International Journal of Science, Engineering and TechnologyAuthors: Master Van Huy Khuat, Master Trong Son Phan, Master Le Phan
Abstract: Grievances systems for e-governance developed traditionally suffers the issues of duplication of complaints, faulty categorization, delays in resolution, heavy workload of manual intervention and others. The intelligent cloud-based complaint management system powered by artificial intelligence, natural language processing, on-device machine learning, and cloud computing is called AGRS-EG, or autonomous grievance redressal system for e-governance. Through an Android application, users will send complaints containing text, images, and location through GPS. A hybrid classification engine that uses NLP for semantic text understanding and TFLite for on-device image classification achieves a 96.3% combined accuracy. The image validation module rejects irrelevant uploads at 89.5% accuracy. The main contribution is the three-level duplicate detection pipeline category filtering, Haversine-based 50-metre geo-proximity filtering, and AI semantic similarity which results in 93.8% duplicate detection accuracy and 4.1% false duplicate. Any duplicate, based on its severity, number, and type, will cause an escalation in priority based on a dynamic system. AI chatbot solves 87.6% of user queries by itself. Using the Google Maps API the administrative dashboard shows possible hot-spots on the map. The processing was 6.9× faster than manual baseline
DOI: https://doi.org/10.5281/zenodo.20457484
