Ekspertsystemer og vidensbaserede systemer

This book constitutes the refereed proceedings of the 7th IFIP TC 10 International Embedded Systems Symposium, IESS 2022, held in Lippstadt, Germany, during November 3-4, 2022. The 10 full revised papers and 2 short papers presented were carefully reviewed and selected from 13 submissions. The presented research and technical works cover system-level design methods, algorithms, verification and validation techniques, estimation of system properties and characteristics, performance analysis, and real-time systems design. Also, the book presents industrial and real-world application case studies that discuss the challenges and realizations of modern embedded systems, especially when it comes to including artificial intelligence algorithms and techniques in embedded systems.

This book constitutes the refereed proceedings of the 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, held in Portoroz, Slovenia, in June12¿15, 2023.The 23 full papers and 21 short papers presented together with 3 demonstration papers were selected from 108 submissions. The papers are grouped in topical sections on: machine learning and deep learning; explainability and transfer learning; natural language processing; image analysis and signal analysis; data analysis and statistical models; knowledge representation and decision support.

Elevate your problem-solving prowess by using cutting-edge quantum machine learning algorithms in the financial domainPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn to solve financial analysis problems by harnessing quantum powerUnlock the benefits of quantum machine learning and its potential to solve problemsTrain QML to solve portfolio optimization and risk analytics problemsBook DescriptionQuantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems.This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you'll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing.By the end of this book, you'll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling.What you will learnExamine quantum computing frameworks, models, and techniquesGet to grips with QC's impact on financial modelling and simulationsUtilize Qiskit and Pennylane for financial analysesEmploy renowned NISQ algorithms in model buildingDiscover best practices for QML algorithmSolve data mining issues with QML algorithmsWho this book is forThis book is for financial practitioners, quantitative analysts, or developers; looking to bring the power of quantum computing to their organizations. This is an essential resource written for finance professionals, who want to harness the power of quantum computers for solving real-world financial problems. A basic understanding of Python, calculus, linear algebra, and quantum computing is a prerequisite.Table of ContentsQuantum Computing ParadigmQuantum Machine Learning AlgorithmsQuantum Finance LandscapeDerivatives ValuationPortfolio ValuationsCredit Risk AnalyticsImplementation in Quantum CloudsHPCs and Simulators RelevanceNISQ Quantum Hardware EvolutionQuantum Roadmap for Banks and Fintechs

This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode.

This book presents a collection of essays written by leading researchers to honor Roman Slowinski's major scholarly interests and contributions. He is well-known for conducting extensive research on methodologies and techniques for intelligent decision support, where he combines operational research and artificial intelligence. The book reconstructs his main contributions, presents cutting-edge research and provides an outlook on the most promising and advanced domains of computer science and multiple criteria decision aiding. The respective chapters cover a wide range of related research areas, including decision sciences, ordinal data mining, preference learning and multiple criteria decision aiding, modeling of uncertainty and imprecision in decision problems, rough set theory, fuzzy set theory, multi-objective optimization, project scheduling and decision support applications. As such, the book will appeal to researchers and scholars in related fields.

Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors.

Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams takes on the primary challenges of bidirectional trust and performance of autonomous systems, providing readers with a review of the latest literature, the science of autonomy, and a clear path towards the autonomy of human-machine teams and systems. Throughout this book, the intersecting themes of collective intelligence, bidirectional trust, and continual assurance form the challenging and extraordinarily interesting themes which will help lay the groundwork for the audience to not only bridge knowledge gaps, but also to advance this science to develop better solutions. The distinctively different characteristics and features of humans and machines are likely why they have the potential to work well together, overcoming each other's weaknesses through cooperation, synergy, and interdependence which forms a "collective intelligence.? Trust is bidirectional and two-sided; humans need to trust AI technology, but future AI technology may also need to trust humans.

This book constitutes the refereed post-conference proceedings of the 12th EAI International Conference on Mobile Networks and Management, MONAMI 2022, which took place virtually during October 29-31, 2022.The 31 full papers were carefully reviewed and selected from 78 submissions. The papers are divided into groups of content as follows: Innovative Artificial Intelligence Applications for Smart City; The New Era of Computer Network by using Machine Learning; Advanced Technologies in Edge and Fog Computing; Emerging Technologies in Mobile Networks and Management; and Recent Advances in Communications and Computing.

