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Smart Applications with Advanced Machine Learning and Human-Centred Problem Design
This book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for developments-improvements within different engineering fields, which are highly interested in using artificial intelligence and applied mathematics. As a collection of the outputs from the ICAIAME 2021, the book is specifically considering research outcomes including advanced use of machine learning and careful problem designs on human-centred aspects. In this context, it aims to provide recent applications for real-world improvements making life easier and more sustainable for especially humans. The book targets the researchers, degree students, and practitioners from both academia and the industry.
Trends in Data Engineering Methods for Intelligent Systems
This book briefly covers internationally contributed chapters with artificial intelligence and applied mathematics-oriented background-details. Nowadays, the world is under attack of intelligent systems covering all fields to make them practical and meaningful for humans. In this sense, this edited book provides the most recent research on use of engineering capabilities for developing intelligent systems. The chapters are a collection from the works presented at the 2nd International Conference on Artificial Intelligence and Applied Mathematics in Engineering held within 09-10-11 October 2020 at the Antalya, Manavgat (Turkey). The target audience of the book covers scientists, experts, M.Sc. and Ph.D. students, post-docs, and anyone interested in intelligent systems and their usage in different problem domains. The book is suitable to be used as a reference work in the courses associated with artificial intelligence and applied mathematics.
Nonlinear Dynamics and Complexity
This book collects a range of contributions on nonlinear dynamics and complexity, providing a systematic summary of recent developments, applications, and overall advances in nonlinearity, chaos, and complexity. It presents both theories and techniques in nonlinear systems and complexity and serves as a basis for more research on synchronization and complexity in nonlinear science as well as a mechanism to fast-scatter the new knowledge to scientists, engineers, and students in the corresponding fields. Written by world-renown experts from across the globe, the collection is ideal for researchers, practicing engineers, and students concerned with machinery and controls, manufacturing, and controls.
Complex Motions and Chaos in Nonlinear Systems
This book brings together 12 chapters on a new stream of research examining complex phenomena in nonlinear systems—including engineering, physics, and social science. Complex Motions and Chaos in Nonlinear Systems provides readers a particular vantage of the nature and nonlinear phenomena in nonlinear dynamics that can develop the corresponding mathematical theory and apply nonlinear design to practical engineering as well as the study of other complex phenomena including those investigated within social science.
Domain Structured Chaos and Applications
Domain structured dynamics introduces a way for analysis of chaos in fractals, neural networks and random processes. It starts with newly invented abstract similarity sets and maps, which are in the basis of the abstract similarity dynamics. Then a labeling procedure is designed to determine the domain structured dynamics. The results follow the Pythagorean doctrine, considering finite number of indices for the labeling, with potential to become universal in future. The immediate power of the approach for fractals as domains of chaos, revisited famous deterministic and stochastic models, new types of differential equations and neural networks is seen in the book. This is not considered through widening areas, where the notions can be seen and recognized, but by deepening abstraction. Key Features Provides the abstract similarity map, which generalizes the Bernoulli shift for abstract self-similar sets. Discusses dynamically generated self-similar sets as a method of chaos generation. Introduces abstract fractals as sets of metric spaces on the basis of abstract similar sets. Presents fractals concepts discussed through the newly introduced notions.
Almost Periodicity, Chaos, and Asymptotic Equivalence
The central subject of this book is Almost Periodic Oscillations, the most common oscillations in applications and the most intricate for mathematical analysis. Prof. Akhmet's lucid and rigorous examination proves these oscillations are a "regular" component of chaotic attractors. The book focuses on almost periodic functions, first of all, as Stable (asymptotically) solutions of differential equations of different types, presumably discontinuous; and, secondly, as non-isolated oscillations in chaotic sets. Finally, the author proves the existence of Almost Periodic Oscillations (asymptotic and bi-asymptotic) by asymptotic equivalence between systems. The book brings readers' attention to co...
Mathematical Reviews
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Computing Anticipatory Systems
This book constitutes the proceedings of the Seventh International Conference on Computing Anticipatory Systems, CASYS'05. The volume includes all invited papers as well as those papers that received a best paper award. Also included is a lecture on "General Principles for Brain Design," presented by Brian D. Josephson, Nobel Laureate in Physics. The papers discuss recent research and development in the modeling and computing of anticipatory systems in fields of natural and artificial systems.
Computing Anticipatory Systems
This book constitutes the proceedings of the Seventh International Conference on Computing Anticipatory Systems, CASYS'05. The volume includes all invited papers as well as those papers that received a best paper award. Also included is a lecture on "General Principles for Brain Design," presented by Brian D. Josephson, Nobel Laureate in Physics. The papers discuss recent research and development in the modeling and computing of anticipatory systems in fields of natural and artificial systems.
