Forensic Audio Classifier Tool is an ML-based digital forensics system built using PyTorch, Transformers, and a custom hybrid pipeline (Acoustic Model + Language Model + Classifier). It is designed for the Tripura Bengali dialect, enabling accurate transcription, keyword detection, and automated (Flagged / Review / Safe) audio classification.

This project develops a fraud detection dashboard that allows user to overview all important KPIs, fraudulent transactions and fraud network.

Design and Implementation of Crime Report Management System

This project aims to perform topic modeling on a dataset of crime-related news articles in New York City using **Latent Dirichlet Allocation (LDA) and Mallet's LDA Model. The articles were collected over a two-month period through continuous querying using the News API.

Face recognition software to detect criminals in images and videos, noting their time of occurrences.

This project aims to perform topic modeling on a dataset of crime-related news articles in New York City using **Latent Dirichlet Allocation (LDA) and Mallet's LDA Model. The articles were collected over a two-month period through continuous querying using the News API.

A crime detection and campus management system that helps in the detection, classification and subsequent mitigation of crimes occurring in a region of surveillance.

Real‑time crime detection system using VideoMAE, Flask, async camera streaming, alert snapshots, and email notifications.

DL-powered surveillance system using SlowFast deep learning networks for real-time crime detection, motion analysis, and alert generation from live CCTV footage with GUI and web app support.

Real Time Detection of Anomalous Activity From Videos (mainly crime actvity). Images of the video is trained using AutoEncoder to get the imtermediate feature representation of image & applied svm model for the bag of such features to detect the anomaly & LSTM to detect the type of Anomaly.

Face recognition software to detect criminals in images and videos, noting their time of occurences.