Sentiment analysis deals with extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. Medical sentiment analysis refers to the identification and analysis of sentiments or emotions expressed in free-textual documents with a scope on healthcare and medicine. This fascinating problem offers numerous application areas in the domain of medicine, but also research challenges. The book provides a comprehensive introduction to the topic. The primary purpose is to provide the necessary background on medical sentiment analysis, ranging from a description of the notions of medical sentiment to use cases that have been considered already and application areas of relevance. Medical sentiment analysis uses natural language processing (NLP), text analysis and machine learning to realise the process of extracting and classifying statements regarding expressed opinion and sentiment. The book offers a comprehensive overview on existingmethods of sentiment analysis applied to healthcare resources or health-related documents. It concludes with open research avenues providing researchers indications which topics still have to be developed in more depth.

This book constitutes revised selected papers from the refereed proceedings of the 7th International Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2022, which took place in Potsdam, Germany, on September 6, 2022.The 6 full papers included in this book were carefully reviewed and selected from 16 submissions. They were organized in topical sections as follows: Cloud-Based Urban Mobility Services; New Results in Priority-Based Bin Packing; More Sparking Soundex-based Privacy-Preserving Record Linkage and Privacy Preserving Queries of Shortest Path Distances.

Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations.Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.

This book collects ground-breaking works on the actual and potential impact of big data and data-integrated design for resilient urban environments, including human- and ecology-centred perspectives. Comprehending and designing for urban social, demographic and environmental change is a complex task. Big data, data structuring, data analysis (i.e. AI and ML) and data-integrated design can play a significant role in advancing approaches to this task. The themes presented in this book include urban adaptation, urban morphology, urban mobility, urban ecosystems, urban climate, urban ecology and agriculture. Given the compound nature of complex sustainability problems, most chapters address the correlation between several of these themes. The book addresses practitioners, researchers and graduate students concerned with the rapidly increasing role of data in developing urban environments.

This volume represents the 20th International Conference on Information Technology - New Generations (ITNG), 2023. ITNG is an annual event focusing on state of the art technologies pertaining to digital information and communications. The applications of advanced information technology to such domains as astronomy, biology, education, geosciences, security, and health care are the among topics of relevance to ITNG. Visionary ideas, theoretical and experimental results, as well as prototypes, designs, and tools that help the information readily flow to the user are of special interest. Machine Learning, Robotics, High Performance Computing, and Innovative Methods of Computing are examples of related topics. The conference features keynote speakers, a best student award, poster award, service award, a technical open panel, and workshops/exhibits from industry, government and academia. This publication is unique as it captures modern trends in IT with a balance of theoretical and experimental work. Most other work focus either on theoretical or experimental, but not both. Accordingly, we do not know of any competitive literature.

This book focuses on the observability of hybrid systems. It enables the reader to determine whether and how a hybrid system¿s state can be reconstructed from sometimes necessarily partial information. By explaining how available measurements can be used to deduce past and future behaviours of a system, the authors extend this study of observability to embrace the properties of diagnosability and predictability.H-systems shows how continuous and discrete dynamics and their interaction affect the observability of this general class of hybrid systems and demonstrates that hybrid characteristics are not simply generalizations of well-known aspects of traditional dynamics. The authors identify conditions for state reconstruction, prediction and diagnosis of the occurrence of possibly faulty states. The formal approach to proving those properties for hybrid systems is accompanied by simple illustrative examples. For readers who are interested in the use of state estimation for controller design, the book also provides design methods for hybrid state observers and covers their application in some industrial cases.The book¿s tutorial approach to the various forms of observability of hybrid systems helps to make H-systems of interest to academic researchers and graduate students working in control and to practitioners using control in an industrial environment.

In recent years, extensive research has been conducted by eminent mathematicians and engineers whose results and proposed problems are presented in this new volume. It is addressed to graduate students, research mathematicians, physicists, and engineers. Individual contributions are devoted to topics of approximation theory, functional equations and inequalities, fixed point theory, numerical analysis, theory of wavelets, convex analysis, topology, operator theory, differential operators, fractional integral operators, integro-differential equations, ternary algebras, super and hyper relators, variational analysis, discrete mathematics, cryptography, and a variety of applications in interdisciplinary topics. Several of these domains have a strong connection with both theories and problems of linear and nonlinear optimization. The combination of results from various domains provides the reader with a solid, state-of-the-art interdisciplinary reference to theory and problems.  Some of the works provide guidelines for further research and proposals for new directions and open problems with relevant discussions.

This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.

