This book introduces characteristic features of the protein structure prediction (PSP) problem. It focuses on systematic selection and improvement of the most appropriate metaheuristic algorithm to solve the problem based on a fitness landscape analysis, rather than on the nature of the problem, which was the focus of methodologies in the past. Protein structure prediction is concerned with the question of how to determine the three-dimensional structure of a protein from its primary sequence. Recently a number of successful metaheuristic algorithms have been developed to determine the native structure, which plays an important role in medicine, drug design, and disease prediction. This interdisciplinary book consolidates the concepts most relevant to protein structure prediction (PSP) through global non-convex optimization. It is intended for graduate students from fields such as computer science, engineering, bioinformatics and as a reference for researchers and practitioners.

Two of the most important factors contributing to national and international economy are processing of information for accurate financial forecasting and decision making as well as processing of information for efficient control of manufacturing systems for increased productivity. The associated problems are very complex and conventional methods often fail to produce acceptable solutions. Moreover, businesses and industries always look for superior solutions to boost profitability and productivity. In recent times, artificial neural networks have demonstrated promising results in solving many real-world problems in these domains, and these techniques are increasingly gaining business and industry acceptance among the practitioners. ""Artificial Neural Networks in Finance and Manufacturing"" presents many state-of-the-art and diverse applications to finance and manufacturing, along with underlying neural network theories and architectures. It offers researchers and practitioners the opportunity to access exciting and cutting-edge research focusing on neural network applications, combining two aspects of economic domain in a single and consolidated volume.

This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.

The collected works of Turing, including a substantial amount of unpublished material, will comprise four volumes: Mechanical Intelligence, Pure Mathematics, Morphogenesis and Mathematical Logic. Alan Mathison Turing (1912-1954) was a brilliant man who made major contributions in several areas of science. Today his name is mentioned frequently in philosophical discussions about the nature of Artificial Intelligence. Actually, he was a pioneer researcher in computer architecture and software engineering; his work in pure mathematics and mathematical logic extended considerably further and his last work, on morphogenesis in plants, is also acknowledged as being of the greatest originality and of permanent importance. He was one of the leading figures in Twentieth-century science, a fact which would have been known to the general public sooner but for the British Official Secrets Act, which prevented discussion of his wartime work. What is maybe surprising about these papers is that although they were written decades ago, they address major issues which concern researchers today.

This book enriches our views on representation and deepens our understanding of its different aspects. It arises out of several years of dialog between the editors and the authors, an interdisciplinary team of highly experienced researchers, and it reflects the best contemporary view of representation and reality in humans, other living beings, and intelligent machines. Structured into parts on the cognitive, computational, natural sciences, philosophical, logical, and machine perspectives, a theme of the field and the book is building and presenting networks, and the editors hope that the contributed chapters will spur understanding and collaboration between researchers in domains such as computer science, philosophy, logic, systems theory, engineering, psychology, sociology, anthropology, neuroscience, linguistics, and synthetic biology.

This book focuses on the design of Robotic Flexible Assembly Cell (RFAC) with multi-robots. Its main contribution consists of a new effective strategy for scheduling RFAC in a multi-product assembly environment, in which dynamic status and multi-objective optimization problems occur. The developed strategy, which is based on a combination of advanced solution approaches such as simulation, fuzzy logic, system modeling and the Taguchi optimization method, fills an important knowledge gap in the current literature and paves the way for future research towards the goal of employing flexible assembly systems as effectively as possible despite the complexity of their scheduling.

Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Knowledge, Learning, and Machine Intelligence (D. Michie). Relating Images, Concepts, and Words (D.L. Waltz). Methods for an Expert System to Access and External Database (G.W. Ernst, X. He). Perceptual Representation and Reasoning (B. Chandrasekaran, N.H. Narayanan). Feature Based, Collision Free Inspection Path Planning (F.L. Merat, O. Jeon). Of Using Constraint Logic Programming for Design of Mechanical Parts (L. Sterling). Explanation Facility for Neural Networks (L.F. Pau, T. Goetzsche). CompileTime Type Prediction and Type Checking for Common Lisp Programs (R. Beer). Cognitive Neuroethology (H.J. Chiel). Generating Polytope Intersection Configurations from a Symbolic Description Using CLP (G.M. Radack, M.J. Andersson). Agent (P.J. Drongowski). Index.

This book examines some of the underlying processes behind different forms of information management, including how we store information in our brains, the impact of new technologies such as computers and robots on our efficiency in storing information, and how information is stored in families and in society. The editors brought together experts from a variety of disciplines. While it is generally agreed that information reduces uncertainties and that the ability to store it safely is of vital importance, these authors are open to different meanings of "information": computer science considers the bit as the information block; neuroscience emphasizes the importance of information as sensory inputs that are processed and transformed in the brain; theories in psychology focus more on individual learning and on the acquisition of knowledge; and finally sociology looks at how interpersonal processes within groups or society itself come to the fore. The book will be of value to researchers and students in the areas of information theory, artificial intelligence, and computational neuroscience.

