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Artificial Intelligence and Applications: 26th International Conference, ICAI 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024, Las Vegas, NV, USA, July 22–25, 2024, Revised Selected Papers (Communications in Computer and Information Science #2252)
by Hamid R. Arabnia Leonidas Deligiannidis Soheyla Amirian Farzan Shenavarmasouleh Farid Ghareh Mohammadi David de la FuenteThis book constitutes the proceedings of the 26th International Conference on Artificial Intelligence and Applications, ICAI 2024, held as part of the 2024 World Congress in Computer Science, Computer Engineering and Applied Computing, in Las Vegas, USA, during July 22 to July 25, 2024. The 38 full papers included in this book were carefully reviewed and selected from 376 submissions. They have been organized in topical sections as follows: Deep convolutional neural networks, ANNs, and applications; machine learning and novel applications; large language models and applications; data science, recognition and authentication methods and applications; artificial intelligence and applications; XXIV Technical Session on Applications of Advanced AI Techniques to information management for solving company-related problems.
Artificial Intelligence and Applications: Proceedings of ICAIA 2024 (Algorithms for Intelligent Systems)
by Amita Yadav Amit M. Joshi Mehmet Ergezer Valentina Emilia BalasThe papers in this book are high quality refereed papers presented at ICAIA 2024, the second International conference on Artificial Intelligence and Applications, held at Maharaja Surajmal Institute of Technology, New Delhi in collaboration with Wentworth Institute of Technology, Boston, USA in March 2024. This book presents new and innovative developments and applications in machine learning, data mining, neural networks, computation optimisation technologies, followed by research applications in signals, language and classification, prediction, recommendations, and systems. This book is essential for researchers and practitioners in this field.
Artificial Intelligence and Applied Mathematics in Engineering Problems: Proceedings of the International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2019) (Lecture Notes on Data Engineering and Communications Technologies #43)
by D. Jude Hemanth Utku KoseThis book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20–22 April 2019 at Antalya, Manavgat (Turkey). In today’s world, various engineering areas are essential components of technological innovations and effective real-world solutions for a better future. In this context, the book focuses on problems in engineering and discusses research using artificial intelligence and applied mathematics. Intended for scientists, experts, M.Sc. and Ph.D. students, postdocs and anyone interested in the subjects covered, the book can also be used as a reference resource for courses related to artificial intelligence and applied mathematics.
Artificial Intelligence and Autoimmune Diseases: Applications in the Diagnosis, Prognosis, and Therapeutics (Studies in Computational Intelligence #1133)
by Khalid Raza Surender SinghThe 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.
Artificial Intelligence and Big Data: The Birth of a New Intelligence
by Fernando IafrateWith the idea of “deep learning” having now become the key to this new generation of solutions, major technological players in the business intelligence sector have taken an interest in the application of Big Data. In this book, the author explores the recent technological advances associated with digitized data flows, which have recently opened up new horizons for AI. The reader will gain insight into some of the areas of application of Big Data in AI, including robotics, home automation, health, security, image recognition and natural language processing.
Artificial Intelligence and Big Data for Financial Risk Management: Intelligent Applications (Banking, Money and International Finance)
by Hassan M. Kabir Metawa Saad Metawa NouraThis book presents a collection of high-quality contributions on the state-of-the-art in Artificial Intelligence and Big Data analysis as it relates to financial risk management applications. It brings together, in one place, the latest thinking on an emerging topic and includes principles, reviews, examples, and research directions. The book presents numerous specific use-cases throughout, showing practical applications of the concepts discussed. It looks at technologies such as eye movement analysis, data mining or mobile apps and examines how these technologies are applied by financial institutions, and how this affects both the institutions and the market. This work introduces students and aspiring practitioners to the subject of risk management in a structured manner. It is primarily aimed at researchers and students in finance and intelligent big data applications, such as intelligent information systems, smart economics and finance applications, and the internet of things in a marketing environment.
