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Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves—in terms of training, topologies, types, etc.—a similar amount of work has examined their application to a whole host of real-world problems. Such problems are usually difficult to define and hard to solve using conventional techniques. Examples include computer vision, speech recognition, financial applications, medicine, meteorology, robotics, hydrology, etc.This Special Issue focuses on the second of these two research themes, that of the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.

Machine Learning, Artificial Neural Networks, Deep learning, Data Mining

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Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This work explores the potential of using textual patterns for Information Extraction from the World Wide Web.

Information Extraction, Data Mining, Pattern Induction, Web Mining, Machine Learning

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This book is an overview of the different paths automation and control engineering have taken lately, from a modern point of view. Built up with example chapters, this book provides some insight into the use of artificial intelligence and control theory on manufacturing, comfort analysis, reliability of modern digital systems, and the use of unusual reference and feedback signals as those coming from the brain. Nonetheless, some chapters are also devoted to a more traditional point of view of control theory, addressing complex problems where human intervention must be limited. Overall, this book is an effort to show that modern automation and control engineering are comprised by many diverse areas, which should interact in order to provide a complete result. In this way, as the systems become more complex and the control objectives more subjective, both, formal analytic and intelligent approaches, should be seen as complementary tools, not unrelated competitors. This book’s aim is precisely that of showing how broad and diverse the control objectives have become and how the abilities of the control engineer should be extended.

Physical Sciences, Engineering and Technology --- Robotics --- Robot Control --- Machine Learning

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What we call the "color" of an object is the result of a very complicated and not yet fully understood mechanism that involves the interaction between the light reflected by the object surface and the eyes, and the subsequent analysis of the retinal signal by the brain. For these reasons, research into color is intrinsically multidisciplinary and can be conducted from many points of view. The contributions presented in this book reflect this spirit, ranging from rigorous mathematical models of color geometry, to the psychophysical discussion of chromatic visual illusion, passing through state-of-the-art computational models in color enhancement. The idea behind this book is to give the reader an idea of the richness and variety of the research into color, proving it to be one of the most fascinating interdisciplinary research fields in contemporary science.

Color space --- Machine learning --- Contrast enhancement --- Colorization --- Color perception --- Night vision --- Color consistency.

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This open access book brings out the state of the art on how informatics-based tools are used and expected to be used in nanomaterials research. There has been great progress in the area in which “big-data” generated by experiments or computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensive approaches play indispensable roles in advanced materials characterization. "Materials informatics" is the central paradigm in the new trend. "Nanoinformatics" is its essential subset, which focuses on nanostructures of materials such as surfaces, interfaces, dopants, and point defects, playing a critical role in determining materials properties. There have been significant advances in experimental and computational techniques to characterize individual atoms in nanostructures and to gain quantitative information. The collaboration of researchers in materials science and information science is growing actively and is creating a new trend in materials science and engineering.

Machine learning --- Big data --- Atomic resolution characterization --- First-principles calculations --- Nanomaterials synthesis