This book constitutes the refereed proceedings of the 25th International Conference on Distributed Computer and Communication Networks, DCCN 2022, held in Moscow, Russia, in September 2022.The 27 full papers and 2 short papers included in this book were carefully reviewed and selected from 130 submissions. They were organized in topical sections as follows: Distributed Systems Applications, Computer and Communication Networks, Analytical Modeling of Distributed Systems.

This book constitutes the refereed proceedings of the 16th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2022, held in Gaeta, Italy, during September 14¿16, 2022. The 21 full papers and 3 short papers included in this book were carefully reviewed and selected from 45 submissions. They were organized in topical sections as follows: answer set programming; networks and complex systems, metaheuristics, robotics, and machine learningChapters 7, 8, and 9 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This volume is the third (III) of four under the main themes of Digitizing Agriculture and Information and Communication Technologies (ICT). The four volumes cover rapidly developing processes including Sensors (I), Data (II), Decision (III), and Actions (IV). Volumes are related to ¿digital transformation¿ within agricultural production and provision systems, and in the context of Smart Farming Technology and Knowledge-based Agriculture. Content spans broadly from data mining and visualization to big data analytics and decision making, alongside with the sustainability aspects stemming from the digital transformation of farming. The four volumes comprise the outcome of the 12th EFITA Congress, also incorporating chapters that originated from select presentations of the Congress. The focus of this book (III) is on the transformation of collected information into valuable decisions and aims to shed light on how best to use digital technologies to reduce cost, inputs,and time, toward becoming more efficient and transparent. Fourteen chapters are grouped into 3 Sections. The first section of is dedicated to decisions in the value chain of agricultural products. The next section, titled Primary Production, elaborates on decision making for the improvement of processes taking place with the farm under the implementation of ICT. The last section is devoted to the development of innovative decision applications that also consider the protection of the environment, recognizing its importance in the preservation and considerate use of resources, as well as the mitigation of adverse impacts that are related to agricultural production.Planning and modeling the assessment of agricultural practices can provide farmers with valuable information prior to the execution of any task. This book provides a valuable reference for them as well as for those directly involved with decision making in planning and assessment of agricultural production.Specific advances covered in the volume: Modelling and Simulation of ICT-based agricultural systemsFarm Management Information Systems (FMIS) Planning for unmanned aerial systems Agri-robotics awareness and planning Smart livestock farming Sustainable strategic planning in agri-production Food business information systems

Get your hands on the secret recipe for a rewarding career in data science from 18 AI leadersPurchase of the print or Kindle book includes a free PDF eBookKey Features:- Gain access to insights and expertise from data science leaders shared in one-on-one interviews- Get pragmatic advice on how to become a successful data scientist and data science leader- Receive guidance to overcome common pitfalls and challenges and ensure your projects' successBook Description:A Gartner prediction in 2018 led to numerous articles stating that "85% of AI and machine learning projects fail to deliver." Although it's unclear whether a mass extinction event occurred for AI implementations at the end of 2022, the question remains: how can I ensure that my project delivers value and doesn't become a statistic?The demand for data scientists has only grown since 2015, when they were dubbed the new "rock stars" of business. But how can you become a data science rock star? As a new senior data leader, how can you build and manage a productive team? And what is the path to becoming a chief data officer?Creators of Intelligence is a collection of in-depth, one-on-one interviews where Dr. Alex Antic, a recognized data science leader, explores the answers to these questions and more with some of the world's leading data science leaders and CDOs.Interviews with: Cortnie Abercrombie, Edward Santow, Kshira Saagar, Charles Martin, Petar Veli¿kovi¿, Kathleen Maley, Kirk Borne, Nikolaj Van Omme, Jason Tamara Widjaja, Jon Whittle, Althea Davis, Igor Halperin, Christina Stathopoulos, Angshuman Ghosh, Maria Milosavljevic, Dr. Meri Rosich, Dat Tran, and Stephane Doyen.What You Will Learn:- Find out where to start with AI ethics and how to evolve from frameworks to practice- Discover tips on building and managing a data science team- Receive advice for organizations seeking to build or mature a data science capability- Stop beating your head against a brick wall - pick the environment that'll support your success- Read stories from successful data leaders as they reflect on the successes and failures in data strategy development- Understand how business areas can best work with data science teams to drive business valueWho this book is for:This book is for a wide range of audience, from people working in the data science industry through to data science leaders and chief data officers. This book will also cater to senior business leaders interested in learning how data and analytics are used to support decision-making in different domains and sectors. Students contemplating a career in artificial intelligence (AI) and the broader data sector will also find this book useful, along with anyone developing and delivering university-level education, including undergraduate, postgraduate, and executive programs.Table of Contents- Introducing the Creators of Intelligence- Cortisone Abercrombie Wants the Truth- Edward Santow vs. Unethical AI- Kshira Saagar Tells a Story- Consulting Insights with Charles Martin- Petar Veli¿kovi¿ and His Deep Network- Kathleen Maley Analyzes the Industry- Kirk Borne Sees the Stars- Nikolaj Van Omme Can Solve Your Problems- Jason Tamara Widjaja and the AI People- Jon Whittle Turns Research into Action- Building the Dream Team with Althea Davis- Igor Halperin Watches the Markets- Christina Stathopoulos Exerts Her Influence- Angshuman Ghosh Leads the Way- Maria Milosavljevic Assesses the Risks- Stephane Doyen Follows the Science- Intelligent Leadership with Meri Rosich- Teaming Up with Dat Tran- Summary

