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  • thema EDItEUR::U Computing and Information Technology::UN Databases  (6)
  • Springer International Publishing  (6)
  • English  (6)
  • French
  • Japanese
  • Polish
  • 2020-2024  (6)
  • 1980-1984
  • 2022  (6)
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  • English  (6)
  • French
  • Japanese
  • Polish
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  • 2020-2024  (6)
  • 1980-1984
Year
  • 1
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    Big Data and Artificial Intelligence in Digital Finance (2022)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-11
    Description: This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance.
    Keywords: Artificial Intelligence and Big Data ; Digital Finance ; FinTech ; Blockchain ; RegTech ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence ; thema EDItEUR::U Computing and Information Technology::UN Databases ; thema EDItEUR::K Economics, Finance, Business and Management::KJ Business and Management::KJQ Business mathematics and systems
    Language: English
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  • 2
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    Deep Neural Networks and Data for Automated Driving (2022)
    Springer Nature | Springer International Publishing
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    Publication Date: 2024-04-14
    Description: This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
    Keywords: Highly Automated Driving ; Autonomous Driving ; Environment Perception ; Deep Learning ; Safety ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades::TRC Automotive technology and trades ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYT Image processing ; thema EDItEUR::U Computing and Information Technology::UN Databases
    Language: English
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  • 3
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    Data Science-Based Full-Lifespan Management of Lithium-Ion Battery (2022)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-11
    Description: This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers.
    Keywords: Lithium-ion Battery ; Battery Manufacturing Management ; Battery Operation Management ; Battery Recycling Management ; Data Science ; Artificial Intelligence ; Open Access ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGM Materials science ; thema EDItEUR::U Computing and Information Technology::UN Databases ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering
    Language: English
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  • 4
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    Technologies and Applications for Big Data Value (2022)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-14
    Description: This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.
    Keywords: Big Data ; Data Management ; Data Processing ; Data Analytics ; Data Visualisation and User Interaction ; Knowledge Discovery ; Information Retrieval ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining ; thema EDItEUR::U Computing and Information Technology::UN Databases ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics ; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems
    Language: English
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  • 5
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    Data Spaces (2022)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-14
    Description: This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical.
    Keywords: Data Platforms ; Data Spaces ; Data Sharing ; Data Lifecycle ; Platform Architectures ; Data Life Cycle ; Regulatory Compliance ; thema EDItEUR::U Computing and Information Technology::UN Databases ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics ; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYD Systems analysis and design ; thema EDItEUR::U Computing and Information Technology::UR Computer security
    Language: English
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  • 6
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    The Road to General Intelligence (2022)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-14
    Description: Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book.
    Keywords: Computational Intelligence ; Artificial Intelligence ; Machine Learning ; General Intelligence ; AI ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence ; thema EDItEUR::U Computing and Information Technology::UN Databases
    Language: English
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