This volumes consists of 59 peer-reviewed papers, presented at the International Conference on Sustainable Design and Manufacturing (SDM-16) held in Chania, Crete Greece in April 2016. Leading-edge research into sustainable design and manufacturing aims to enable the manufacturing industry to grow by adopting more advanced technologies, and at the same time improve its sustainability by reducing its environmental impact. SDM-16 covers a wide range of topics from sustainable product design and service innovation, sustainable process and technology for the manufacturing of sustainable products, sustainable manufacturing systems and enterprises, decision support for sustainability, and the study of societal impact of sustainability including research for circular economy. Application areas are wide and varied. The book will provide an excellent overview of the latest research and development in the area of Sustainable Design and Manufacturing.

This book focuses on small flying drones and their applications in conducting geographic surveys. Scholars and professionals will discover the potential of this tool, and hopefully develop a conceptual and methodological framework for doing the following things: a) Translate their data acquisition needs into specifications. (b) Use the developed specifications to choose the best accessible configuration for their drones, and (c) Design and organize effective and low-cost field deployment and flight operations by integrating technical aspects with regulatory and research requirements. Readers can apply this knowledge to work in cartography, environmental monitoring and analysis, land-use studies and landscape archaeology. Particular attention is also given to the reasons why a drone can dramatically boost a geographer's capability to understand geographic phenomena both from hard-science and humanities-oriented approach.

To sustain and stay at the top of the market and give absolute comfort to the consumers, industries are using different strategies and technologies. Natural language processing (NLP) is a technology widely penetrating the market, irrespective of the industry and domains. It is extensively applied in businesses today, and it is the buzzword in every engineer's life. NLP can be implemented in all those areas where artificial intelligence is applicable either by simplifying the communication process or by refining and analyzing information. Neural machine translation has improved the imitation of professional translations over the years. When applied in neural machine translation, NLP helps educate neural machine networks. This can be used by industries to translate low-impact content including emails, regulatory texts, etc. Such machine translation tools speed up communication with partners while enriching other business interactions. Deep Natural Language Processing and AI Applications for Industry 5.0 provides innovative research on the latest findings, ideas, and applications in fields of interest that fall under the scope of NLP including computational linguistics, deep NLP, web analysis, sentiments analysis for business, and industry perspective. This book covers a wide range of topics such as deep learning, deepfakes, text mining, blockchain technology, and more, making it a crucial text for anyone interested in NLP and artificial intelligence, including academicians, researchers, professionals, industry experts, business analysts, data scientists, data analysts, healthcare system designers, intelligent system designers, practitioners, and students.

This book contains the proceedings of the KES International conferences on Innovation in Medicine and Healthcare (KES-InMed-19) and Intelligent Interactive Multimedia Systems and Services (KES-IIMSS-19), held on 17-19 June 2019 and co-located in St. Julians, on the island of Malta, as part of the KES Smart Digital Futures 2019 multi-theme conference. The major areas covered by KES-InMed-19 include: Digital IT Architecture in Healthcare; Advanced ICT for Medical and Healthcare; Biomedical Engineering, Trends, Research and Technologies and Healthcare Support System. The major areas covered by KES-IIMSS-19 were: Interactive Technologies; Artificial Intelligence and Data Analytics; Intelligent Services and Architectures and Applications. This book is of use to researchers in these vibrant areas, managers, industrialists and anyone wishing to gain an overview of the latest research in these fields.

This book offers a multifaceted perspective on fuzzy set theory, discussing its developments over the last 50 years. It reports on all types of fuzzy sets, from ordinary to hesitant fuzzy sets, with each one explained by its own developers, authoritative scientists well known for their previous works. Highlighting recent theorems and proofs, the book also explores how fuzzy set theory has come to be extensively used in almost all branches of science, including the health sciences, decision science, earth science and the social sciences alike. It presents a wealth of real-world sample applications, from routing problem to robotics, and from agriculture to engineering. By offering a comprehensive, timely and detailed portrait of the field, the book represents an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on new fuzzy set extensions.

Never before was anticipation more relevant to the life and activity of humankind than it is today. "It is no overstatement to suggest that humanity's future will be shaped by its capacity to anticipate...." (Research Agenda for the 21st Century, National Science Foundation). The sciences and the humanities can no longer risk explaining away the complexity and interactivity that lie at the foundation of life and living. The perspective of the world that anticipation opens justifies the descriptor "the post-Cartesian Revolution." If anticipation is a valid research domain, what practical relevance can we await? Indeed, anticipation is more than just the latest catch-word in marketing the apps developed by the digital technology industry. Due to spectacular advances in the study of the living, anticipation can claim a legitimate place in current investigations and applications in the sciences and the humanities. Biology, genetics, medicine, as well as politics and cognitive, behavioral, and social sciences, provide rich evidence of anticipatory processes at work. Readers seeking a foundation for an ticipation will find in these pages recent outcomes pertinent to plant life, political anticipation, cognitive science, architecture, computation. The authors contributing to this volume frame experimental data in language that can be shared among experts from all fields of endeavor. The major characteristic is the inference from the richness of data to principles and practical consequences.

