Multidisciplinary approaches to neural computing

Provides fundamental insights for cross-fertilization: machine learning, artificial neural networks (ANNs) (algorithms and models), social and biometric data for applications in human–computer interactions, and neural networks-based approaches to industrial processes Identifies features from dynamic realistic signal exchanges and invariant machine representations to automatically identify, detect, analyze, and process them in related applications Simplifies automatic signal processing and its exploitation in realistic applications devoted to improving the quality of life of the end users Features contributions from computer science, physics, psychology, statistics, mathematics, electrical engineering, and communication science