CivicConnect: A CNN-Driven Citizen-Centric Governance Platform for Intelligent Civic Issue Prioritization

Harshitha Thota; Pujitha Tammisetti; Pavan Palli; Aakarsh Vanapalli; Sarojini Devi Saladi

CivicConnect: A CNN-Driven Citizen-Centric Governance Platform for Intelligent Civic Issue Prioritization

Author(s)	Ms. Harshitha Thota, Ms. Pujitha Tammisetti, Mr. Pavan Palli, Mr. Aakarsh Vanapalli, Dr. Sarojini Devi Saladi
Country	India
Abstract	The current citizen grievance systems have shown improved levels of accessibility with the help of digital technologies. Nevertheless, they are faced with issues such as inefficient classification of citizen complaints, the absence of intelligent prioritization systems, and the lack of verifiable resolution processes. Accurate classification of the type and priority of the citizen issues based on the data provided by the citizen is still a major problem due to the heterogeneous quality of the images and the changing environment. The current classification system is mostly based on rule-based classification and human interventions. This has led to inefficient response and resolution of the issues. The proposed paper discusses an AI-based citizen-centric governance system called CivicConnect. The system uses the Convolutional Neural Network (CNN) for the classification of citizen complaints. The proposed system processes the geo-tagged citizen complaint images and performs classification based on categories. The system also performs priority-based classification and prioritizes the citizen complaints as high, medium, and low. Additionally, the proposed system performs geo-spatial jurisdiction mapping and role- based workflow management for citizens and officials. The system also performs proof-based resolution with the help of image validation and citizen confirmation. The proposed system uses a custom-built dataset for training the CNN model. The dataset is based on the collection of various types of citizen issues in an urban environment. The dataset is pre-processed with normalization and augmentation. The experimental results show that the proposed system has an accuracy ranging from 78% to 85%. This is much higher than the accuracy of the current manual system.
Keywords	CivicConnect, Smart Governance, Convolutional Neural Network (CNN), Complaint Classification, Priority Prediction, E-Governance, Urban Issue Detection, Geo-Spatial Mapping, Role-Based Workflow, Proof-Based Resolution
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-08

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

CivicConnect: A CNN-Driven Citizen-Centric Governance Platform for Intelligent Civic Issue Prioritization

Share this