A deep learning-based web application for deepfake video detection, powered by the fine-tuned XceptionNet (Extreme Inception) model. The system allows users to upload videos for deepfake detection, processes them through the trained model, and provides results via a clean Django-based web interface.

🧠 ML training pipeline within our MLOps architecture. Manages the training, evaluation, and versioning of the CatBoost model.

Machine learning models for credit card fraud detection with baseline vs SMOTE comparison, evaluated using Recall, Precision, and F1-score.

A sample website integrating the ComplyCube SDK.

This project uses machine learning models like Logistic Regression, Random Forest, and XGBoost to detect fraudulent credit card transactions. It handles class imbalance using SMOTE and visualizes key fraud patterns through an interactive Power BI dashboard.

Machine learning model to detect fraudulent mass subscriptions by analyzing user behavior patterns like tab switching, typing speed, and navigation activity.

An AI-powered fraud detection system that uses machine learning to detect suspicious financial transactions in real time. Features include interactive dashboards, secure authentication, and comprehensive reporting for fintech risk analysis.

🎯 A comprehensive Bank Identification Number (BIN) intelligence platform for e-commerce fraud detection and prevention.

Machine learning system for credit card fraud detection using XGBoost with advanced feature engineering

This project implements an end-to-end pipeline for detecting SMS spam using LLM-based embeddings (Mistral), interpretable machine learning, and risk-aware reporting.

Fraud detection analysis

Iraboti 🌐 Transparent Lottery Software 🌐 Choose winners with confidence using this open-source, tamper-proof lottery software 🌐 Live Preview at https://tawhidurrahmandear.github.io/iraboti

I created a website using Flask framework for detecting financial fraud using Machine Learning Model

🛡️ SecureCard-AI: A high-performance credit card fraud detection system implemented in a Jupyter Notebook, achieving 99.97% accuracy.

This project is an Insurance Workflow Management System designed to streamline policy management, claims processing, and fraud detection. It includes user account management, customer feedback analysis via NLP, alert notifications through SMS, and a fraud detection model, providing a secure, efficient solution for insurance operations.

The objective of this project is to develop a robust classification model capable of identifying and flagging potentially fraudulent job postings on LinkedIn.

Machine Learning project to detect fraudulent transactions on ethereum blockchain, reducing false positives