Bøger i Studies in Computational Intelligence serien

The book focuses on original approaches intended to support the development of biologically inspired cognitive architectures. It bridges together different disciplines, including artificial intelligence, linguistics, neuro- and social sciences, psychology and philosophy of mind, among others. The chapters are based on contributions presented at the 2023 Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence (the 14th Annual Meeting of the BICA Society, BICA*AI 2023), organized in collaboration with the University of Ningbo and held on October 13-15, 2023, in Ningbo, China. The book discusses emerging methods, theories and ideas towards the realization of general-purpose humanlike artificial intelligence or fostering a better understanding of the ways the human mind works. It provides engineers, mathematicians, psychologists, computer scientists and other experts with a timely snapshot of recent research and a source of inspiration for future developments in the broadly intended areas of artificial intelligence and biological inspiration.

The scientific theme of the book is ¿Sustainability for the digital manufacturing erä which is focused on how the technological enablers of digital systems drive the sustainability for products, processes, manufacturing, and logistics systems in the industry of the future. The book¿s chapters describe sustainability drivers specific to the technological advances that motivate, encourage, and engage the economic, social, and environmental aspects within intelligent manufacturing and logistics systems. The novelty of this approach consists in extending the economic sustainability of digital manufacturing with the ethical perspective at human social scale. The general scope of the book is to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods, and solutions for the digital transformation of manufacturing through service orientation in holonic and agent-based control with distributed intelligence. The book¿s readership is comprised of researchers and engineers working in the value chain of products and processes, who develop digital control solutions in the ¿Industry of the Future¿ vision. The book is also addressed to master¿s and Ph.D. students enrolled in Engineering Sciences programs.

In this book, various chaos maps are embedded in eleven efficient and well-known metaheuristics and a significant improvement in the optimization results is achieved. The two basic steps of metaheuristic algorithms consist of exploration and exploitation. The imbalance between these stages causes serious problems for metaheuristic algorithms, which are immature convergence and stopping in local optima. Chaos maps with chaotic jumps can save algorithms from being trapped in local optima and lead to convergence toward global optima. Embedding these maps in the exploration phase, exploitation phase, or both simultaneously corresponds to three efficient and useful scenarios. By creating competition between different modes and increasing diversity in the search space and creating sudden jumps in the search phase, improvements are achieved for chaotic algorithms. Four Chaotic Algorithms, including Chaotic Cyclical Parthenogenesis Algorithm, Chaotic Water Evaporation Optimization, Chaotic Tug-of-War Optimization, and Chaotic Thermal Exchange Optimization are developed.

This book offers a comprehensive overview of the latest advancements in the field of applied mathematics as it relates to finance, marketing, and economics. It covers a range of topics including the effective utilization of applied mathematics and mathematical modeling in finance, economics, and marketing. Additionally, it explores the intersection between applied mathematics and practical applications in various scientific fields. The book targets a multidisciplinary audience, fostering the exchange of diverse ideas and showcasing the broad appeal of different subjects. It delves into recent developments in areas such as mathematical modeling in finance, mathematical modeling in marketing, the modeling of financial and economic fundamentals (e.g., interest rates, asset prices), market behavior modeling, modeling market imperfections, pricing financial derivative securities, hedging strategies, numerical methods, and financial engineering.The book features selected contributions presented at the first edition of the International Conference in Applied Mathematics to Finance, Marketing and Economics, which took place at the National School of Commerce and Management in El Jadida, Morocco, from November 26 to 27, 2020.

This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder¿decoder network, meta-learning, conditional variational encoder¿decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.

This book is focused on connecting two interesting research areas, mathematics and computational intelligence, by means of appealing contributions devoted to give solutions to different challenges of the current technological age.It continues the collection of articles dealing with the important and efficient combination of these both areas, with a stress of fuzzy systems and fuzzy logic. It also includes relevant papers on the development and application of mathematics, artificial intelligence, and automatic reasoning tools to Digital Forensics, which have been developed within the framework of the COST Action DigForASP-CA17124 (digforasp.uca.es).

This book explores current and future trends in adopting intelligent technologies, such as the metaverse, social technologies, FinTech applications, and blockchain, among individuals and organizations. The edited book includes empirical and review studies primarily focusing on these issues. This focus aids scholars in conducting future research in the domain and identifying possible future developments of emerging technologies. The empirical studies in the book utilize recent and advanced analytical techniques for data analysis.

Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape.

This book is a collection of various methodologies that make it possible for metaheuristics and hyper-heuristics to solve problems that occur in the real world. This book contains chapters that make use of metaheuristics techniques. The application fields range from image processing to transmission power control, and case studies and literature reviews are included to assist the reader. Furthermore, some chapters present cutting-edge methods for load frequency control and IoT implementations. In this sense, the book offers both theoretical and practical contents in the form of metaheuristic algorithms. The researchers used several stochastic optimization methods in this book, including evolutionary algorithms and Swarm-based algorithms. The chapters were written from a scientific standpoint. As a result, the book is primarily aimed at undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, but it can also be used in courses on Artificial Intelligence, among other things. Similarly, the material may be beneficial to research in evolutionary computation and artificial intelligence communities.

The book provides an overview of various autoimmune disorders and how artificial intelligence (AI) and machine learning will be used for the diagnosis, prognosis, and treatment of these disorders. AI algorithms are used to create synthetic patient populations with the properties of actual patient cohorts, build personalized predictive models of drug combinations and unravel complex relationships between diet, microbiome, and genetic line-up to determine the comparative treatment response. The book highlights clinical applications and challenges of AI for the diagnosis and treatment/management of autoimmune disorders which includes Rheumatoid Arthritis (RA), Multiple Sclerosis (MS), Type I Diabetes, Psoriatic Arthritis (PsA), and other critical diseases.

