Kunstig intelligens

This book constitutes the refereed proceedings of the 5th Multidisciplinary International Symposium on Disinformation in Open Online Media, MISDOOM 2023, which was held in Amsterdam, The Netherlands, during November 21¿22, 2023.The 13 full papers presented in this book were carefully reviewed and selected from 19 submissions. The papers focus on misinformation, disinformation, hate speech, disinformation campaigns, social network analysis, large language models, generative AI, and multi-modal embeddings.

This book studies the intersection between cryptography and AI, highlighting the significant cross-impact and potential between the two technologies. The authors first study the individual ecosystems of cryptography and AI to show the omnipresence of each technology in the ecosystem of the other one. Next, they show how these technologies have come together in collaborative or adversarial ways. In the next section, the authors highlight the coevolution being formed between cryptography and AI. Throughout the book, the authors use evidence from state-of-the-art research to look ahead at the future of the crypto-AI dichotomy. The authors explain how they anticipate that quantum computing will join the dichotomy in near future, augmenting it to a trichotomy. They verify this through two case studies highlighting another scenario wherein crypto, AI and quantum converge. The authors study current trends in chaotic image encryption as well as information-theoretic cryptography and show how these trends lean towards quantum-inspired artificial intelligence (QiAI). After concluding the discussions, the authors suggest future research for interested researchers.

Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry, improving the accuracy of diagnostics. However, the lack Magnetic Resonance Imaging (MRI) data leads to the failure of the depth study algorithm. Medical records are often different because of the cost of obtaining information and the time spent consuming the information. In general, clinical data is unreliable and therefore the training of neural network methods to distribute disease across classes does not yield the desired results. Data augmentation is often done by training data to solve problems caused by augmentation tasks such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue.Data Augmentation and Segmentation imaging using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on an MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome these problems by enabling scientists and clinicians to work on beautiful and realistic images. This can improve diagnosis, prognosis, and disease. Finally, GAN highlights the potential for location of patient information within the data. This is a beneficial clinical application of GAN because it can effectivelyprotect patient confidentiality. This book covers the application of GANs on medical imaging augmentation and segmentation.

This book presents a methodology for the real-time scheduling problems of real-time systems (RTS) from the viewpoint of control theory. Generally, any system can be viewed as an RTS if it performs real-time application functions and behaves correctly depending on given logical activities and satisfying specified deadlines for the activities. This monograph provides broad views and detailed introductions to supervisory control theory (SCT) and its application in real-time scheduling and reconfiguration. Based on three popular SCT modelling frameworks, discrete-event system (DES), timed DES (TDES), and state-tree structures (STS), the authors provide RTS modelling frameworks; thereafter, SCT is used to find their safe execution sequences.As the main contribution, we use (untimed) DES events to represent the execution and preemption of each individual RTS task. This modelling formalism brings the possibilities to model the preemptions of tasks¿ executions. Furthermore, in somecases, priorities cannot be assigned to real-time tasks. In order to solve this problem, a matrix-based priority-free conditional-preemption (PFCP) relation is provided, which generalizes fixed-priority (FP) RTS scheduling. As a natural extension, a generalized modular modelling framework is presented to model the task parameters instead of the global real-time task. The modular models are taken to be generic entities, which also considers the exact execution time of real-time tasks. STS are undoubtedly recognized as a computationally efficient SCT framework which manages the state explosion problem significantly. Hence, building on the (untimed) modular RTS models, a novel STS-based RTS modeling framework is formulated, by assigning dynamic priorities as specified optimality criteria, which can be utilized to model sporadic RTS processing both sporadic and (multi-period) periodic tasks, providing a small set of the safe execution sequences which rank at the top.

This book deals with the advantages of using artificial intelligence (AI) in the fight against the COVID-19 and against future pandemics that could threat humanity and our environment. This book is a practical, scientific and clinically relevant example of how medicine and mathematics will fuse in the 2020s, out of external pandemic pressure and out of scientific evolutionary necessity. This book contains a unique blend of the world's leading researchers, both in medicine, mathematics, computer science, clinical and preclinical medicine, and presents the research front of the usage of AI against pandemics.Equipped with this book the reader will learn about the latest AI advances against COVID-19, and how mathematics and algorithms can aid in preventing its spreading course, treatments, diagnostics, vaccines, clinical management and future evolution.

