Kunstig intelligens

This book presents an exploration of linkages among soil-water, agriculture, and climate change with a special focus on thematic areas for assessment, mitigation, and management of natural resources under climate change conditions. This book covers advances in modelling approaches, including machine learning (ML)/ artificial intelligence (AI) applications; GIS and remote sensing; sensors; impacts of climate change on agriculture; subsurface water; contaminants; and socio-economic impacts, which are lacking in a more comprehensive manner in the previous titles. This book encompasses updated information as well as future directions for researchers working in the field of management of natural resources. The goal of this book is to provide scientific evidence to researchers and policymakers and end-to-end value chain practitioners which may help in reducing the overall adverse impacts of climate change on water resources and the related mitigation strategies. This book focuses on the knowledge, modern tools, and techniques, i.e., machine learning, artificial intelligence, etc. for soil-water, agriculture, and climate change. Further, nature-based solutions for management of natural resources with special targets on contaminants, extreme events, disturbances, etc. will be targeted. The book provides readers with the enhanced knowledge for application of engineering principles and economic and regulatory constraints to determine a soil-water, agriculture production action strategy, and select appropriate technologies to implement the strategy for a given data set at a site. It would also cover the application of laboratory, modeling, numerical methods for determination and forecasting of climate change impacts, agriculture production, pollution, soil health, etc. Overall, it provides hydrologists, environmental engineers, administrators, policy makers, consultants, and industrial experts with essential support in effective management of soils health, agricultural productions, and mitigation of extreme climatic events.

This book presents a comparative perspective of current metaheuristic developments, which have proved to be effective in their application to several complex problems. The study of biological and social entities such as animals, humans, or insects that manifest a cooperative behavior has produced several computational models in metaheuristic methods. Although these schemes emulate very different processes or systems, the rules used to model individual behavior are very similar. Under such conditions, it is not clear to identify which are the advantages or disadvantages of each metaheuristic technique. The book is compiled from a teaching perspective. For this reason, the book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. It is appropriate for courses such as Artificial Intelligence, Electrical Engineering, Evolutionary Computation. The book is also useful for researchers from the evolutionary and engineering communities. Likewise, engineer practitioners, who are not familiar with metaheuristic computation concepts, will appreciate that the techniques discussed are beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise in engineering areas.

This book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios.  In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling.  Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years.  Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items.  This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets.  This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items.  To deal with the challenging task of outfit compatibility modeling, this book provides comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning.  Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.  

This book explains the basic concepts, theory and applications of fuzzy systems in control in a simple unified approach with clear ex-amples and simulations in the MATLAB programming language. Fuzzy systems, especially, type-2 neuro-fuzzy systems, are now used extensively in various engineering fields for different purposes. In plain language, this book aims to practically explain fuzzy sys-tems and different methods of training and optimizing these systems. For this purpose, type-2 neuro-fuzzy systems are first analyzed along with various methods of training and optimizing these systems through implementation in MATLAB. These systems are then em-ployed to design adaptive fuzzy controllers. The authors aim at pre-senting all the well-known optimization methods clearly and code them in the MATLAB language.

This book provides a coherent and complete overview of various Question Answering (QA) systems. It covers three main categories based on the source of the data that can be unstructured text (TextQA), structured knowledge graphs (KBQA), and the combination of both. Developing a QA system usually requires using a combination of various important techniques, including natural language processing, information retrieval and extraction, knowledge graph processing, and machine learning.After a general introduction and an overview of the book in Chapter 1, the history of QA systems and the architecture of different QA approaches are explained in Chapter 2. It starts with early close domain QA systems and reviews different generations of QA up to state-of-the-art hybrid models. Next, Chapter 3 is devoted to explaining the datasets and the metrics used for evaluating TextQA and KBQA. Chapter 4 introduces the neural and deep learning models used in QA systems. This chapter includes the required knowledge of deep learning and neural text representation models for comprehending the QA models over text and QA models over knowledge base explained in Chapters 5 and 6, respectively. In some of the KBQA models the textual data is also used as another source besides the knowledge base; these hybrid models are studied in Chapter 7. In Chapter 8, a detailed explanation of some well-known real applications of the QA systems is provided. Eventually, open issues and future work on QA are discussed in Chapter 9.This book delivers a comprehensive overview on QA over text, QA over knowledge base, and hybrid QA systems which can be used by researchers starting in this field. It will help its readers to follow the state-of-the-art research in the area by providing essential and basic knowledge.

