This book constitutes the refereed proceedings of the 44th German Conference on Artificial Intelligence, KI 2021, held in September/October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 full and 4 short papers with one extended abstract were carefully reviewed and selected from 59 submissions. As well-established annual conference series KI is dedicated to research on theory and applications across all methods and topic areas of AI research.
This book constitutes the refereed proceedings of the 44th German Conference on Artificial Intelligence, KI 2021, held in September/October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 full and 4 short papers with one extended abstract were carefully reviewed and selected from 59 submissions. As well-established annual conference series KI is dedicated to research on theory and applications across all methods and topic areas of AI research.
This book constitutes the refereed proceedings of the 45th German Conference on Artificial Intelligence, KI 2022, held in September 2022. The 12 full and 5 short papers were carefully reviewed and selected from 51 submissions. Additionally, five abstracts of invited talks are included. As well-established annual conference series KI is dedicated to research on theory and applications across all methods and topic areas of AI research. Due to COVID-19 the conference was held virtually. The chapter "Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Artificial Intelligence (AI) is widely known as a knowledge field that aims to make computers, robots, or products that mimic the way humans think. In the current scientific community, AI is an intensively studied area composed of multiple branches. Historically, machine learning and optimization are two of the most studied fronts thanks to the development of novel and challenging research topics such as transfer optimization, swarm robotics, and drift detection and adaptation to evolving conditions in real-time. This book collects radically new theoretical insights, reporting recent developments and evincing innovative applications regarding AI methods in all fields of knowledge. It also presents works focused on new paradigms and novel branches of AI science.
This edited book is a collection of selected research papers presented at the 2021 2nd International Conference on Artificial Intelligence in Education Technology (AIET 2021), held in Wuhan, China on July 2-4, 2021. AIET establishes a platform for AI in education researchers to present research, exchange innovative ideas, propose new models, as well as demonstrate advanced methodologies and novel systems. Rapid developments in artificial intelligence (AI) and the disruptive potential of AI in educational use has drawn significant attention from the education community in recent years. For educators entering this uncharted territory, many theoretical and practical questions concerning AI in education are raised, and issues on AI’s technical, pedagogical, administrative and socio-cultural implications are being debated. The book provides a comprehensive picture of the current status, emerging trends, innovations, theory, applications, challenges and opportunities of current AI in education research. This timely publication is well-aligned with UNESCO’s Beijing Consensus on Artificial Intelligence (AI) and Education. It is committed to exploring how best to prepare our students and harness emerging technologies for achieving the Education 2030 Agenda as we move towards an era in which AI is transforming many aspects of our lives. Providing a broad coverage of recent technology-driven advances and addressing a number of learning-centric themes, the book is an informative and useful resource for researchers, practitioners, education leaders and policy-makers who are involved or interested in AI and education.
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
After decades of basic research and more promises than impressive applications, artificial intelligence (AI) is starting to deliver benefits. A convergence of advances is motivating this new surge of AI development and applications. Computer capability as it has evolved from high throughput and high performance computing systems is increasing. AI models and operations research adaptations are becoming more mature, and the world is breeding big data not only from the web and social media but also from the Internet of Things. Organizations around the world have been realizing that there are substantial performance gains and increases in productivity for the use of AI and predictive analytics techniques. Their use is bringing a new era of breakthrough innovation and opportunities. This book, compiles research insights and applications in diverse areas such as manufacturing, supply chain management, pricing, autonomous vehicles, healthcare, ecommerce, and aeronautics. Using classical and advanced tools in AI such as deep learning, particle swarm optimization, support vector machines and genetic programming among others. This is a very distinctive book which discusses important applications using a variety of paradigms from AI and outlines some of the research to be performed. The work supersedes similar books that do not cover as diversified a set of sophisticated applications. The authors present a comprehensive and articulated view of recent developments, identifies the applications gap by quoting from the experience of experts, and details suggested research areas. Artificial Intelligence: Advances in Research and Applications guides the reader through an intuitive understanding of the methodologies and tools for building and modeling intelligent systems. The book's coverage is broad, starting with clustering techniques with unsupervised ensemble learning, where the optimal combination strategy of individual partitions is robust in comparison to the selection of an algorithmic clustering pool. This is followed by a case in a parallel-distributed simulator using deep learning for its configuration. Chapter Three presents a case for autonomous vehicles. Chapter Four discusses the novel use of genetic algorithms with support vector machines. Chapters Five through Thirteen focus on the applications. The book discusses how the use of AI can allow for productivity development and other benefits not just for businesses, but also for economies. Finally, you can find an interesting investigation of the transhuman dimension of AI.
This book covers all the emerging trends in artificial intelligence (AI) and the Internet of Things (IoT). The Internet of Things is a term that has been introduced in recent years to define devices that are able to connect and transfer data to other devices via the Internet. While IoT and sensors have the ability to harness large volumes of data, AI can learn patterns in the data and quickly extract insights in order to automate tasks for a variety of business benefits. Machine learning, an AI technology, brings the ability to automatically identify patterns and detect anomalies in the data that smart sensors and devices generate, and it can have significant advantages over traditional business intelligence tools for analyzing IoT data, including being able to make operational predictions up to 20 times earlier and with greater accuracy than threshold-based monitoring systems. Further, other AI technologies, such as speech recognition and computer vision can help extract insights from data that used to require human review. The powerful combination of AI and IoT technology is helping to avoid unplanned downtime, increase operating efficiency, enable new products and services, and enhance risk management.
