This volume constitutes the proceedings of the 8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013, held in Salamanca, Spain, in September 2013. The 68 papers published in this volume were carefully reviewed and selected from 218 submissions. They are organized in topical sessions on Agents and Multi Agents Systems; HAIS Applications; Classification and Cluster Analysis; Data Mining and Knowledge Discovery; Video and Image Analysis; Bio-inspired Models and Evolutionary Computation; Learning Algorithms; Systems, MAN, and Cybernetics; Hybrid Intelligent Systems for Data Mining and Applications; Metaheuristics for Combinatorial Optimization and Modelling Complex Systems.

The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised full papers presented were carefully reviewed and selected from numerous submissions. The contributions are structured in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; learning algorithms; optimization; pattern recognition; vision; image processing; information processing; neurocontrol; and novel applications.

The two-volume set LNCS 7367 and 7368 constitutes the refereed proceedings of the 9th International Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised full papers presented were carefully reviewed and selected from numerous submissions. The contributions are structured in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; learning algorithms; optimization; pattern recognition; vision; image processing; information processing; neurocontrol; and novel applications.

This volume is part of the two-volume proceedings of the 19th International Conf- ence on Artificial Neural Networks (ICANN 2009), which was held in Cyprus during September 14-17, 2009. The ICANN conference is an annual meeting sp- sored by the European Neural Network Society (ENNS), in cooperation with the - ternational Neural Network Society (INNS) and the Japanese Neural Network Society (JNNS). ICANN 2009 was technically sponsored by the IEEE Computational Intel- gence Society. This series of conferences has been held annually since 1991 in various European countries and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing structure inspired by biological nervous systems. They consist of a large number of highly interconnected processing elements, with the capability of learning by example. The field of artificial neural networks has evolved significantly in the last two decades, with active partici- tion from diverse fields, such as engineering, computer science, mathematics, artificial intelligence, system theory, biology, operations research, and neuroscience. Artificial neural networks have been widely applied for pattern recognition, control, optimization, image processing, classification, signal processing, etc.

This volume is part of the two-volume proceedings of the 19th International Conf- ence on Artificial Neural Networks (ICANN 2009), which was held in Cyprus during September 14-17, 2009. The ICANN conference is an annual meeting sp- sored by the European Neural Network Society (ENNS), in cooperation with the - ternational Neural Network Society (INNS) and the Japanese Neural Network Society (JNNS). ICANN 2009 was technically sponsored by the IEEE Computational Intel- gence Society. This series of conferences has been held annually since 1991 in various European countries and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing structure inspired by biological nervous systems. They consist of a large number of highly interconnected processing elements, with the capability of learning by example. The field of artificial neural networks has evolved significantly in the last two decades, with active partici- tion from diverse fields, such as engineering, computer science, mathematics, artificial intelligence, system theory, biology, operations research, and neuroscience. Artificial neural networks have been widely applied for pattern recognition, control, optimization, image processing, classification, signal processing, etc.

This book constitutes revised selected papers from the 9th International Conference on Critical Information Infrastructures Security, CRITIS 2014, held in Limassol, Cyprus, in October 2014. The 20 full and 19 short papers presented in this volume were carefully reviewed and selected from 74 submissions. They are organized in topical sections named: cyber-physical systems and sensor networks; security of water systems; power and energy system security; security and recovery policies, cyber security; and security tools and protocols.

Recent advances in information and communication technologies, embedded systems and sensor networks have generated significant research activity in the development of so-called cyber-physical systems. An example of a large network of cyber-physical systems is a smart city with intelligent infrastructures for supporting the environment, energy and water distribution, transportation, telecommunication, health care, home automation, and so on. From a systems point of view, safety, reliability and fault tolerance become key challenges in designing cyber-physical systems. One of the major issues is detecting and correcting faults in the sensors that form a critical part of these networks and systems. For example, if two sensors should provide similar information, how do you know which one is at fault should their readings suddenly greatly differ? Sensor Fault Diagnosis addresses all the issues in sensor fault detection and isolation. It provides a clear tutorial on the challenges and models that can be used to address them. It describes, in detail, the requirements for modeling the systems, designing the architecture, detecting faults, isolating those faults, and presents learning techniques for enhancing performance. This monograph will appeal to all researchers and students working on large sensor networks and systems.