Hidden Pattern

Hidden pattern discovery focuses on identifying and extracting meaningful structures from complex data, often obscured by noise or high dimensionality. Current research employs diverse techniques, including autoencoders (variational and convolutional), Boltzmann machines, wavelet transforms, and matrix profile analysis, to uncover these patterns in various domains, such as language processing, biomedical imaging, and time series analysis. These advancements improve data security, enable more accurate diagnoses (e.g., Parkinson's disease), and facilitate the design of novel materials (e.g., metamaterials), ultimately advancing both scientific understanding and practical applications.