QR-Based Smart Public Service Feedback and Priority Management System

Abstract

The AI-based QR code system for public service feedback and priority management aims to improve public grievance redressal operations through better efficiency and transparency and enhanced public service responsiveness. The system enables citizens to submit complaints through text and images by scanning QR codes that public service locations display. The system automatically captures essential metadata such as location ID, department ID, and timestamp to ensure accurate tracking and management. The system uses machine learning techniques to process and classify complaints through Term Frequency–Inverse Document Frequency (TF-IDF) text feature extraction and Logistic Regression complaint classification. The system uses Random Forest algorithm for detecting priority levels and it employs MobileNetV2 for validating images to achieve precise analysis results. The system uses these techniques to classify complaints into different categories which receive corresponding priority designations. The system sends urgent alerts to relevant departments when high-priority complaints occur, while it automatically forwards unresolved cases to higher authorities for prompt resolution. The real-time dashboard presents analytical data that includes total complaints and pending cases along with resolution time, which enhances monitoring capabilities and aids in decision-making processes. The proposed system demonstrates improved response time, enhanced accountability, and increased citizen satisfaction, making it a scalable solution for smart city and e-governance applications.

Introduction

The QR-Based Smart Public Service Feedback and Priority Management System is a smart city solution designed to improve how citizens report and track public service issues such as water leaks, road damage, sanitation problems, and electricity failures. Traditional complaint systems are slow, manual, and lack transparency, often leading to delayed responses and low citizen satisfaction.

To solve this, the system uses QR codes placed in public locations that allow users to quickly submit complaints through their smartphones. Users can enter text, images, and location details, while the system automatically captures metadata like timestamp and location ID for accuracy.

A key feature of the system is its AI-powered complaint processing engine, which:

• Classifies complaints into departments (water, electricity, roads, etc.)
• Predicts priority levels (High, Medium, Low)
• Routes issues automatically to the correct authority
• Enables real-time tracking and status updates for citizens

The system is built using a modular client-server architecture with three main layers:

• User interface for QR-based complaint submission
• Backend processing layer for validation and routing
• AI/NLP layer for classification and priority prediction
• Database layer for secure storage and tracking

Additional features include security mechanisms (encryption, authentication, API protection), scalability for large user loads, and performance optimization for fast response times.

The workflow includes scanning a QR code, submitting a complaint, processing it using NLP and machine learning, assigning category and priority, and forwarding it to the relevant department.

Conclusion

The QR-Based Smart Public Service Feedback and Priority Management System exists as part of this project which operates to improve public grievance redressal processes through better efficiency and transparency and faster response times. The system uses QR code technology together with machine learning and real-time communication to make complaint submission easier while delivering quicker resolution times. The implementation shows that intelligent classification together with priority assignment methods can decrease public complaint handling times. The system achieves two functions by automatically directing complaints to their correct departments while providing immediate response to critical problems. The experimental results show that the system achieves high accuracy in complaint classification and prioritization which makes it a suitable solution for smart city applications. The web-based interface together with the real-time notification system enhances both user experience and trust in public services. The project demonstrates how public service systems can evolve into efficient citizen-friendly platforms through the use of contemporary technologies which include AI and QR-based access systems.

References

[1] K. Jain and M. Singh present the concept of e-governance systems for improving public service delivery through digital platforms (International Journal of Computer Applications, vol. 120, no. 5, pp. 15–20, 2015). [2] The United Nations E-Government Survey demonstrates how digital technologies improve citizen participation and governmental operations (UN Department of Economic and Social Affairs, 2020). [3] T. Ahmed and S. Khan describe a smart complaint management system using mobile and web technologies (International Journal of Advanced Research in Computer Science, vol. 9, no. 2, pp. 45–50, 2018). [4] S. R. Bharathi and P. Karthikeyan study how QR code systems improve access to information and delivery of services (International Journal of Engineering Research & Technology, vol. 7, no. 4, pp. 234–238, 2019). [5] J. Ramos presents the TF-IDF algorithm as a method for text processing and keyword extraction in information retrieval systems (Proceedings of the First Instructional Conference on Machine Learning, 2003). [6] Pedregosa et al. present Scikit-learn, a machine learning library in Python used for classification and prediction tasks (Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011). [7] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O’Reilly Media, 2009. [8] T. Mikolov et al. introduced word embeddings which enable machine learning models to represent text data through efficient text representation. [9] D. Jurafsky and J. H. Martin, Speech and Language Processing, 2nd ed., Pearson, 2009. [10] M. Abadi et al. present TensorFlow, a scalable system for machine learning applications (USENIX Symposium on Operating Systems Design and Implementation, 2016). [11] R. Kitchin explains the concept of data-driven smart cities and their impact on urban management (GeoJournal, vol. 79, no. 1, pp. 1–14, 2014). [12] Zanella et al. discuss Internet of Things (IoT) applications in smart cities for monitoring and automation (IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, 2014). [13] K. Ashton introduces the concept of the Internet of Things (IoT) and its applications in real-world systems (RFID Journal, 2009). [14] M. Janssen and J. Estevez show that big data analytics play a crucial role in enhancing public administration systems according to their research which appears in Government Information Quarterly. [15] M. Zaharia et al. describe Apache Spark for large-scale data processing and analytics (Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016).

Copyright

Copyright © 2026 Mrs. MD. Jareena Begum, Mani Deepika Chatragadda, Sowjanya Bhukya, Sireesha Annapureddy. This 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.