This edited book presents scientific results of the 1st ACIS International Symposium on Emotional Artificial Intelligence & Metaverse (EAIM) which was held on August 4-6, 2022, in Danang, Vietnam. The aim of this symposium 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.  All aspects (theory, applications, and tools) of emotional artificial intelligence and metaverse, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here in the results of the articles featured in this book.The symposium organizers selected the best papers from those papers accepted for presentation at the symposium. 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, 15 of the symposium's most promising papers are then published in this Springer (SCI) book and not the symposium proceedings. We impatiently await the important contributions that we know these authors will bring to the field of emotional artificial intelligence and metaverse.

This book brings the recent collection of smart technologies. Smart cities challenges and key requirements are discussed through the technological solutions, IoT, cloud computing, block chain and artificial intelligence. Firstly, the key technologies contributing to the smart cities research are identified. Then, the most popular ones are covered in context to their theoretical and practical applications.Smart cities technologies are one of the recent research areas. Every day new technological solutions are coming to make smart cities more sustainable. The book explores the integration of main key technologies for smart cities which are IoT & cloud computing, data science, AI and block chain & Industry 4.0. Moreover, some integrated solutions using AI, data science and IoT will attract the attention of end users.Primary market of the book is aimed toward the undergraduate and master students. IoT, cloud computing, artificial intelligence and block chain are elective courses at the bachelor level in the engineering domain, and its application areas in context to smart cities are covered in this book.The book is a good source of reference for their master dissertations. Ph.D. students or scholars who are working on these key technologies like IoT & cloud, AI, data science, block chain & Industry 4.0 will find this book as a constant source of reference for their ongoing research.Smart city planners, architects and municipal experts may also find this book useful.

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas.  All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms.   As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy.  Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy,  Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels,  and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

This book emphasizes the latest developments and achievements in AI and related technologies with a special focus on food quality. The book describes the applications, and conceptualization of ideas, and critical surveys covering most aspects of AI for food quality.

This book presents a comprehensive guide to the design of playing robots and the related play experiences. Play is a natural activity for building and improving abilities, and it reveals important particularly for persons with disabilities. Many social, physical and cultural factors may hinder children with disabilities from fully enjoying play as their peers. Autonomous robots with specific characteristics can enhance the ludic experience, having implications for the character of the play and presenting opportunities related to autonomy and physical movement, the very nature of robots. Their introduction into play thus provides everybody, and in particular persons with disabilities, new possibilities for developing abilities, improving general status, participating in social contexts, as well as supporting professionals in monitoring progress.This book presents a framework for the design of playful activities with robots, developed over 20 years' experience at AIRLab - POLIMI. Part 1 introduces the play concepts and characteristics, and research results about play of children with different kinds of impairments. Part 2 focuses on implementing robots able to play. The design of playful activities is discussed, as well as the necessary characteristics for them to be useful in both general play and activities involving disability-related limitations. In Part 3, the defined framework is used to analyze possibilities involving robots available on the toy market, robots developed at research labs, and robots to be developed in the next future. The aim of the book is to give developers, caregivers, and users a set of methodological tools for selecting, exploring, and designing inclusive play activities where robots play a central role.

The main objective of this book is to explore the synergy between cutting-edge AI technologies and environmentally conscious practices through collecting best selected research papers presented at the International Conference on Artificial Intelligence and Green Computing (ICAIGC 2023), which took place from March 15 to 17, 2023, in Beni Mellal, Morocco.Within the pages of this book, readers find a wealth of research findings, survey works, and practical experiences aimed at fostering a comprehensive understanding of the pivotal role AI plays in various fields, including agriculture, health care, IT, and more. It highlights both the opportunities presented by the widespread usage of AI and the challenges associated with its continued advancement. As a result, the book has been divided into three parts: 1)- AI for multimedia processing, 2)- AI for distributed computing, and 3)- AI applications.The book serves as a comprehensive resource that brings together on-goingresearch and practical experiences from the ICAIGC 2023 conference. It strives to deepen the understanding of the essential role AI plays in multiple fields. Whether you are an AI enthusiast, researcher, or practitioner, the insights contained within these pages expand your horizons and inspire further exploration of AI's potential in shaping a greener and more technologically advanced future.