This book gathers contributions from the fourth edition of the Conference on "e;Philosophy and Theory of Artificial Intelligence"e; (PT-AI), held on 27-28th of September 2021 at Chalmers University of Technology, in Gothenburg, Sweden. It covers topics at the interface between philosophy, cognitive science, ethics and computing. It discusses advanced theories fostering the understanding of human cognition, human autonomy, dignity and morality, and the development of corresponding artificial cognitive structures, analyzing important aspects of the relationship between humans and AI systems, including the ethics of AI. This book offers a thought-provoking snapshot of what is currently going on, and what are the main challenges, in the multidisciplinary field of the philosophy of artificial intelligence. 

This textbook comprehensively covers the latest state-of-the-art methods and applications of artificial intelligence (AI) in medicine, placing these developments into a historical context. Factors that assist or hinder a particular technique to improve patient care from a cognitive informatics perspective are identified and relevant methods and clinical applications in areas including translational bioinformatics and precision medicine are discussed. This approach enables the reader to attain an accurate understanding of the strengths and limitations of these emerging technologies and how they relate to the approaches and systems that preceded them.With topics covered including knowledge-based systems, clinical cognition, machine learning and natural language processing, Intelligent Systems in Medicine and Health: The Role of AI details a range of the latest AI tools and technologies within medicine. Suggested additional readings and review questions reinforce the key points covered and ensure readers can further develop their knowledge. This makes it an indispensable resource for all those seeking up-to-date information on the topic of AI in medicine, and one that provides a sound basis for the development of graduate and undergraduate course materials.

This book presents explainability in edge AI, an amalgamation of edge computing and AI. The issues of transparency, fairness, accountability, explainability, interpretability, data-fusion, and comprehensibility that are significant for edge AI are being addressed in this book through explainable models and techniques. The concept of explainable edge AI is new in front of the academic and research community, and consequently, it will undoubtedly explore multiple research dimensions. The book presents the concept of explainability in edge AI which is the amalgamation of edge computing and AI. In the futuristic computing scenario, the goal of explainable edge AI will be to execute the AI tasks and produce explainable results at the edge. First, this book explains the fundamental concepts of explainable artificial intelligence (XAI), then it describes the concept of explainable edge AI, and finally, it elaborates on the technicalities of explainability in edge AI. Owing to the quick transition in the current computing scenario and integration with the latest AI-based technologies, it is significant to facilitate people-centric computing through explainable edge AI. Explainable edge AI will facilitate enhanced prediction accuracy with the comprehensible decision and traceability of actions performed at the edge and have a significant impact on futuristic computing scenarios. This book is highly relevant to graduate/postgraduate students, academicians, researchers, engineers, professionals, and other personnel working in artificial intelligence, machine learning, and intelligent systems.

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 presents the latest findings and ongoing research in the field of green information systems as well as green information and communication technology (ICT). It provides insights into a whole range of cross-cutting concerns in ICT and environmental sciences and showcases how information and communication technologies allow environmental and energy efficiency issues to be handled effectively. Offering a selection of extended and reworked contributions to the 30th International Conference EnviroInfo 2016, it is essential reading for anyone wanting to extend their expertise in the area. 

Mit Beginn der ersten industriellen Revolution entwickelten sich im Laufe der Zeit sukzessiv neuartige Technologien, deren konvergierendes Zusammenspiel in der gegenwärtigen vierten industriellen Revolution mündeten. Die Mensch-Roboter-Kollaboration (MRK) bildet hierbei ein Interaktionskonzept zwischen Mensch und Roboter in einem gemeinsamen Arbeitsbereich. Das Ziel ist eine Verbesserung menschlicher Arbeitsbedingungen bei gleichzeitiger Produktivitätssteigerung.MRK erfahren derzeit eine revolutionäre Weiterentwicklung von statisch programmierten MRK-Systemen hin zu dynamisch lernenden MRK-Systemen auf Basis von Methoden der Künstlichen Intelligenz (KI). Der Nutzen lässt sich anhand der Dimensionen von Flexibilität, Sicherheit sowie Produktivität und der damit verbundenen Steigerung erkennen. Die Integration von leistungsfähiger schwacher KI lässt zugleich neue arbeitswissenschaftliche Herausforderungen entstehen. Es gilt insbesondere einen ganzheitlichen Ansatz in der soziotechnischen Arbeitsgestaltung zu verfolgen. Unternehmen stehen in diesem Zusammenhanggegenwärtig keine benötigten praxisnahen Handlungsempfehlungen zur Verfügung.Das Werk setzt sich folgerichtig zum Ziel, erstmals einen integrativen arbeitswissenschaftlichen Handlungsrahmen unter Berücksichtigung der soziotechnischen Gestaltungselemente herzuleiten. Dieser dient zur Sensibilisierung, ermöglicht es Verbesserungspotenziale und Handlungsmöglichkeiten eigenständig zu identifizieren und bildet in idealtypischer Form die relevanten arbeitswissenschaftlichen Erfolgsfaktoren und Handlungsfelder ab.

