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This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, masters students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance.
Statistical science --- Actuarial mathematics --- Computer. Automation --- neuronale netwerken --- statistiek --- wiskunde --- actuariaat --- Regression analysis. --- Actuarial science.
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This volume in the Lecture Notes in Electrical Engineering series explores advanced optimization techniques in computer and electrical engineering. It emphasizes hybrid and improved algorithms to solve complex engineering problems such as embedded systems, circuit design, robotics, and energy management. The book integrates concepts from artificial intelligence, control theory, and machine learning to develop efficient solutions. It covers evolutionary computation, swarm intelligence, and ant colony optimization, with applications in robotics, machine learning, and autonomous systems. Designed for researchers, engineers, and students, it provides a comprehensive overview of metaheuristic methods through examples and case studies.
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The purpose of this volume is to present current work of the Intelligent Computer Graphics community, a community growing up year after year. This volume is a kind of continuation of the previously published Springer volume "Artificial Intelligence Techniques for Computer Graphics". Nowadays, intelligent techniques are more and more used in Computer Graphics in order, not only to optimise the processing time, but also to find more accurate solutions for a lot of Computer Graphics problems, than with traditional methods. This volume contains both invited and selected extended papers from the last 3IA Conference (3IA’2009), which has been held in Athens (Greece) in May 2009. The Computer Graphics areas approached in this volume are behavioural modelling, declarative modelling, intelligent modelling and rendering, data visualisation, scene understanding, realistic rendering, and more.
Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- fuzzy logic --- cybernetica --- grafische vormgeving --- KI (kunstmatige intelligentie) --- robots --- Computer vision --- Computer graphics
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This book presents volume 2 of selected research papers presented at the Second International Conference on Digital Technologies and Applications (ICDTA 23). This book highlights the latest innovations in digital technologies as artificial intelligence, Internet of Things, embedded systems, network technology, digital transformation, and their applications in several areas as Industry 4.0, renewable energy, mechatronics, digital healthcare, etc. The respective papers encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.
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This book presents volume 1 of selected research papers presented at the third International Conference on Digital Technologies and Applications (ICDTA 23). This book highlights the latest innovations in digital technologies as: artificial intelligence, Internet of things, embedded systems, network technology, digital transformation and their applications in several areas as Industry 4.0, renewable energy, mechatronics, digital healthcare. The respective papers encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.
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This book collects different artificial intelligence methodologies that applied to solve real-world problems. This book has exciting chapters that employ artificial intelligence and applied to different applications based on integration with meta-heuristic and other techniques. The area of applications is including medical diagnosis, text analysis, cloud computing, and others which will enrich the reader. In this sense, the book provides practical and theory content with novel artificial intelligence techniques. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics and is applied in courses on artificial intelligence, optimization techniques, advanced machine learning, among others.
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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.
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This book states that data users often suffer from the difficulty of acquiring knowledge for decision-making, and others are unsure how existing data are useful. The reader will be released from these dilemmas and enabled to act beyond patterns in past events by creating a process to interact with the data market and the dynamic real-world rich in new events. We present new approaches from the aspects of computation, communication, and their integration, to readers including analysts in sciences and businesses, systems managers, and learners desiring to design knowledge to learn. We show clues to explaining causalities in the target world of a black-box AI of which users may seek a predictive performance. For obtaining interpretable knowledge, we show the integration of model- and data-driven approaches, the analysis and perception of signals from data acquired in the cyber or the real word, and creative communication which connects demands to data by visualizing the data market as a place for innovations.
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This book shows how such a computational process functions, how great is its power and versatility, since it is possible to show how discoveries are made. In 1759, A. Smith realized that there must exist an additional powerful control mechanism behind Great Britain's authority and government, explaining the extraordinary successes of Great Britain. He called this the Invisible Hand. Despite having used this term only 3 times, the idea evokes extreme scientific and political emotions to this day. If we apply a molecular model of computation, such as in in Adleman's DNA computer, a computational model for the Invisible Hand can be built to show how it affects a society. It is a spontaneous, unconscious, distributed, noncontinuous computational process on the platform of minds of, e.g., people or ants. Knowing this mechanism, a future self-steering and self-optimization system for AI robot teams can be proposed, e.g., for construction sites and rescue operations.
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