One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson's disease patients. This framework offers a new way to assess and monitor patients' motor deficits in a personalized and automated way, improving the efficiency and accuracy of diagnosis and treatment.Compared to other books on eHealth and Parkinson's disease, this book offers a unique perspective and solution to the challenges facing patients and healthcare providers. It combines state-of-the-art technology, such as wearable devices and deep learning algorithms, with clinical expertise to develop a personalized and efficient evaluation framework for Parkinson's disease patients.This book provides a roadmap for the integration of cutting-edge technology into clinical practice, paving the way for more effective and patient-centered healthcare. To understand this book, readers should have a basic knowledge of eHealth, IoT, deep learning, and Parkinson's disease. However, the book provides clear explanations and examples to make the content accessible to a wider audience, including researchers, practitioners, and students interested in the intersection of technology and healthcare.

This book constitutes the refereed proceedings of the 21st Asian Symposium on Programming Languages and Systems, APLAS 2023, held in Taipei, Taiwan, during November 26¿29, 2023.The 15 full papers included in this book are carefully reviewed and selected from 32 submissions. They were organized in topical sections as follows: semantics, logics, and foundational theory; design of languages, type systems, and foundational calculi; domain-specific languages; compilers, interpreters, and abstract machines; program derivation, synthesis, and transformation; program analysis, verification, and model-checking; logic, constraint, probabilistic, and quantum programming; software security; concurrency and parallelism; tools and environments for programming and implementation; and applications of SAT/SMT to programming and implementation.

This book constitutes the proceedings of the 8th International Conference on Future Data and Security Engineering, FDSE 2021, held in Ho Chi Minh City, Vietnam, in November 2021.*The 28 full papers and 8 short were carefully reviewed and selected from 168 submissions. The selected papers are organized into the following topical headings: big data analytics and distributed systems; security and privacy engineering; industry 4.0 and smart city: data analytics and security; blockchain and access control; data analytics and healthcare systems; and short papers: security and data engineering.* The conference was held virtually due to the COVID-19 pandemic.

"This Cambridge Handbook is the first dedicated treatment of the interface between AI and private law, and the challenges that AI poses for private law. The Handbook brings together a global team of private law experts and computer scientists to examine the interface, identify the problems, and propose solutions"--

A new approach to the challenges surrounding artificial intelligence that argues for assessing AI actions as if they came from a human being

This book includes a set of selected revised and extended versions of the best papers presented at the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) ¿ held as an online event, from October 25 to 27, 2021. We focus on three outstanding fields of Computational Intelligence through the selected panel, namely: Evolutionary Computation, Fuzzy Computation, and Neural Computation. Besides presenting the recent advances of the selected areas, the book aims to aggregate new and innovative solutions for confirmed researchers and on the other hand to provide a source of information and/or inspiration for young interested researchers or learners in the ever-expanding and current field of Computational Intelligence. It constitutes a precious provision of knowledge for individual researchers as well as represent a valuable sustenance for collective use in academic libraries (of universities and engineering schools) relating innovative techniques in various fieldsof applications.

This book constitutes the refereed proceedings of the XXIInd International Conference on AIxIA 2023 ¿ Advances in Artificial Intelligence, AIxIA 2023, held in Rome, Italy, during November 6¿10, 2023.The 33 full papers included in this book were carefully reviewed and selected from 53 submissions. They were organized in topical sections as follows: Argumentation and Logic Programming, Natural Language Processing, Machine Learning, Hybrid AI and Applications of AI.

