This project is an AI-powered anomaly detection system that monitors social media trends, physical environmental conditions, and stock market behavior in real time. It correlates signals across multiple domains using machine learning and LLMs to detect abnormal patterns and explain why anomalies occur and what they may indicate.

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

Multimodal detection of Alzheimer's disease using the OASIS-1 dataset with CNNs, Anomaly Detection, and Explainable AI (Grad-CAM).

This project applies anomaly detection techniques to government spending patterns using Isolation Forest and SHAP for model explainability. It includes a Streamlit dashboard for interactive data exploration, making it ideal for public sector analytics, compliance use cases, and responsible AI.

AI-powered real-time fraud detection system for identifying money mule accounts in UPI transactions using behavioral and network analysis. Implements risk-based pre-authorization, anomaly detection, and explainable decision logic for proactive fintech security.

This project presents a Domestic AI-Driven Intrusion Detection System, an intelligent solution that safeguards home and small-scale networks using machine learning-based anomaly detection, threat intelligence, and a real-time dashboard for smart, autonomous, and explainable network defense.

An AI-driven Intrusion Prevention System (IPS) integrated with File Integrity Monitoring (FIM) and Network Traffic Analysis that detects and prevents insider threats through real-time anomaly detection, context-aware analysis, and adaptive automated response using Explainable AI (XAI).

FraudBuster is a real-time, pre-authorization payment anomaly detection system using a hybrid rule-based + ML engine. It delivers adaptive risk scoring, explainable AI alerts, and behavioral intelligence to flag suspicious transactions without blocking legitimate users. Built for scalable fintech environments.

HEMM Agentic Predictive Maintenance System Autonomous multi-agent AI system for real-time anomaly detection, failure prediction, and smart maintenance planning for Heavy Earth Moving Machinery (HEMM). Built using LightGBM + intelligent agents + explainability for the Kaggle Agents Intensive Capstone. #AI Agents #Predicitve_Maintenance,

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ML-Based Network Anomaly Detection Detects cyber threats using Random Forest and Isolation Forest on KDD Cup 99 data. Achieves 98% accuracy with SHAP explainability. Includes data preprocessing, model training, and evaluation pipeline. Ideal for intrusion detection systems. #Cybersecurity #MachineLearning #AnomalyDetection

Anomaly detection using Autoencoder with Explainable AI (SHAP)

Anomaly Detection in Network Traffic Using Explainable AI

AI-powered traffic anomaly detection using ML and explainable insights

Network anomaly detection becomes evident as a critical defensive strategy, designed to identify anomalous patterns or behaviors in network traffic that could signify a potential cyberattack

Trustworthy AI-Enhanced DevSecOps Framework: Bridging XAI and RAG for Interpretable Anomaly Detection and Context-Aware Root Cause Analysis

Trustworthy AI-Enhanced DevSecOps Framework: Bridging XAI and RAG for Interpretable Anomaly Detection and Context-Aware Root Cause Analysis

AI-powered anomaly detection system for SCADA networks using LSTM and explainable AI.

Network Anomaly detection along with explainable AI using basic Machine Learning models.

Spacecraft Anomaly Detection with Conditional GANs and Explainable AI using NASA dataset

Anomaly detection in Bitcoin Blockchain transactions using machine learning and Explainable AI

POLICY-AWARE ADAPTIVE EXPENSE ANOMALY DETECTION SYSTEM USING MACHINE LEARNING AND EXPLAINABLE AI

Agentic AI system for detecting fraudulent job postings using transformers, anomaly detection, and explainable GenAI.

Anomaly detection on NBA player performance data using machine learning models and explainable AI techniques.

End-to-end fraud detection system using anomaly detection and explainable AI to analyze public sector spending data.

Explainable AI project using SHAP to interpret solar production anomaly detection with a Random Forest model

🚀 An industry-style fraud detection system with explainable AI and anomaly detection.End-to-end Fraud Detection System using Machine Learning and Explainable AI. Built with Random Forest, Logistic Regression, Isolation Forest, and SHAP, featuring time-based fraud analysis, anomaly detection.

AI-Driven AML Risk & Anomaly Detection System: Developed ML-based risk analysis using Random Forest, Isolation Forest and SHAP to deliver explainable risk scores and anomaly detection.

Agentic AI system for explainable cybersecurity threat detection using machine learning, anomaly detection, and LLM-based reasoning to generate automated incident reports.