This book offers an overview of current and recent methods for the analysis of the nonstationary processes, focusing on cyclostationary systems that are ubiquitous in various application fields. Based on the 13th Workshop on Nonstationary Systems and Their Applications, held on February 3-5, 2020, in Grodek nad Dunajcem, Poland, the book merges theoretical contributions describing new statistical and intelligent methods for analyzing nonstationary processes, and applied works showing how the proposed methods can be implemented in practice and do perform in real-world case studies.

This book presents a collection of high-quality research by leading experts in computer vision and its applications. Each of the 16 chapters can be read independently and discusses the principles of a specific topic, reviews up-to-date techniques, presents outcomes, and highlights the challenges and future directions. As such the book explores the latest trends in fashion creative processes, facial features detection, visual odometry, transfer learning, face recognition, feature description, plankton and scene classification, video face alignment, video searching, and object segmentation.
It is intended for postgraduate students, researchers, scholars and developers who are interested in computer vision and connected research disciplines, and is also suitable for senior undergraduate students who are taking advanced courses in related topics. However, it is also provides a valuable reference resource for practitioners from industry who want to keep abreast of recent developments in this dynamic, exciting and profitable research field.

The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning - ICAAAIML 2020. The book covers research in artificial intelligence, machine learning, and deep learning applications in healthcare, agriculture, business, and security.
This volume contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book will be a valuable resource for students, academics, and practitioners in the industry working on AI applications.

Science rests on the twin pillars of experiment and theory. An experiment is more than mere collection of data. One should have a credible hypothesis to explain the mass of data. For example, Tycho Brahe, the Danish astronomer, had accumulated a large volume of data on the position of planets in the sky over many years of careful observation. Kepler analyzed this complex maze of data, and deduced three simple laws of planetary motion. Newton then introduced the hypothesis of attraction between two masses, inversely proportional to the square of the distance between the two, and showed that all three laws of motion followed as natural consequences of this hypothesis. This is the path taken by any scientific discovery.

This book presents selected papers presented in the Symposium on Applied Aerodynamics and Design of Aerospace Vehicles (SAROD 2018), which was jointly organized by Aeronautical Development Agency (the nodal agency for the design and development of combat aircraft in India), Gas-Turbine Research Establishment (responsible for design and development of gas turbine engines for military applications), and CSIR-National Aerospace Laboratories (involved in major aerospace programs in the country such as SARAS program, LCA, Space Launch Vehicles, Missiles and UAVs).

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms.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 useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.