The problem of non-linear data is one of the oldest in experimental science. The solution to this problem is very complex, since the exact mechanisms that describe a phenomenon and its nonlinearities, are often unknown. At the same time, environmental factors such as the finite precision of the processing machine, noise, and sensor limitations—among others—produce further inaccuracies making even more unfitting the description of the phenomenon described by the collected data. In this context, while developing complex systems, with optimal performance, capable of interacting with the environment in an autonomous way, and showing some form of intelligence, the ultimate solution is to process, identify and recognize such nonlinear dynamics. Problems and challenges in Computational Intelligence (CI) and Information Communication Technologies (ICT) are devoted to implement sophisticated detection, recognition, and signal processing methodologies, to promptly, efficiently and effectively manage such problems. To this aim, neural networks, deep learning networks, genetic algorithms, fuzzy logic, and complex artificial intelligence designs, are favored because of their easy handling of nonlinearities while discovering new data structure, and new original patterns to enhance the efficiency of industrial and economic applications. The collection of chapters presented in this book offer a scenery of the current progresses in such scientific domain.