Artificial Intelligence and Bioethics (SpringerBriefs in Ethics)
by Perihan Elif Ekmekci Berna ArdaThis book explores major bioethical issues emerging from the development and use of artificial intelligence in medical settings. The authors start by defining the past, present and future of artificial intelligence in medical settings and then proceed to address the resulting common and specific bioethical inquiries. The book discusses bioethical inquiries in two separate sets. The first set is comprised of ontological discussions mainly focusing on personhood and being an ethical agent of an artefact. The second set discusses bioethical issues resulting from the use of artificial intelligence. It focuses particularly on the area of artificial intelligence use in medicine and health services. It addresses the main challenges by considering fundamental principles of medical ethics, including confidentiality, privacy, compassion, veracity and fidelity. Finally, the authors discuss the ethical implications of involvement of artificial intelligence agents in patient care by expanding on communication skills in a case-based approach. The book is of great interest to ethicists, medical professionals, academicians, engineers and scientists working with artificial intelligence.
Artificial Intelligence and Bioethics: Perspectives
by Luiz Vianna Sobrinho Leandro Modolo Maíra Araújo de Santana Giselle Machado Magalhães Moreno Fabiano Tonaco Borges Wellington Pinheiro dos SantosThe fourth industrial revolution challenges humanity ethically and morally: mass unemployment, new forms of colonialism, and mass-and-granular surveillance are a few examples of these challenges. Nevertheless, the industrial revolutions have increased human productivity and quality of life. This book aims to review the ethical challenges related to the use of these technologies. It unfolds bioethical perspectives regarding Artificial Intelligence (AI) and its impact on life on earth. It discusses both northern and southern epistemologies of bioethics. Northern bioethics comprises principles of autonomy, beneficence, non-maleficence, and justice. Southern bioethics gives weightage to struggles for human liberation, social justice, and the pluralism of knowledge. The book discusses topics from aging to mass surveillance, to deliver a universal bioethical guideline to a wide range of professions that work with AI and are concerned about its impact on life. This book will not label AI, but broaden the readers' view of an ethical and explainable AI that works for life on earth.
Artificial Intelligence and Bioinspired Computational Methods: Proceedings of the 9th Computer Science On-line Conference 2020, Vol. 2 (Advances in Intelligent Systems and Computing #1225)
by Radek SilhavyThis book gathers the refereed proceedings of the Artificial Intelligence and Bioinspired Computational Methods Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020.Artificial intelligence and bioinspired computational methods now represent crucial areas of computer science research. The topics presented here reflect the current discussion on cutting-edge hybrid and bioinspired algorithms and their applications.
Artificial Intelligence and Blockchain for Future Cybersecurity Applications (Studies in Big Data #90)
by Yassine Maleh Youssef Baddi Mamoun Alazab Loai Tawalbeh Imed RomdhaniThis book presents state-of-the-art research on artificial intelligence and blockchain for future cybersecurity applications. The accepted book chapters covered many themes, including artificial intelligence and blockchain challenges, models and applications, cyber threats and intrusions analysis and detection, and many other applications for smart cyber ecosystems. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on artificial intelligence and blockchain for future cybersecurity applications.
Artificial Intelligence and Blockchain for Social Impact: Social Business Models and Impact Finance
by Wolfgang Spiess-KnaflArtificial Intelligence and Blockchain for Social Impact provides an accessible overview of artificial intelligence (AI) and blockchain technologies, and explores their applications for social enterprise and impact investing. The opening chapter introduces the impact space, exploring different social business models, the role of technology, the impact investing market and general problems in the space. The remainder of this book falls into two paths: the first focusing on AI and the other looking at the blockchain technology. Providing introductions to each of these technologies and their histories, the author goes on to examine them from the perspectives of social business models and impact finance. A concluding chapter explores AI and cryptocurrencies in the impact space in the future. Readers are supported with international case studies and other student-friendly features. Situated at the intersection between technology, fintech, social enterprise, impact investing and social impact, this book is a valuable resource for upper-level courses across all these areas. It also offers an introduction to this emerging topic for researchers and business professionals. Online teaching resources to accompany this book include instructor lecture slides and data sets.
