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Dieses forschungsorientierte Buch enthält wichtige Beiträge zur Gestaltung der digitalen Transformation. Es umfasst die folgenden Hauptabschnitte in 20 Kapiteln:- Digitale Transformation- Digitales Geschäft- Digitale Architektur- Entscheidungshilfe- Digitale Anwendungen Es konzentriert sich auf digitale Architekturen für intelligente digitale Produkte und Dienstleistungen und ist eine wertvolle Ressource für Forscher, Doktoranden, Postgraduierte, Absolventen, Studenten, Akademiker und Praktiker, die sich für die digitale Transformation interessieren.

This book provides essential future directions for IoT and Big Data research. Thus, there is a new global interest in these applications in various domains such as health, agriculture, energy, security and retail.

Deep Architectures in Visual Transfer Learning.- Deep Reinforcement Learning: A New Frontier in Computer Vision Research.- Deep Learning for Data-driven Predictive Maintenance.- Multi-Criteria Fuzzy Goal Programming under Multi-Uncertainty.- Skeleton-based Human Action Recognition on Large-Scale Datasets.

This book explores the use of a socio-inspired optimization algorithm (the Cohort Intelligence algorithm), along with Cognitive Computing and a Multi-Random Start Local Search optimization algorithm.

This book focuses on the use of The Internet of Things (IoT) and big data in business intelligence, data management, Hadoop, machine learning, cloud, smart cities, etc. IoT and big data emerged from the early 2000s data boom, driven forward by many of the early internet and technology companies. The Internet of Things (IoT) is an interconnection of several devices, networks, technologies, and human resources to achieve a common goal. There are a variety of IoT-based applications being used in different sectors and have succeeded in providing huge benefits to the users. The generation of big data by IoT has ruptured the existing data processing capacity of IoT and recommends to adopt the data analytics to strengthen solutions. The success of IoT depends upon the influential association of big data analytics. New technologies like search engines, mobile devices, and industrial machines provided as much data as companies could handle-and the scale continues to grow. In a study conducted by IDC, the market intelligence firm estimated that the global production of data would grow 10x between 2015 and 2020. So, the proposed book covers up all the aspects in the field discuss above.

This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas: Urban environment monitoring Intelligent mobility Waste recycling processes Video surveillance Computer-aided diagnose in healthcare systems Computer vision-based approaches for efficiency in production processesThe book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field. 

This book presents recent research in decision making under uncertainty, in particular reinforcement learning and learning with expert advice. The core elements of decision theory, Markov decision processes and reinforcement learning have not been previously collected in a concise volume. Our aim with this book was to provide a solid theoretical foundation with elementary proofs of the most important theorems in the field, all collected in one place, and not typically found in introductory textbooks.  This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning.  

This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the healthcare institutions and hospitals for practical analysis, storage, and data analysis. It explores the distinct nature-inspired optimization-based approaches that are able to handle more accurate outcomes for the current biomedical and healthcare problems. In addition to providing a state-of-the-art and advanced intelligent methods, it also enlightens an insight for solving diversified healthcare problems such as cancer and diabetes.

This book aims to provide a refreshing, friendly and exciting manner artificial intelligence (AI) theoretical concepts and practical methods applied in obstetrics and gynaecology. The book follows the nine and a half months journey from preconception till birth alongside with AI. It discusses topics such as the poignant role of AI in improving the change of women getting pregnant; AI methods for detecting congenital anomalies in first and second trimester foetal sonography; how AI aids physicians in determining what type of birth should be deployed (vaginal versus caesarean); how AI can predict pre-eclampsia, preterm birth, mortality, birth weight, miscarriage, postpartum depression, etc. Additionally, it provides information on AI used for perinatal depression, for the evaluation of the relationship between pollutants and pregnancy outcome and even how AI tools can improve physician training in labour and delivery. The book is designed for bioinformaticians, obstetric and gynaecology researchers and physicians, and all those who wish to learn how to explore, analyse, find novel potential solutions for the challenging domains: obstetrics and gynaecology. Likewise, this book will be useful for application engineers who wish to use AI paradigms in areas such as engineering and science too.

