This book constitutes the proceedings of the 9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 11 full and 2 short papers presented were carefully reviewed and selected from 42 submissions. They cover topics in AI Modeling, AI Analysis, AI and Mobile Applications, AI Architecture, AI Management, AI Engineering, Mobile backend as a service (MBaaS), User experience of AI and mobile services.

This book constitutes the proceedings of the 11th International Conference on Artificial Intelligence and Mobile Services, AIMS 2022, held as Part of the Services Conference Federation, SCF 2022, held in Honolulu, HI, USA, in December 2022. The 10 full papers presented in this volume were carefully reviewed and selected from 22 submissions. The International Conference on AI & Mobile Services (AIMS 2022) aims at providing an international forum that is dedicated to exploring different aspects of AI (from technologies to approaches and algorithms) and mobile services (from business management to computing systems, algorithms, and applications) to promoting technological innovations in research and development of mobile services, including, but not limited to, wireless & sensor networks, mobile & wearable computing, mobile enterprise & eCommerce, ubiquitous collaborative & social services, machine-to-machine & Internet-of-things clouds, cyber-physical integration, and big data analytics for mobility-enabled services.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

This book constitutes the refereed proceedings of the 12th International Conference on Artificial Intelligence and Mobile Services, AIMS 2023, Held as Part of the Services Conference Federation, SCF 2023, Honolulu, HI, USA, September 23–26, 2023. The 9 full papers presented together and selected from 16 submissions. The conference focuses on AI modeling; AI analysis; AI & mobile applications; AI architecture; AI management; AI engineering; mobile backend as a service (MBaaS); and user experience of AI & mobile services.

Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains. Features: Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”. Reviews adept handling with respect to existing software and evaluation issues of interpretability. Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression. Focuses on interpreting black box models like feature importance and accumulated local effects. Discusses capabilities of explainability and interpretability. This book is aimed at graduate students and professionals in computer engineering and networking communications.

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]

This book contains the proceedings of the 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023) held via hybrid form in Nanjing, China during May 26th to 28th, 2023. The specific topics covered in this conference include the application of artificial intelligence in education, learning management systems, collaborative learning, e-learning methodologies, intellectual property rights, and patents. The aim of the conference is to bring together various professionals from the scientific community to foster connections between science, technology, and industry, and provide a platform for exploring fundamental issues and new applications in related fields. We hope that the scientific attitudes and skills developed through research will encourage scholars worldwide to contribute to the development of knowledge generated by research. Finally, we would like to express our gratitude to the conference chair, publication chairs, technical program committee chairs, local organizing chairs, program committee chairs, conference secretariat, and conference sponsors for their financial support, which made the successful organization of ICEKIM 2023 possible. We hope that this conference will continue to be held in the coming years, publishing more insightful articles with inspiring research. We would also like to thank the invited speakers for their valuable contributions and for sharing their perspectives during their speeches.

Artificial Intelligence in Biomedical and Modern Healthcare Informatics provides a deeper understanding of the current trends in AI and machine learning within healthcare diagnosis, its practical approach in healthcare, and gives insight into different wearable sensors and its device module to help doctors and their patients in enhanced healthcare system. The primary goal of this book is to detect difficulties and their solutions to medical practitioners for the early detection and prediction of any disease. The 56 chapters in the volume provide beginners and experts in the medical science field with general pictures and detailed descriptions of imaging and signal processing principles and clinical applications. With forefront applications and up-to-date analytical methods, this book captures the interests of colleagues in the medical imaging research field and is a valuable resource for healthcare professionals who wish to understand the principles and applications of signal and image processing and its related technologies in healthcare. - Discusses fundamental and advanced approaches as well as optimization techniques used in AI for healthcare systems - Includes chapters on various established imaging methods as well as emerging methods for skin cancer, brain tumor, epileptic seizures, and kidney diseases - Adopts a bottom-up approach and proposes recent trends in simple manner with the help of real-world examples - Synthesizes the existing international evidence and expert opinions on implementing decommissioning in healthcare - Promotes research in the field of health and hospital management in order to improve the efficiency of healthcare delivery systems