Artificial Intelligence and Blockchain in Digital Forensics (River Publishers Series in Digital Security and Forensics)
by P. Karthikeyan Hari Mohan Pandey Velliangiri SarveshwaranDigital forensics is the science of detecting evidence from digital media like a computer, smartphone, server, or network. It provides the forensic team with the most beneficial methods to solve confused digital-related cases. AI and blockchain can be applied to solve online predatory chat cases and photo forensics cases, provide network service evidence, custody of digital files in forensic medicine, and identify roots of data scavenging. The increased use of PCs and extensive use of internet access, have meant easy availability of hacking tools. Over the past two decades, improvements in the information technology landscape have made the collection, preservation, and analysis of digital evidence extremely important. The traditional tools for solving cybercrimes and preparing court cases are making investigations difficult. We can use AI and blockchain design frameworks to make the digital forensic process efficient and straightforward. AI features help determine the contents of a picture, detect spam email messages and recognize swatches of hard drives that could contain suspicious files. Blockchain-based lawful evidence management schemes can supervise the entire evidence flow of all of the court data. This book provides a wide-ranging overview of how AI and blockchain can be used to solve problems in digital forensics using advanced tools and applications available on the market.
Artificial Intelligence and Blockchain in Industry 4.0 (Future Generation of Soft and Intelligent Computing)
by Rohit Sharma Rajendra Prasad Mahapatra Gwanggil JeonThe book addresses the challenges in designing blockchain-based secured solutions for Industry 4.0 applications using artificial intelligence. It further provides a comparative analysis of various advanced security approaches such as edge computing, cybersecurity, and cloud computing in the realm of information technology. This book: • Address the challenges in designing blockchain-based secured solutions for Industry 4.0 applications using artificial intelligence • Provides a comparative analysis of various advanced security approaches such as edge computing, cybersecurity, and cloud computing in the realm of information technology • Discusses the evolution of blockchain and artificial intelligence technology, from fundamental theories to practical aspects • Illustrates the most recent research solutions that handle the security and privacy threats while considering the resource-constrained in Industry 4.0 devices • Showcases the methods and tools necessary for intelligent data analysis and gives solutions to problems resulting from automated data collection The text aims to fill the gap between the theories of blockchain and its practical application in business, government, and defense among other areas. It further highlights the challenges associated with the use of blockchain for various industry 4.0 applications such as data analytics, software-defined networks, cyber-physical systems, drones, and cybersecurity. The text is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, manufacturing engineering, and industrial engineering.
Artificial Intelligence and Brain Research: Neural Networks, Deep Learning and the Future of Cognition
by Patrick KraussHow does artificial intelligence (AI) work and are there parallels to the human brain? What do natural and artificial intelligence have in common, and what are the differences? Is the brain nothing more than a biological computer? What are neural networks and how can the term deep learning be explained simply?Since the cognitive revolution in the middle of the last century, AI and brain research have been closely intertwined. There have been several spectacular breakthroughs in the field of AI in recent years, from alphaGo to DALL-E 2 and ChatGPT, which were completely unthinkable until recently. However, researchers are already working on the innovations of tomorrow, such as hybrid machine learning or neuro-symbolic AI. But what does this actually mean?Based on current research findings and exciting practical examples, this non-fiction book provides an understandable introduction to the basics and challenges of these fascinating disciplines. You will learn what neuroscience and psychology know about how the brain works and how artificial intelligence works. You will also learn how AI has revolutionized our understanding of the brain and how findings from brain research are used in computer science to further develop AI algorithms. Discover the fascinating world of these two disciplines. Find out why artificial intelligence and brain research are two sides of the same coin and how they will shape our future.
Artificial Intelligence and Business Transformation: Impact in HR Management, Innovation and Technology Challenges (Contributions to Management Science)
by María Teresa Del Val Núñez Alba Yela Aránega Domingo Ribeiro-SorianoThis book offers a current perspective on Artificial Intelligence in the context of an ever-changing and growing technological revolution in business management. It analyses how existing companies are adapting, new ones are emerging, and others are disappearing. Process re-engineering has made it possible to reshape organizational structures and create new departments and positions, all geared towards digitalization. The emergence of new business functions has led to new strategic thinking on e.g. companies’ structure, size, and core business – but also to the creation of new jobs, the need to cover digital skills, and the need for innovative team management. In short, it is a question of delving deeper into HR and the impact that digitalization has had on it, as the employee is one of the key figures to protect. The book initially focuses on providing a review of the current literature on the advancement of Artificial Intelligence and its impact on business transformation and the emergence of new management models. In turn, it addresses the diverse perspectives that currently dominate the business market, as well as the corporate transformations that have taken place in the post-pandemic era. Lastly, it equips employers with new tools to incorporate into their organizations, facilitating talent retention. In connection with HR, this digital transformation is reflected in new roles for change management and cultural transformation, including the use of digital technologies to improve the employee experience. In brief, the book offers a practical guide to business transformation, technological advances, and their application in human resources departments.