This book addresses agent-based computing, concentrating in particular on evolutionary multi-agent systems (EMAS), which have been developed since 1996 at the AGH University of Science and Technology in Cracow, Poland. It provides the relevant background information on and a detailed description of this computing paradigm, along with key experimental results. Readers will benefit from the insightful discussion, which primarily concerns the efficient implementation of computing frameworks for developing EMAS and similar computing systems, as well as a detailed formal model. Theoretical deliberations demonstrating that computing with EMAS always helps to find the optimal solution are also included, rounding out the coverage.

This is the first book to explore how Semantic Web technologies (SWTs) can be used to create intelligent engineering applications (IEAs). Technology-specific chapters reflect the state of the art in relevant SWTs and offer guidelines on how they can be applied in multi-disciplinary engineering settings characteristic of engineering production systems. In addition, a selection of case studies from various engineering domains demonstrate how SWTs can be used to create IEAs that enable, for example, defect detection or constraint checking. Part I "Background and Requirements of Industrie 4.0 for Semantic Web Solutions" provides the background information needed to understand the book and addresses questions concerning the semantic challenges and requirements of Industrie 4.0, and which key SWT capabilities may be suitable for implementing engineering applications. In turn, Part II "Semantic Web-Enabled Data Integration in Multi-Disciplinary Engineering" focuses on how SWTs can be used for data integration in heterogeneous, multi-disciplinary engineering settings typically encountered in the creation of flexible production systems. Part III "Creating Intelligent Applications for Multi-Disciplinary Engineering" demonstrates how the integrated engineering data can be used to support the creation of IEAs, while Part IV "Related and Emerging Trends in the Use of Semantic Web in Engineering" presents an overview of the broader spectrum of approaches that make use of SWTs to support engineering settings. A final chapter then rounds out the book with an assessment of the strengths, weaknesses and compatibilities of SWTs and an outlook on future opportunities for applying SWTs to create IEAs in flexible industrial production systems. This book seeks to build a bridge between two communities: industrial production on one hand and Semantic Web on the other. Accordingly, stakeholders from both communities should find this book useful in their work. Semantic Web researchers will gain a better understanding of the challenges and requirements of the industrial production domain, offering them guidance in the development of new technologies and solutions for this important application area. In turn, engineers and managers from engineering domains will arrive at a firmer grasp of the benefits and limitations of using SWTs, helping them to select and adopt appropriate SWTs more effectively. In addition, researchers and students interested in industrial production-related issues will gain valuable insights into how and to what extent SWTs can help to address those issues.

Mechanical laws of motion were applied very early for better understanding anthropomorphic action as suggested in advance by Newton "For from hence are easily deduced the forces of machines, which are compounded of wheels, pullies, levers, cords, and weights, ascending directly or obliquely, and other mechanical powers; as also the force of the tendons to move the bones of animals". In the 19th century E.J. Marey and E. Muybridge introduced chronophotography to scientifically investigate animal and human movements. They opened the field of motion analysis by being the first scientists to correlate ground reaction forces with kinetics. Despite of the apparent simplicity of a given skilled movement, the organization of the underlying neuro-musculo-skeletal system remains unknown. A reason is the redundancy of the motor system: a given action can be realized by different muscle and joint activity patterns, and the same underlying activity may give rise to several movements. After the pioneering work of N. Bernstein in the 60's on the existence of motor synergies, numerous researchers "walking on the border" of their disciplines tend to discover laws and principles underlying the human motions and how the brain reduces the redundancy of the system. These synergies represent the fundamental building blocks composing complex movements. In robotics, researchers face the same redundancy and complexity challenges as the researchers in life sciences. This book gathers works of roboticists and researchers in biomechanics in order to promote an interdisciplinary research on anthropomorphic systems at large and on humanoid robotics in particular.

Intelligent agent technology is emerging as one of the most important and rapidly advancing areas. Researchers are developing a number of agent-based applications and multiagent systems in a variety of fields, such as: electronic commerce, supply chain management, resource allocation, intelligent manufacturing, mass customization, industrial control, information retrieval and filtering, collaborative work, mobile commerce, decision support, and computer games. ""Application of Agents and Intelligent Information Technologies"" presents an outstanding collection of the latest research associated with intelligent agents and information technologies. ""Application of Agents and Intelligent Information Technologies"" provides a comprehensive analysis of issues related to agent design, implementation, integration, deployment, evaluation, and business value. This book presents research results and application of agents and other intelligent information technologies in various domains. ""Application of Agents and Intelligent Information Technologies"" offers the intelligent information technologies that will potentially revolutionize the work environment as well as social computing.

This book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership - while others concern more specific business situations (e.g., initial public offering, growth strategies, etc.). The important matter of selecting the right skills and people for an effective team will be extensively explained, and practical ways to recognize them and understanding their personalities will be provided. Finally, few relevant technological future trends will be acknowledged (i.e., IoT, Artificial intelligence, blockchain, etc.), especially for their close relation with the increasing amount of data and our ability to analyse them faster and more effectively.