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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64595
Volume:
10 |
6 |
Jun 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master Of Computer Applications
GIET Engineering College (Autonomous)
Master Of Computer Applications
GIET Engineering College (Autonomous)
In educational institutions, students often face various problems related to academics, hostel facilities, transportation, maintenance, internet connectivity, and other campus services. In many colleges, the process of reporting and resolving these issues is still carried out manually, which can be slow, inefficient, and difficult to track. As a result, students may experience delays in getting their problems resolved, while administrators find it challenging to manage complaints effectively.To address these challenges, the Smart Student Issue Resolution System is proposed as a web-based platform that simplifies and automates the complaint management process. The system allows students to register securely using email-based OTP verification, submit complaints, upload supporting images, and monitor the status of their complaints in real time. It also includes an anonymous complaint feature that enables students to report sensitive issues such as ragging, harassment, or misconduct without revealing their identity. The system consists of three main modules: Student, Department, and Admin. Students can submit and track complaints, departments can view and resolve assigned issues, and administrators can monitor the overall complaint management process. The admin also manages department accounts and handles anonymous complaints to ensure privacy and proper action. A major feature of the proposed system is the integration of Natural Language Processing (NLP). NLP techniques are used to analyze complaint content, detect duplicate complaints, identify priority levels, and automatically assign complaints to the appropriate department. Similar complaints are grouped together using a voting mechanism, which helps highlight common issues affecting multiple students. The system also supports automatic escalation of unresolved complaints to higher authorities, ensuring accountability and timely resolution. By combining modern web technologies with intelligent automation, the Smart Student Issue Resolution System improves transparency, reduces response time, minimizes manual effort, and enhances communication between students and institutional departments. The project demonstrates how emerging technologies such as Full-Stack Web Development and Natural Language Processing can be effectively applied to improve administrative processes and student satisfaction within educational institutions.
KEYWORDS
The Smart Student Issue Resolution System provides an intelligent and efficient platform for managing student complaints within educational institutions. By integrating Full-Stack Web Development technologies with Natural Language Processing (NLP), the system automates complaint handling, improves transparency, and reduces the administrative burden associated with traditional complaint management methods. The system enables students to submit complaints easily, track their progress in real time, and receive updates throughout the resolution process. Features such as anonymous reporting, duplicate complaint detection, automatic department assignment, priority classification, and escalation management significantly enhance the overall effectiveness of the platform. These functionalities help ensure that complaints are addressed promptly and routed to the appropriate authorities without unnecessary delays. The platform also improves communication between students, departments, and administrators, creating a more accountable and responsive environment. The use of graphical dashboards and analytical reports allows administrators to monitor complaint trends and departmental performance efficiently. By reducing manual effort and improving workflow management, the system contributes to better service quality and increased student satisfaction. Overall, the proposed system demonstrates how modern web technologies and intelligent automation can be effectively utilized to solve real-world problems in educational institutions. It provides a reliable, transparent, and scalable solution for complaint management while promoting accountability and timely issue resolution. Future enhancements may include mobile application support, AI-powered chatbots for instant assistance, sentiment analysis for better complaint understanding, multilingual complaint processing, predictive analytics for identifying recurring issues, and cloud-based deployment to support larger institutions and multiple campuses.
[1] C. D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing. Cambridge, MA, USA: MIT Press, 1999. [2] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. Sebastopol, CA, USA: O'Reilly Media, 2009. [3] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [4] M. Fowler, Patterns of Enterprise Application Architecture. Boston, MA, USA: Addison-Wesley, 2002. [5] MongoDB Inc., MongoDB Documentation. [Online]. Available: https://www.mongodb.com/docs/. [Accessed: Jun. 05, 2026]. [6] Meta Platforms, Inc., React Documentation. [Online]. Available: https://react.dev/. [Accessed: Jun. 05, 2026]. [7] OpenJS Foundation, Node.js Documentation. [Online]. Available: https://nodejs.org/docs/. [Accessed: Jun. 05, 2026]. [8] OWASP Foundation, OWASP Web Application Security Guidelines. [Online]. Available: https://owasp.org/. [Accessed: Jun. 05, 2026]. [9] National Institute of Standards and Technology (NIST), Cybersecurity Framework (CSF). [Online]. Available: https://www.nist.gov/cyberframework. [Accessed: Jun. 05, 2026]. [10] Various Authors, “Intelligent Complaint Management Systems and Natural Language Processing Applications,” IEEE Xplore Digital Library. [Online]. Available: https://ieeexplore.ieee.org/. [Accessed: Jun. 05, 2026].
Publication Date
6/6/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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