Artificial Intelligence and Causal Inference
by Momiao XiongArtificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine. Key Features: Cover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin’s Maximum Principle for network training. Deep learning for nonlinear mediation and instrumental variable causal analysis. Construction of causal networks is formulated as a continuous optimization problem. Transformer and attention are used to encode-decode graphics. RL is used to infer large causal networks. Use VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes. AI-based methods for estimation of individualized treatment effect in the presence of network interference.
Artificial Intelligence and Cognitive Science: 30th Irish Conference, AICS 2022, Munster, Ireland, December 8–9, 2022, Revised Selected Papers (Communications in Computer and Information Science #1662)
by Luca Longo Ruairi O’ReillyThis open access book constitutes selected papers presented during the 30th Irish Conference on Artificial Intelligence and Cognitive Science, held in Munster, Ireland, in December 2022. The 41 presented papers were thoroughly reviewed and selected from the 102 submissions. They are organized in topical sections on machine learning, deep learning and applications; responsible and trustworthy artificial intelligence; natural language processing and recommender systems; knowledge representation, reasoning, optimisation and intelligent applications.
Artificial Intelligence and Communication Techniques in Industry 5.0 (Advances in Manufacturing, Design and Computational Intelligence Techniques)
by Payal Bansal Rajeev Kumar Ashwani Kumar Daniel D. DasigThe book highlights the role of artificial intelligence in driving innovation, productivity, and efficiency. It further covers applications of artificial intelligence for digital marketing in Industry 5.0 and discusses data security and privacy issues in artificial intelligence, risk assessments, and identification strategies.This book: Discusses the role of artificial intelligence applications for digital manufacturing in Industry 5.0 Presents blockchain methods and data-driven decision-making with autonomous transportation Covers reinforcement learning algorithm and highly predicted models for accurate data analysis in industry automation Highlights the importance of robust authentication mechanisms and access control policies to protect sensitive information, prevent unauthorized access, and enable secure interactions between humans and machines Explains attack pattern detection and prediction which play a crucial role in ensuring the security of business systems and networks It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, industrial engineering, manufacturing engineering, and production engineering.
Artificial Intelligence and Competition: Economic and Legal Perspectives in the Digital Age (Contributions to Economics)
by Georgios I. ZekosThis book examines the impact of artificial intelligence on competition and antitrust in today's global digital economy. It scrutinizes the economic and legal ramifications of Artificial Intelligence (AI), addressing the challenges it presents to competition and the law.Beginning with an analysis of AI's developments across various economic sectors, the book highlights the need for updated legislation. It focuses on the digital economy, emphasizing digital platforms' role in shaping competition. Econometric investigations and a novel index assess competition's influence on foreign direct investment and multinational enterprises. Comparing competition practices across jurisdictions like the EU, US, Germany, and China, the book uncovers commonalities and differences in competition law principles. It also explores various theories on competition and competition law, seeking convergence or divergence.This book is an essential resource for scholars, legal professionals, policymakers, and anyone seeking a better understanding of how AI is reshaping competition and antitrust in the digital age.
Artificial Intelligence and Complex Dynamical Systems (Understanding Complex Systems)
by Giorgos TsironisThis book serves as a comprehensive introduction to nonlinear complex systems through the application of machine learning methods. Artificial intelligence (AI) has affected the foundations of scientific discovery, and can therefore lend itself to developing a better understanding of the unpredictable nature of complex dynamical systems and to predict their future evolution. Utilizing Python code, this book teaches and applies machine learning to topics such as chaotic dynamics and time-series analysis, solitons, breathers, chimeras, nonlinear localization, biomolecular dynamics, and wave propagation in the heart. The consistent integration of methods and models allow for readers to develop a necessary intuition on how to handle complexity through AI. This textbook contains a wealth of expository material, code, and example problems to support and organize academic coursework, allowing the technical nature of these areas of study to become highly accessible. Requiring only a basic background in mathematics and coding in Python, this book is an essential text for a wide array of advanced undergraduate or graduate students in the applied sciences interested in complex systems through the lens of machine learning.
