or fearful about situations in life, such as money problems or exams but often once the difficult situation is over, you feel better and calmer. Sometimes the feelings of fear or anxiety continue after the difficult situation or sometimes you may feel a stronger sense of fear than other people and this is when anxiety becomes a problem and can affect you doing everyday things. This paper briefly describes the research carried out in the problems of anxiety. It focuses anxiety among college students, nature of generalized anxiety disorder (GAD), panic disorder, Phobias, Social anxiety disorder of college students. Emphasizing how counseling will help students with anxiety problems and also suggested preventive measures to be taken by college such as setting up student counseling centers, create awareness among college students in seeking help with counseling centers. They should be giving regular seminar and workshop for teachers and college students on various issues of anxiety problems and its coping mechanism.

Keywords: ANXIETY PROBLEMS, ST. JOSEPH’S COLLEGE OF ARTS AND SCIENCE, CUDDALORE

References:

1. Zhang, J. (2024). Optimization design of highway route based on deep learning. Frontiers in Future Transportation, 5, 1430509.
2. Rahman, T., Mahmud, T., Roy, S., Setara, M. M., Hossain, M. S., & Andersson, K. (2024, June). Enhancing road safety: A deep learning approach to automated crack detection. In 2024 Second International Conference on Inventive Computing and Informatics (ICICI) (pp. 405-411). IEEE.
3. Khalil, R. A., Safelnasr, Z., Yemane, N., Kedir, M., Shafiqurrahman, A., & Saeed, N. (2024). Advanced learning technologies for intelligent transportation systems: Prospects and challenges. IEEE Open Journal of Vehicular Technology.
4. Wei, X. (2024). Enhancing road safety in internet of vehicles using deep learning approach for real-time accident prediction and prevention. International Journal of Intelligent Networks, 5, 212-223.
5. Adewopo, V. A., & Elsayed, N. (2024). Smart city transportation: Deep learning ensemble approach for traffic accident detection. IEEE Access.
6. Girija, M., & Divya, V. (2024, April). Road Traffic Accident Prediction using Deep Learning. In 2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS) (pp. 148-159). IEEE.
7. Merolla, D., Latorre, V., Salis, A., & Boanelli, G. (2024). Automated Road Safety: Enhancing Sign and Surface Damage Detection with AI. arXiv preprint arXiv:2407.15406.
8. Nippani, A., Li, D., Ju, H., Koutsopoulos, H., & Zhang, H. (2024). Graph neural networks for road safety modeling: datasets and evaluations for accident analysis. Advances in Neural Information Processing Systems, 36.
9. Hamim, O. F., & Ukkusuri, S. V. (2024). Towards safer streets: A framework for unveiling pedestrians’ perceived road safety using street view imagery. Accident Analysis & Prevention, 195, 107400.
10. Li, F., Cheng, J., Mao, Z., Wang, Y., & Feng, P. (2024). Enhancing safety and efficiency in automated container terminals: Route planning for hazardous material AGV using LSTM neural network and Deep Q-Network. Journal of Intelligent and Connected Vehicles, 7(1), 64-77.
11. Razi, A., Chen, X., Li, H., Wang, H., Russo, B., Chen, Y., & Yu, H. (2023). Deep learning serves traffic safety analysis: A forward‐looking review. IET Intelligent Transport Systems, 17(1), 22-71.
12. Liu, L., Gao, Z., Luo, P., Duan, W., Hu, M., Mohd Arif Zainol, M. R. R., & Zawawi, M. H. (2023). The influence of visual landscapes on road traffic safety: An assessment using remote sensing and deep learning. Remote Sensing, 15(18), 4437.

View PDF