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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
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Volume Published : 11
Issue Published : 120
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| Paper Title: | AN INTELLIGENT AI-ENABLED SYSTEM FOR LARGE-SCALE PETITION ANALYSIS, REDUNDANT GRIEVANCE DETECTION, AND TRANSPARENT RESOLUTION TRACKING |
| Authors Name: | GAYATHRI M , SIVAGURU R , ARUTSELVAN A , BALASAKTHI K , INDHUVADHANI Y |
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IJRTI_211410
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| Published Paper Id: | IJRTI2604323 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | This study proposes an intelligent and scalable system aimed at transforming large-scale petition and grievance management by significantly reducing manual processing efforts, efficiently handling redundant or duplicate submissions, and enhancing transparency within citizen-centric governance platforms. The proposed framework integrates advanced artificial intelligence and machine learning techniques to enable a more structured and intelligent approach to petition processing. It facilitates interactive petition submission through user-friendly interfaces while incorporating natural language understanding to semantically analyze grievance content, ensuring that the system accurately interprets the intent and context of each complaint. Furthermore, it includes a robust duplicate detection mechanism that identifies similar or repeated grievances using semantic similarity measures, thereby preventing redundancy and optimizing resource utilization. The system also prioritizes petitions based on urgency, severity, and historical data patterns, allowing authorities to address critical issues more effectively and in a timely manner. In addition to text-based analysis, the framework supports image-based evidence validation, where submitted images are processed using deep learning techniques to verify authenticity and strengthen the credibility of reported grievances. Moreover, the system provides real-time resolution tracking, enabling citizens to monitor the progress of their petitions, and offers comprehensive administrative dashboards that present actionable insights, performance metrics, and analytics for decision-makers. By combining these capabilities, the proposed system enhances accountability, improves operational efficiency, and delivers a more transparent, responsive, and user-centric grievance redressal process compared to traditional systems. |
| Keywords: | Artificial Intelligence (AI), Natural Language Processing (NLP), Petition Analysis, Grievance Redressal System, Duplicate Grievance Detection, Semantic Similarity, Conversational Chatbot, RASA Framework, BERT (Bidirectional Encoder Representations from Transformers), XGBoost, ResNet, Image-Based Evidence Validation, Grievance Prioritization, e-Governance, Real-Time Resolution Tracking, Dashboard Analytics, Text Classification, Machine Learning, Deep Learning, Citizen-Centric Governance |
| Cite Article: | "AN INTELLIGENT AI-ENABLED SYSTEM FOR LARGE-SCALE PETITION ANALYSIS, REDUNDANT GRIEVANCE DETECTION, AND TRANSPARENT RESOLUTION TRACKING", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.c389-c393, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604323.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604323
Registration ID:211410
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: c389-c393 Country: Salem, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604323 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604323 |
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ISSN: 2456-3315
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