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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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This book constitutes the thoroughly refereed proceedings of the 9th International Conference on Intelligent Human Computer Interaction, IHCI 2017, held in Evry, France, in December 2017.The 15 papers presented together with three invited papers were carefully reviewed and selected from 25 submissions. The conference is forum for the presentation of technological advances and research results at the crossroads of human-computer interaction, artificial intelligence, signal processing and computer vision.

brain computer interface --- artificial intelligence --- computer networks --- classification databases --- HCI machine learning --- signal processing --- user interfaces

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Radiation oncology is uniquely positioned to harness the power of big data as vast amounts of data are generated at an unprecedented pace for individual patients in imaging studies and radiation treatments worldwide. The big data encountered in the radiotherapy clinic may include patient demographics stored in the electronic medical record (EMR) systems, plan settings and dose volumetric information of the tumors and normal tissues generated by treatment planning systems (TPS), anatomical and functional information from diagnostic and therapeutic imaging modalities (e.g., CT, PET, MRI and kVCBCT) stored in picture archiving and communication systems (PACS), as well as the genomics, proteomics and metabolomics information derived from blood and tissue specimens. Yet, the great potential of big data in radiation oncology has not been fully exploited for the benefits of cancer patients due to a variety of technical hurdles and hardware limitations.With recent development in computer technology, there have been increasing and promising applications of machine learning algorithms involving the big data in radiation oncology. This research topic is intended to present novel technological breakthroughs and state-of-the-art developments in machine learning and data mining in radiation oncology in recent years.

Radiation Oncology --- big data --- machine learning --- artificial intelligence --- deep learning --- personalized medicine --- precision medicine