The book delivers an excellent professional development resource for educators and practitioners on the cutting-edge computational intelligence techniques and applications. It covers many areas and topics of computational intelligence techniques and applications proposed by computational intelligence experts and researchers and furthers the enhancement of the community outreach and engagement component of computational intelligence techniques and applications. Furthermore, it presents a rich collection of manuscripts in highly regarded computational intelligence techniques and applications topics that have been creatively compiled. Computers are capable of learning from data and observations and providing solutions to real-life complex problems, following the same reasoning approach of human experts in various fields. This book endows a rich collection of applications in widespread areas. Among the areas addressed in this book are Computational Intelligence Principles andTechniques; CI in Manufacturing, Engineering, and Industry; CI in Recognition and Processing; CI in Robotics and Automation; CI in Communications and Networking; CI in Traditional Vehicles, Electric Vehicles, and Autonomous Vehicles; CI in Smart Cities and Smart Energy Systems; and CI in Finance, Business, Economics, and Education. These areas span many topics including repetitive manufacturing, discrete manufacturing, process manufacturing, electronic systems, speech recognition, pattern recognition, signal processing, image processing, industrial monitoring, vision systems for automation and robotics, cooperative and network robotics, perception, planning, control, urban traffic networks control, vehicle-to-roadside communications, smart buildings, smart urbanism, smart infrastructure, smart connected communities, smart energy, security, arts, and music.

This contributed volume consists of 11 chapters that specifically cover the security aspects of the latest technologies such as Blockchain, IoT, and DevOps, and how to effectively deal with them using Intelligent techniques. Moreover, machine learning (ML) and deep learning (DL) algorithms are also not secured and often manipulated by attackers for data stealing. This book also discusses the types of attacks and offers novel solutions to counter the attacks on ML and DL algorithms. This book describes the concepts and issues with figures and the supporting arguments with facts and charts. In addition to that, the book provides the comparison of different security solutions in terms of experimental results with tables and charts. Besides, the book also provides the future directions for each chapter and novel alternative approaches, wherever applicable. Often the existing literature provides domain-specific knowledge such as the description of security aspects. However, the readers find it difficult to understand how to tackle the application-specific security issues. This book takes one step forward and offers the security issues, current trends, and technologies supported by alternate solutions. Moreover, the book provides thorough guidance on the applicability of ML and DL algorithms to deal with application-specific security issues followed by novel approaches to counter threats to ML and DL algorithms. The book includes contributions from academicians, researchers, security experts, security architectures, and practitioners and provides an in-depth understanding of the mentioned issues.

This book aims to highlight the latest achievements in the use of AI and multimodal artificial intelligence in biomedicine and healthcare. Multimodal AI is a relatively new concept in AI, in which different types of data (e.g. text, image, video, audio, and numerical data) are collected, integrated, and processed through a series of intelligence processing algorithms to improve performance. The edited volume contains selected papers presented at the 2022 Health Intelligence workshop and the associated Data Hackathon/Challenge, co-located with the Thirty-Sixth Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI and Multimodal AI in public health and medicine.

Authored by a leading expert in the field, this book introduces an innovative methodology that harnesses the power of fuzzy logic to enhance decision-making in multi-attribute scenarios. In a world of complexity and uncertainty, effective decision-making is paramount. Springer proudly presents a cutting-edge publication that revolutionizes decision analysis: "Fuzzy Decision Analysis: Multi-attribute Decision-Making Approach." This book stands at the forefront of decision analysis, introducing the integration of fuzzy logic into multi-attribute decision-making. It is a transformative journey into the realm of advanced decision analysis. It book not only equips you with the knowledge to comprehend the theoretical underpinnings but also empowers you to apply these insights in practical scenarios. This book serves as your indispensable companion. Its comprehensive coverage serves as a beacon, guiding you through the intricate maze of fuzzy logic and multi-attribute decision-making, ultimately empowering you to embrace innovation and master the art of making well-informed decisions in an ever-changing world.

This edited book presents scientific results of the 12th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2013) which was held on June 16-20, 2013 in Toki Messe, Niigata, Japan. The aim of this conference was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas, research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve themThe conference organizers selected the best 20 papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rigorous rounds of review.

This book describes current and potential use of artificial intelligence and computational intelligence techniques in biomedicine and other application areas. Medical applications range from general diagnostics to processing of X-ray images to e-medicine-related privacy issues.Medical community understandably prefers methods that have been successful other on other application areas, where possible mistakes are not that critical. This book describes many promising methods related to deep learning, fuzzy techniques, knowledge graphs, and quantum computing. It also describes the results of testing these new methods in communication networks, education, environmental studies, food industry, retail industry, transportation engineering, and many other areas. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications-and to further develop this important research direction.

The book presents advancements in computational intelligence in perception with healthcare applications. Besides, the concepts, theory, and applications in various domains of healthcare systems including decision making in healthcare management, disease diagnosis, and electronic health records will be presented in a lucid manner. To achieve these objectives, both theoretical advances and its applications to healthcare problems will be stressed upon. This has been done to make the edited book more flexible and to stimulate further research interest in topics. The book is divided into four sections such as theoretical foundation of computational intelligence techniques, computational intelligence in analyzing health data, computational intelligence in electronic health record (EHR), and computational intelligence in ethical issues in health care.

This book is very beneficial for early researchers/faculty who want to work in deep learning and machine learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies studies' image and data classifications. The early start-up can use it to work with product or prototype design requirement analysis and its design and development.

This edited book presents scientific results of the 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021-Winter) which was held on January 28-30, at Ho Chi Minh City, Vietnam. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way and research results about all aspects (theory, applications, and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 18 of most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science. 

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.