Introduction Atrial fibrillation (AF) is the leading cause of cardioembolic stroke and is associated with increased stroke severity and fatality. Early identification of AF is essential for adequate ...

The Elastic Stack integrated with NetEye offers Machine Learning (ML) jobs that assist in analyzing observability data. These ...

Explore how AI-driven anomaly detection enhances the security of Model Context Protocol (MCP) deployments, protecting AI infrastructure from evolving threats with real-time insights.

Explore strategies for managing combinatorial explosion in high dimensional anomaly detection to enhance data observability ...

Researchers in Slovakia have demonstrated a machine-learning framework that predicts PV inverter output and detects anomalies using only electrical and temporal data, achieving 100% accuracy in ...

Discover how AI-driven anomaly detection safeguards post-quantum context streams in Model Context Protocol (MCP) environments, ensuring robust security for AI infrastructure against future threats.

Researchers from Politecnico di Milano propose a data-driven water leak detection method that treats leaks as anomalies in ...

Multimodal Learning, Deep Learning, Financial Statement Analysis, LSTM, FinBERT, Financial Text Mining, Automated Interpretation, Financial Analytics Share and Cite: Wandwi, G. and Mbekomize, C. (2025 ...

A research team led by Professor Jaesik Choi of KAIST's Kim Jaechul Graduate School of AI, in collaboration with KakaoBank Corp, has developed an accelerated explanation technology that can explain ...

├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...

ABSTRACT: Purpose: The purpose of this study is to develop a scalable, risk-aware artificial intelligence (AI) framework capable of detecting financial fraud in high-throughput digital transaction ...