International Joint Conference Soco 17 Cisis 17 Iceute 17 Le N Spain September 6 8 2017 Proceeding

This volume includes papers presented at SOCO 2017, CISIS 2017, and ICEUTE 2017, all conferences held in the beautiful and historic city of León (Spain) in September 2017. Soft computing represents a collection of computational techniques in machine learning, computer science, and some engineering disciplines, which investigate, simulate, and analyze highly complex issues and phenomena. These proceeding s feature 48 papers from the 12th SOCO 2017, covering topics such as artificial intelligence and machine learning applied to health sciences; and soft computing methods in manufacturing and management systems. The book also presents 18 papers from the 10th CISIS 2017, which provided a platform for researchers from the fields of computational intelligence, information security, and data mining to meet and discuss the need for intelligent, flexible behavior by large, complex systems, especially in mission-critical domains. It addresses various topics, like identification, simulation and prevention of security and privacy threats in modern communication networks Furthermore, the book includes 8 papers from the 8th ICEUTE 2017. The selection of papers for all three conferences was extremely rigorous in order to maintain the high quality and we would like to thank the members of the Program Committees for their hard work in the reviewing process.

This book includes papers presented at SOCO 2018, CISIS 2018 and ICEUTE 2018, all held in the beautiful and historic city of San Sebastian (Spain), in June 2018. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze highly complex issues and phenomena. After a rigorous peer-review process, the 13th SOCO 2018 International Program Committee selected 41 papers, with a special emphasis on optimization, modeling and control using soft computing techniques and soft computing applications in the field of industrial and environmental enterprises. The aim of the 11th CISIS 2018 conference was to offer a meeting opportunity for academic and industry researchers from the vast areas of computational intelligence, information security, and data mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, was the catalyst for the overall event.Eight of the papers included in the book were selected by the CISIS 2018 International Program Committee. The International Program Committee of ICEUTE 2018 selected 11 papers for inclusion in these conference proceedings.

This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.

This book presents algorithms and computational applications integrated in software that are being applied in the industry. It shows how companies using these tools are more competitive and efficient in the use and resources management. The book is organized in three sections, depending on the supply chain stage: procurement, including contact with costumers and product design; Production process, including relationship with suppliers and among departments; and Distribution, including logistics and transportation.

As the demand for efficient energy sources continues to grow, electrical systems are becoming more essential to meet these increased needs. Electrical generation and transmission plans must remain cost-effective, reliable, and flexible for further future expansion. As these systems are being utilized more frequently, it becomes imperative to find ways of optimizing their overall function. Novel Advancements in Electrical Power Planning and Performance is an essential reference source that provides vital research on the specific challenges, issues, strategies, and solutions that are associated with electrical transmission and distribution systems and features emergent methods and research in the systemic and strategic planning of energy usage. Featuring research on topics such as probabilistic modeling, voltage stability, and radial distribution, this book is ideally designed for electrical engineers, practitioners, power plant managers, investors, industry professionals, researchers, academicians, and students seeking coverage on the methods and profitability of electrical expansion planning.

This book highlights recent research on intelligent systems and nature-inspired computing. It presents 62 selected papers from the 19th International Conference on Intelligent Systems Design and Applications (ISDA 2019), which was held online. The ISDA is a premier conference in the field of computational intelligence, and the latest installment brought together researchers, engineers and practitioners whose work involves intelligent systems and their applications in industry. Including contributions by authors from 33 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.

This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.

In a series of essays, this book describes and analyzes the concept and theory of the recent smart city phenomenon from a global perspective, with a focus on its implementation around the world. After defining the concept it then elaborates on the role of Information and Communication Technology (ICT) as an enabler for smart cities, and the role of ICT in the interplay with smart mobility. A separate chapter develops the concept of an urban smart dashboard for stakeholders to measure performance as well as the economic and public value. It offers examples of smart cities around the globe, and two detailed case studies on Genoa and Amsterdam exemplify the book’s theoretical and empirical findings, helping readers understand and evaluate the effectiveness and capability of new smart city programs.