Welcome to "Machine Learning and Algorithms: A Comprehensive Guide." In an age where data is generated at an unprecedented pace and technology continually reshapes the landscape, understanding the fundamental concepts of machine learning and algorithms has become essential.This book is designed to be your companion on a journey through the captivating realm of machine learning and algorithms. Whether you're a student taking your first steps into this exciting field, a professional looking to enhance your skills, or simply curious about the inner workings of the technologies shaping our world, this comprehensive guide aims to provide you with a solid foundation and a clear path forward.

Are you ready to become an AI Hero and make positive transformation? In Become an AI Hero: A Journey Towards Inclusive Innovation, Award-Winning Talent Development Expert, Liza Wisner takes readers on a profound odyssey into the heart of the digital era, delving deep into the far-reaching influence of Artificial Intelligence (AI) and how we can harness its power with a greater focus on inclusivity. This book will equip readers with the knowledge and tools to be a catalyst for change and use AI as a potent force for good.

Dieses Buch ist eine Einführung in naturanaloge Techniken und verwandte formale Methoden. Zu jeder Technik werden Anwendungsbeispiele gegeben. Dargestellt werden Zellularautomaten und Boolesche Netze, Evolutionäre Algorithmen sowie Simulated Annealing, Fuzzy-Methoden, Neuronale Netze und schließlich Hybride Systeme, d.h. Koppelungen verschiedener dieser Techniken. Auf der Basis der Theorie komplexer dynamischer Systeme werden zusätzlich theoretische Grundlagen dargestellt und es wird auf die Gemeinsamkeiten der auf einen ersten Blick sehr heterogenen Techniken hingewiesen. Die Auflage wurde überarbeitet und erweitert mit aktuellen Trends wie ChatGPT.

This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.

This book is a broad-based introduction to an increasingly important topic. The overview is easy because each chapter is structured uniformly: theory, methods, realization alternatives, methodical and practical advantages and disadvantages, and examples from industrial technology and medicine as well as research and development practice.Compared to the first edition ¿Biosignal Processing¿, the content of this book has been expanded by a sensor chapter (galvanic and capacitive sensors) and exemplary experimental data. Chapter 7: ¿Stochastic Processes¿ is also new with selected topics such as statistical analysis of time series, signal detection, and signal decomposition. Furthermore, various electronic measuring circuits and calculations have been adapted to the changed legal regulations and standards and some calculation errors have been corrected.The content is oriented to the sequence of the metrological and signal-analytical chain: neuron as a signal source¿sensor technology¿signal amplification and conditioning¿signal sampling and digitization¿methods of biosignal processing¿biostatistics and stochastic processes.The target groupsThe book is suitable for medical technology studies, research, and practice. You can inform yourself compactly across your professional boundaries about the neighbouring fields and topics at the interdisciplinary interface between medicine and technology.

The two-volume set LNAI 14391 and 14392 constitutes the proceedings of the 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, held in Yucatán, Mexico, in November 2023.The total of 49 papers presented in these two volumes was carefully reviewed and selected from 115 submissions.The proceedings of MICAI 2023 are published in two volumes. The first volume, Advances in Computational Intelligence, contains 24 papers structured into three sections:¿ Machine Learning¿ Computer Vision and Image Processing¿ Intelligent SystemsThe second volume, Advances in Soft Computing, contains 25 papers structured into three sections:¿ Natural Language Processing¿ Bioinformatics and Medical Applications¿ Robotics and Applications

This book is a collection of thoroughly well-researched studies presented at the Eighth Future Technologies Conference. This annual conference aims to seek submissions from the wide arena of studies like Computing, Communication, Machine Vision, Artificial Intelligence, Ambient Intelligence, Security, and e-Learning. With an impressive 490 paper submissions, FTC emerged as a hybrid event of unparalleled success, where visionary minds explored groundbreaking solutions to the most pressing challenges across diverse fields. These groundbreaking findings open a window for vital conversation on information technologies in our community especially to foster future collaboration with one another. We hope that the readers find this book interesting and inspiring and render their enthusiastic support toward it.