This book presents different techniques and methodologies used to improve the intelligent decision-making process and increase the likelihood of success in companies of different sectors such as Financial Services, Education, Supply Chain, Energy Systems, Health Services, and others. The book contains and consolidates innovative and high-quality research contributions regarding the implementation of techniques and methodologies applied in different sectors. The scope is to disseminate current trends knowledge in the implementation of artificial intelligence techniques and methodologies in different fields such as: Logistics, Software Development, Big Data, Internet of Things, Simulation, among others. The book contents are useful for Ph.D. researchers, Ph.D. students, master and undergraduate students of different areas such as Industrial Engineering, Computer Science, Information Systems, Data Analytics, and others.

The book aims at presenting a multidisciplinary view meant to illustrate several significant efforts and results about the contribution of information technologies to make available new resources and enable rationally usage the existing ones in the context of the ever-growing trends to use ever more resources. Authors from various countries have been invited to contribute so that a rather broad and balanced image about the current trends and recent results obtained could result. The proposed book addresses methodologies and information technology-based tools and systems designed for the efficient use of diverse resources such as manpower and human knowledge, natural resources including water, raw materials and end of life products, financial assets, datasets, and even cultural goods. The book is organized in ten chapters. It is intended to be insightful for researchers, instructors, and planers from various domains. It can also be used as an auxiliary material for postgraduate studies in applied informatics, business administration, industrial engineering, engineering and management, computer, and digital humanities.

This book states that data users often suffer from the difficulty of acquiring knowledge for decision-making, and others are unsure how existing data are useful. The reader will be released from these dilemmas and enabled to act beyond patterns in past events by creating a process to interact with the data market and the dynamic real-world rich in new events.We present new approaches from the aspects of computation, communication, and their integration, to readers including analysts in sciences and businesses, systems managers, and learners desiring to design knowledge to learn. We show clues to explaining causalities in the target world of a black-box AI of which users may seek a predictive performance. For obtaining interpretable knowledge, we show the integration of model- and data-driven approaches, the analysis and perception of signals from data acquired in the cyber or the real word, and creative communication which connects demands to data by visualizing the data market as a place for innovations

This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.

This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects - one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products - help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers¿ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life.

This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems.In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.

Why AI does not include gender in its agenda? The role of gender in AI, both as part of the community of agents creating such technologies, as well as part of the contents processed by such technologies is, by far, conflictive. Women have been, again, obliterated by this fundamental revolution of our century. Highly innovative and the first step in a series of future studies in this field, this book covers several voices, topics, and perspectives that allow the reader to understand the necessity to include into the AI research agenda such points of view and also to attract more women to this field. The multi-disciplinarity of the contributors, which uses plain language to show the current situation in this field, is a fundamental aspect of the value of this book. Any reader with a genuine interest in the present and future of AI should read it.

This book reconsiders key issues, such as description and explanation, which affect data analytics. For starters: the soul does not exist. Once released from this cumbersome roommate, we are left with complex biological systems: namely, ourselves, who must configure their environment in terms of worlds that are compatible with what they sense. Far from supplying yet another cosmogony, the book provides the cultivated reader with computational tools for describing and understanding data arising from his surroundings, such as climate parameters or stock market trends, even the win/defeat story of his son football team. Besides the superposition of the very many universes considered by quantum mechanics, we aim to manage families of worlds that may have generated those data through the key feature of their compatibility. Starting from a sharp engineering of ourselves in term of pairs consisting of genome plus a neuron ensemble, we toss this feature in different cognitive frameworks within a span of exploitations ranging from probability distributions to the latest implementations of machine learning. From the perspective of human society as an ensemble of the above pairs, the book also provides scientific tools for analyzing the benefits and drawbacks of the modern paradigm of the world as a service.

This book provides an overview about the open challenges in software verification. Software verification is a branch of software engineering aiming at guaranteeing that software applications satisfy some requirements of interest. Over the years, the software verification community has proposed and considered several techniques: abstract interpretation, data-flow analysis, type systems, model checking are just a few examples. The theoretical advances have been always motivated by practical challenges that have led to an equal evolution of both these sides of software verification. Indeed, several verification tools have been proposed by the research community and any software application, in order to guarantee that certain software requirements are met, needs to integrate a verification phase in its life cycle, independently of the context of application or software size. This book is aimed at collecting contributions discussing recent advances in facing open challenges in software verification, relying on a broad spectrum of verification techniques. This book collects contributions ranging from theoretical to practical arguments, and it is aimed at both researchers in software verification and their practitioners.