Artificial Intelligence and Computer Vision (Studies in Computational Intelligence #672)
by Huimin Lu Yujie LiThis edited book presents essential findings in the research fields of artificial intelligence and computer vision, with a primary focus on new research ideas and results for mathematical problems involved in computer vision systems. The book provides an international forum for researchers to summarize the most recent developments and ideas in the field, with a special emphasis on the technical and observational results obtained in the past few years.
Artificial Intelligence and Conservation (Artificial Intelligence for Social Good)
by Fei Fang Milind Tambe Bistra Dilkina Andrew J. PlumptreWith the increasing public interest in artificial intelligence (AI), there is also increasing interest in learning about the benefits that AI can deliver to society. This book focuses on research advances in AI that benefit the conservation of wildlife, forests, coral reefs, rivers, and other natural resources. It presents how the joint efforts of researchers in computer science, ecology, economics, and psychology help address the goals of the United Nations' 2030 Agenda for Sustainable Development. Written at a level accessible to conservation professionals and AI researchers, the book offers both an overview of the field and an in-depth view of how AI is being used to understand patterns in wildlife poaching and enhance patrol efforts in response, covering research advances, field tests and real-world deployments. The book also features efforts in other major conservation directions, including protecting natural resources, ecosystem monitoring, and bio-invasion management through the use of game theory, machine learning, and optimization.
Artificial Intelligence and COVID Effect on Accounting (Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application)
by Bahaaeddin Alareeni Allam HamdanThis book considers the effects of COVID-19 on accounting, particularly with regard to the role of artificial intelligence in accounting in the post-pandemic business environment. The contributions in the book consider a variety of sectors that have been affected by the pandemic, such as the stock market, forensic accounting, Bitcoin, as well as the economic and educational responses to the pandemic and the aftermath felt by both developing and developed countries. This book will be a valuable read for academics, students and practitioners of accounting who are keen to explore the future of the field in light of the pandemic.
Artificial Intelligence and Credit Risk: The Use of Alternative Data and Methods in Internal Credit Rating
by Rossella Locatelli Giovanni Pepe Fabio SalisThis book focuses on the alternative techniques and data leveraged for credit risk, describing and analysing the array of methodological approaches for the usage of techniques and/or alternative data for regulatory and managerial rating models. During the last decade the increase in computational capacity, the consolidation of new methodologies to elaborate data and the availability of new information related to individuals and organizations, aided by the widespread usage of internet, set the stage for the development and application of artificial intelligence techniques in enterprises in general and financial institutions in particular. In the banking world, its application is even more relevant, thanks to the use of larger and larger data sets for credit risk modelling. The evaluation of credit risk has largely been based on client data modelling; such techniques (linear regression, logistic regression, decision trees, etc.) and data sets (financial, behavioural, sociologic, geographic, sectoral, etc.) are referred to as “traditional” and have been the de facto standards in the banking industry. The incoming challenge for credit risk managers is now to find ways to leverage the new AI toolbox on new (unconventional) data to enhance the models’ predictive power, without neglecting problems due to results’ interpretability while recognizing ethical dilemmas. Contributors are university researchers, risk managers operating in banks and other financial intermediaries and consultants. The topic is a major one for the financial industry, and this is one of the first works offering relevant case studies alongside practical problems and solutions.
Artificial Intelligence and Cyber Security in Industry 4.0 (Advanced Technologies and Societal Change)
by Velliangiri Sarveshwaran Joy Iong-Zong Chen Danilo PelusiThis book provides theoretical background and state-of-the-art findings in artificial intelligence and cybersecurity for industry 4.0 and helps in implementing AI-based cybersecurity applications. Machine learning-based security approaches are vulnerable to poison datasets which can be caused by a legitimate defender's misclassification or attackers aiming to evade detection by contaminating the training data set. There also exist gaps between the test environment and the real world. Therefore, it is critical to check the potentials and limitations of AI-based security technologies in terms of metrics such as security, performance, cost, time, and consider how to incorporate them into the real world by addressing the gaps appropriately. This book focuses on state-of-the-art findings from both academia and industry in big data security relevant sciences, technologies, and applications.