Learning Based Detection

Learning-based detection leverages machine learning algorithms, primarily deep learning architectures like convolutional neural networks (CNNs) and transformers, to automate the identification of patterns and anomalies across diverse data types. Current research focuses on improving detection accuracy and robustness in challenging scenarios, including noisy or incomplete data, and on developing explainable AI methods to enhance transparency and trust. This approach has significant implications for various fields, enabling more efficient and accurate detection in applications ranging from medical diagnosis (e.g., diabetic retinopathy) and cybersecurity (e.g., detecting cyberattacks on UAVs) to financial fraud detection and autonomous driving.