This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today¿s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.

The U.S. no longer has a free marketplace of ideas. Instead, the marketplace is saturated with covert foreign-backed disinformation. And despite the ethical obligations to act, successive administrations have done nothing. Additionally, the decline in trust has left the door open for populism and illiberalism to enter. Some believe the very fabric of American liberalism is at stake. So what are the ethical responsibilities of the executive branch to counter covert campaigns such as the one coming from Russian-backed disinformation circulating within the US? Why has the government failed to act? So far, the practical challenges are daunting if the executive branch addresses the threat to the homeland. The process to limit this problem is wrought with profound political implications. By its very nature, social media-based disinformation is inextricably linked with existing complex societal cleavages, the First Amendment, and politics. But the failure to do anything is a serious abdication of the government¿s ethical responsibilities. This raises the question of where the line is for government intervention. This work provides answers.

This book constitutes the joint refereed proceedings of the 22nd International Conference on Internet of Things, Smart Spaces, and Next Generation Networks and Systems, NEW2AN 2022, held in Tashkent, Uzbekistan, in December 2022.The 58 regular papers presented in this volume were carefully reviewed and selected from 282 submissions. The papers of NEW2AN address various aspects of next-generation data networks, while special attention is given to advanced wireless networking and applications. In particular, the authors have demonstrated novel and innovative approaches to performance and efficiency analysis of 5G and beyond systems, employed game-theoretical formulations, advanced queuing theory, and machine learning. It is also worth mentioning the rich coverage of the Internet of Things, optics, signal processing, as well as digital economy and business aspects.

This book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book is a collection of high-quality peer-reviewed research papers presented in the Sixth International Conference on Computational Intelligence in Data Mining (ICCIDM 2021) held at Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India, during December 11-12, 2021. The book addresses the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

Knowledge Science beschäftigt sich mit Konzepten, Methoden und Prozessen zur systematischen Erzeugung, Extraktion, Speicherung und Bereitstellung von Wissen zur Lösung von Problemen und lässt sich somit dem Wissensmanagement zuordnen. Kognitive Assistenten sorgen dafür, das richtige Wissen zur richtigen Zeit in der richtigen Art und Weise seinen Anwendern und Anwenderinnen bereitzustellen. Damit dies gelingen kann, kommen inzwischen zahlreiche Methoden der Künstlichen Intelligenz (KI) zur Unterstützung unterschiedlicher Aufgaben des Wissensmanagements zum Einsatz.

Discover the revolutionary technology that's changing the way we live, work, and think. From self-driving cars to virtual assistants, artificial intelligence (AI) is transforming the world as we know it. But what exactly is AI, and how does it work?In this comprehensive guide, you'll learn everything you need to know about AI, from its origins and history to the latest developments and breakthroughs. Written by three experts in the field, this book provides a clear and accessible introduction to the concepts and techniques of AI, including machine learning, neural networks, and natural language processing.But this book isn't just about theory - it's also about practice. You'll discover how AI is being used in a wide range of industries, from healthcare and finance to entertainment and transportation. You'll learn about the opportunities and challenges of implementing AI in your own organization, and the ethical considerations that come with creating intelligent machines.Whether you're a student, a professional, or just curious about the world of AI, this book is an essential resource for anyone who wants to understand one of the most exciting and transformative technologies of our time. So why wait? Start exploring the possibilities of AI today!