This book explores the subject of artificial psychology from the standpoint of how online Chatbots have infiltrated and affected societies and the world in general. The book explores the psychological effects of depending on an online entity for our needs ¿ even if it¿s a reminder of scheduled events. The author provides insight into the notion of human-Chatbot exchanges, understanding, and false emotions both from the Chatbot and from the human. He goes on to investigate and discuss the dangers of too much reliance on technology that learns from a variety of sources and how some sources can negatively influence Chatbots, and by doing so, negatively affect people. The book also discusses human-Chatbot interactions and the natural language interface(s) required to respond adequately to humans. Lastly, the author explores the notion of ethical considerations for people, based on their interactions with Chatbots, including information based on cultural differences between different regions of the world.

With approximately 2500 problems, this book provides a collection of practical problems on the basic and advanced data structures, design, and analysis of algorithms. To make this book suitable for self-instruction, about one-third of the algorithms are supported by solutions, and some others are supported by hints and comments. This book is intended for students wishing to deepen their knowledge of algorithm design in an undergraduate or beginning graduate class on algorithms, for those teaching courses in this area, for use by practicing programmers who wish to hone and expand their skills, and as a self-study text for graduate students who are preparing for the qualifying examination on algorithms for a Ph.D. program in Computer Science or Computer Engineering. About all, it is a good source for exam problems for those who teach algorithms and data structure. The format of each chapter is just a little bit of instruction followed by lots of problems. This book is intended to augment the problem sets found in any standard algorithms textbook. This book * begins with four chapters on background material that most algorithms instructors would like their students to have mastered before setting foot in an algorithms class. The introductory chapters include mathematical induction, complexity notations, recurrence relations, and basic algorithm analysis methods. * provides many problems on basic and advanced data structures including basic data structures (arrays, stack, queue, and linked list), hash, tree, search, and sorting algorithms. * provides many problems on algorithm design techniques: divide and conquer, dynamic programming, greedy algorithms, graph algorithms, and backtracking algorithms. * is rounded out with a chapter on NP-completeness.

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 is intended for students, engineers, and researchers interested in both computational mechanics and deep learning. It presents the mathematical and computational foundations of Deep Learning with detailed mathematical formulas in an easy-to-understand manner. It also discusses various applications of Deep Learning in Computational Mechanics, with detailed explanations of the Computational Mechanics fundamentals selected there. Sample programs are included for the reader to try out in practice. This book is therefore useful for a wide range of readers interested in computational mechanics and deep learning.

CONVERSATIONAL CHAT INFORMATIVE BOOKCybercrime is a complex phenomenon with a variety of contributing factors. One important factor is the psycho-social dynamics of cybercrime, which refers to the complex factors that motivate individuals to engage in cybercriminal activities and the social environment in which cybercrime occurs.Cybercriminals are motivated by a variety of factors, including greed, revenge, thrill-seeking, and status recognition. They may also be motivated by social factors, such as peer pressure, the availability of cybercrime tools and resources, and the existence of online communities where cybercriminals can share information, resources, and support.It is important to understand the psycho-social dynamics of cybercrime in order to develop effective prevention and intervention strategies. For example, public awareness campaigns and education programs can help to educate the public about the risks of cybercrime and promote social norms that discourage cybercriminal behavior. Programs that teach young people about the importance of ethical behavior online and how to resist peer pressure can also help to prevent young people from being drawn into cybercrime.Law enforcement can play a role in addressing the psycho-social dynamics of cybercrime by disrupting cybercrime networks and bringing cybercriminals to justice. This can help to deter cybercrime and reduce the availability of cybercrime tools and resources.It is also important to support victims of cybercrime, including providing psychosocial support. This can help victims to recover from the emotional and financial impact of a crime.By understanding and addressing the psycho-social dynamics of cybercrime, we can help to create a safer and more secure online environment for everyone.Here are some specific examples of how the psycho-social dynamics of cybercrime can be addressed:Schools can implement programs that teach students about the dangers of cybercrime and how to protect themselves online.Companies can provide training to employees on cybercrime awareness and prevention.Law enforcement can work with social media companies to crack down on cybercrime activity on their platforms.Governments can fund research on the psycho-social dynamics of cybercrime and develop policies to address this issue.