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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66034
Volume:
10 |
7 |
Jul 2026
Pages:
1-4
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.
Computer Science Engineering
Avinashilingam Instituite of home science and higher education for women,Coimbatore
Computer Science Engineering
Avinashilingam Instituite of home science and higher education for women,Coimbatore
Computer Science Engineering
Avinashilingam Instituite of home science and higher education for women,Coimbatore
Computer Science Engineering
Avinashilingam Instituite of home science and higher education for women,Coimbatore
Civic issue reporting remains a cumbersome process for most citizens, who are often required to navigate multi-step forms, select from rigid category lists, and manually describe the nature and location of a problem. OneTap Civic, presented within the application under the name NagarSeva, is a voice-first, single-problem civic complaint management platform that allows a user to register a complaint by speaking, with an optional photograph for added context. The system automatically captures the user's location, converts the spoken complaint into text, and applies natural language processing to classify the issue into a predefined civic category such as garbage, water, road, or street lighting. A sentiment-driven priority engine estimates the urgency conveyed in the complaint, while a confidence evaluation module gauges the reliability of the automatic classification and flags ambiguous inputs for closer review. The platform also performs location-aware duplicate-issue detection so that repeated reports of the same problem are linked rather than logged separately. An administrative dashboard presents complaints on an interactive map with category filters, status tracking, and downloadable reports, while citizens can track their own submissions from a dedicated tracking view. The bilingual English-Tamil interface and minimal interaction model are intended to widen accessibility for users who find text-heavy reporting systems difficult to use.
KEYWORDS
The proposed system introduces a voice-first intelligent civic complaint platform that simplifies user interaction and enhances administrative efficiency. By integrating automatic classification, sentiment-based prioritisation, duplicate detection, and confidence evaluation, the system ensures accurate and reliable complaint handling. The approach improves accessibility, reduces manual effort for the citizen, and supports more effective decision-making for civic authorities, making it well suited to modern smart-city applications.
REFERENCES [1] R. Khan et al., "Civic Dock: A Smart, Multilingual, Voice-Text-Image Enabled Platform for Effective Civic Complaint Routing and Tracking," in Proc. IEEE Conf., 2025. [2] F. G. David, "FixMyCity: Geo-Based Smart Civic Complaint Management System," Int. J. Scientific Research and Engineering Development (IJSRED), 2021. [3] D. Walwadhar, "Smart Civic Issue Reporting System," Int. J. Advanced Research in Science, Communication and Technology (IJARSCT), 2022. [4] A. Kumar et al., "A Smart City Application for Citizen Complaint Management using NLP," in Proc. IEEE Conf., 2022. [5] M. Patel et al., "Speech Recognition-Based Smart Complaint System," in Proc. IEEE Conf., 2019. [6] S. Reddy et al., "Cloud-Based Framework for Smart Complaint Management System," in Proc. IEEE Conf., 2021. [7] T. Verma et al., "Hybrid Machine Learning Model for Complaint Categorization," IEEE Access, 2022. [8] P. Singh et al., "Sentiment Analysis for Public Service Feedback using Machine Learning," in Proc. IEEE Conf., 2020.
Publication Date
7/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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