AE Based

Autoencoders (AEs), neural networks designed for dimensionality reduction and data reconstruction, are being extensively researched for diverse applications. Current research focuses on improving AE performance in anomaly detection across various data types (images, time series, acoustic signals), often incorporating multimodal inputs and advanced architectures like convolutional and recurrent neural networks. This work aims to enhance the theoretical understanding of AEs and develop more robust and efficient algorithms for tasks ranging from medical diagnosis and industrial monitoring to communication system optimization and environmental monitoring. The resulting improvements have significant implications for various fields, enabling more accurate and reliable analysis of complex data.