Recent developments in information science and technology have been possible due to original and timely research contributions containing new results in various fields of applied mathematics. It is also true that advances in information science create opportunities for developing mathematical models further.

Augmented intelligence is an alternate approach of artificial intelligence (AI), which emphasizes AI’s assistive role. Augmented intelligence enhances human skills of reasoning in a robotic system or software by simulating expectancy, educational mining, problem solving, recollection, sequencing, and decision-making capabilities. It is based on a combination of techniques such as machine learning, deep learning and cognitive computing. This book explains artificial intelligence models that support assistive processes in different situations. The contributors aim to provide information to a diverse audience with groundbreaking developments in mathematical computing. The book presents 8 chapters on these topics: - Educational data mining in augmented reality virtual learning environment - Brain and computer interfaces - Tree-based tools for chemometric analysis of infrared spectra - Applications of deep learning in medical engineering - Bankruptcy prediction model using an enhanced boosting classifier - Reputation systems for mobile agent security - The crow search algorithm - COVID-19 diagnosis and treatment The contents attempt to integrate various facets of augmented Intelligence, by describing recent research developments and advanced topics of interest to academicians and researchers working on machine learning problems and AI.

The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.

This book deals with the methods of theoretical and experimental research of antennas, which are based on the electromagnetic (EM) theory. The theory of thin antennas explains antenna analysis, as thin antennas represent one of the main types of radiators and are extensively applied as both independent antennas and as elements of more complicated antennas.

The contents of the book cover new methods of antenna analysis and design, including the theory of electrically coupled lines, the methods of complex potential, compensation, impedance line, and mathematical programming among others. New results from these experiments are also detailed. The complex potential method is generalized to inhomogeneous media and is employed in conical and parabolic problems. The theory of electrically coupled lines allows considering theoretical problems of multi-conductor cables and multi-radiator antennas. The compensation method permits creating a weak field area in the near region of a transmitting antenna. The method of impedance line is applied to antennas with loads, and the method of mathematical programming deals with selecting loads to develop antennas with the required characteristics.

The methods described within this text enable engineers to calculate parameters for new types of antennas, particularly, the self-complementary antennas. In the general case, similar properties pertain to the three-dimensional self-complementary antennas, their variants are also considered in the book. Close attention is also paid to the field compensation in the near region of a transmitting antenna and to the creation of the ‘dark spot’.

The book is an essential reference for graduates and researchers requiring knowledge on the design and engineering of new antennas as well as the underlying electromagnetic and mathematical principles.