Anomaly Detection News and Research

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Anomaly detection with AI involves using machine learning or statistical algorithms to identify patterns in data and flag unusual or unexpected observations, often used for fraud detection, system health monitoring, or outlier detection in datasets. These algorithms learn from historical data to predict what is normal and then identify deviations from this norm.
Onboard Earthquake Alert: Safeguarding High-Speed Trains in Korea

Onboard Earthquake Alert: Safeguarding High-Speed Trains in Korea

AI, Blockchain, and IoT: Revolutionizing Power Equipment Safety

AI, Blockchain, and IoT: Revolutionizing Power Equipment Safety

Enhancing Road Safety Using a CNN-LSTM Model for Driver Sleepiness Detection

Enhancing Road Safety Using a CNN-LSTM Model for Driver Sleepiness Detection

AI Fortification: Safeguarding IoT Systems Through Comprehensive Algorithmic Approaches

AI Fortification: Safeguarding IoT Systems Through Comprehensive Algorithmic Approaches

Machine Learning for Precision Broiler Weight Estimation

Machine Learning for Precision Broiler Weight Estimation

Revolutionizing Electric Scooter Safety: Innovative Modules and AI Models Mitigate Accidents

Revolutionizing Electric Scooter Safety: Innovative Modules and AI Models Mitigate Accidents

Leveraging Machine Learning for Enhanced Industrial Control System Cybersecurity

Leveraging Machine Learning for Enhanced Industrial Control System Cybersecurity

Unveiling the Impact of AI and ML on Financial Markets: A Comprehensive Analysis

Unveiling the Impact of AI and ML on Financial Markets: A Comprehensive Analysis

Enhanced Road Manhole Cover Detection Using MGB-YOLO: A Deep Learning Approach

Enhanced Road Manhole Cover Detection Using MGB-YOLO: A Deep Learning Approach

Harnessing Machine Learning for Advancing Offshore Wind Energy

Harnessing Machine Learning for Advancing Offshore Wind Energy

Detecting Advanced Persistent Threats with Machine Learning

Detecting Advanced Persistent Threats with Machine Learning

FL-LoRaMAC: Pioneering Federated Learning for Efficient IoT Anomaly Detection

FL-LoRaMAC: Pioneering Federated Learning for Efficient IoT Anomaly Detection

Automated Visual Crowd Analysis: Advances, Challenges, and Open Problems

Automated Visual Crowd Analysis: Advances, Challenges, and Open Problems

Blockchain-Powered Traceability for Safer Grain and Oil Food Supply Chains

Blockchain-Powered Traceability for Safer Grain and Oil Food Supply Chains

Qualitative eXplainable Graphs: Unveiling Interpretability in Automated Driving

Qualitative eXplainable Graphs: Unveiling Interpretability in Automated Driving

AI and Big Data Revolutionizing Low-Carbon Buildings: Challenges and Promises

AI and Big Data Revolutionizing Low-Carbon Buildings: Challenges and Promises

AI-Powered Threat Hunting for Critical Infrastructure Protection

AI-Powered Threat Hunting for Critical Infrastructure Protection

Enhancing Eco-Friendly Air Pollutant Control in Coal-Powered Plants Using AI

Enhancing Eco-Friendly Air Pollutant Control in Coal-Powered Plants Using AI

Detecting Retail Crime with AI: A Game-Changing Strategy

Detecting Retail Crime with AI: A Game-Changing Strategy

Deep Learning for Enhanced Network Intrusion Detection: Smaller Features, Greater Accuracy

Deep Learning for Enhanced Network Intrusion Detection: Smaller Features, Greater Accuracy

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