The two-volume set LNAI 14391 and 14392 constitutes the proceedings of the 22nd Mexican International Conference on Artificial Intelligence, MICAI 2023, held in Yucatán, Mexico, in November 2023.The total of 49 papers presented in these two volumes was carefully reviewed and selected from 115 submissions.The proceedings of MICAI 2023 are published in two volumes. The first volume, Advances in Computational Intelligence, contains 24 papers structured into three sections:¿ Machine Learning¿ Computer Vision and Image Processing¿ Intelligent SystemsThe second volume, Advances in Soft Computing, contains 25 papers structured into three sections:¿ Natural Language Processing¿ Bioinformatics and Medical Applications¿ Robotics and Applications

This book unlike any other previous book provides a platform for scholars and researchers to present the latest insights and findings on the application of artificial intelligence and other sustainable technologies for a human-centric society. It brings together technology with society with special attention given to AI and IoT-related intricacies for a digital economy. It covers a variety of research topics including block ciphers, network marketing for sustainability entrepreneurship and AI, AI and stock trading decisions, digital transformation, knowledge management, chatbot engineering, cybersecurity, and smart metering system. The book is a comprehensive reference work for scholars, academics, policymakers, students, and professionals presenting an overall understanding of AI, its present and future trends, and presents a discourse on important policies and strategies on inclusivity, diversity, bias, accountability, security, metaverse applications of AI, and other technologiessuch as IoT.

This book is a collection of thoroughly well-researched studies presented at the Eighth Future Technologies Conference. This annual conference aims to seek submissions from the wide arena of studies like Computing, Communication, Machine Vision, Artificial Intelligence, Ambient Intelligence, Security, and e-Learning. With an impressive 490 paper submissions, FTC emerged as a hybrid event of unparalleled success, where visionary minds explored groundbreaking solutions to the most pressing challenges across diverse fields. These groundbreaking findings open a window for vital conversation on information technologies in our community especially to foster future collaboration with one another. We hope that the readers find this book interesting and inspiring and render their enthusiastic support toward it.

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 the separation principle which is also known as the principle of separation of estimation and control and states that, under certain assumptions, the problem of designing an optimal feedback controller for a stochastic system can be solved by designing an optimal observer for the system's state, which feeds into an optimal deterministic controller for the system. Thus, the problem may be divided into two halves, which simplifies its design. In the context of deterministic linear systems, the first instance of this principle is that if a stable observer and stable state feedback are built for a linear time-invariant system (LTI system hereafter), then the combined observer and feedback are stable. The separation principle does not true for nonlinear systems in general. Another instance of the separation principle occurs in the context of linear stochastic systems, namely that an optimum state feedback controller intended to minimize a quadratic cost is optimal forthe stochastic control problem with output measurements. The ideal solution consists of a Kalman filter and a linear-quadratic regulator when both process and observation noise are Gaussian. The term for this is linear-quadratic-Gaussian control. More generally, given acceptable conditions and when the noise is a martingale (with potential leaps), a separation principle, also known as the separation principle in stochastic control, applies when the noise is a martingale (with possible jumps).

This book constitutes the refereed proceedings of the 43rd SGAI International Conference on Artificial Intelligence, AI 2023, held in Cambridge, UK, during December 12¿14, 2023.The 27 full papers and 20 short papers included in this book are carefully reviewed and selected from 67 submissions. They were organized in topical sections as follows: Technical Papers: Speech and Natural Language Analysis, Image Analysis, Neural Nets, Case Based Reasoning and Short Technical Papers. Application Papers: Machine Learning Applications, Machine Vision Applications, Knowledge Discovery and Data Mining Applications, other AI Applications and Short Application Papers.

This book constitutes the refereed proceedings of the 5th Iberoamerican Conference and 4th Indo-American Conference on Knowledge Graphs and Semantic Web, KGSWC 2023, held jointly in Zaragoza, Spain, during November 13¿15, 2023.The 18 full and 2 short papers presented were carefully reviewed and selected from 50 submissions. They focus on the following topics: knowledge representation; natural language processing/text mining; and machine/deep learning research.

Recently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. These new methods introduce interesting mechanisms and innovative collaborative strategies that facilitate the efficient exploration and exploitation of extensive search spaces characterized by numerous dimensions. The objective of this book is to present advancements that discuss novel alternative metaheuristic developments that have demonstrated their effectiveness in tackling various complex problems. This book encompasses a variety of emerging metaheuristic methods and their practical applications. The content is presented from a teaching perspective, making it particularly suitable for undergraduate and postgraduate students in fields such as science, electrical engineering, and computational mathematics. The book aligns well with courses in artificial intelligence, electrical engineering, and evolutionary computation. Furthermore, the material offers valuable insights to researchers within the metaheuristic and engineering communities. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques. These methods transcend mere theoretical tools that have been adapted to effectively address the significant real-world problems commonly encountered in engineering domains.