Carlo Cattani - Böcker

The Natural Language for Artificial Intelligence presents the biological and logical structure typical of human language in its dynamic mediating process between reality and the human mind. The book explains linguistic functioning in the dynamic process of human cognition when forming meaning. After that, an approach to artificial intelligence (AI) is outlined, which works with a more restricted concept of natural language that leads to flaws and ambiguities. Subsequently, the characteristics of natural language and patterns of how it behaves in different branches of science are revealed to indicate ways to improve the development of AI in specific fields of science.

A brief description of the universal structure of language is also presented as an algorithmic model to be followed in the development of AI. Since AI aims to imitate the process of the human mind, the book shows how the cross-fertilization between natural language and AI should be done using the logical-axiomatic structure of natural language adjusted to the logical-mathematical processes of the machine.

Presents a comprehensive approach to natural language and its inherent and complex dynamics Develops language content as the next frontier, identifying the universal structure of language as a common structure that appears in both AI and cognitive computing Explains the standard structure present in cognition and AI, making them interchangeable Offers examples of the application of the universal language model in image analysis and conventional language

Wavelet Analysis: Basic Concepts and Applications provides a basic and self-contained introduction to the ideas underpinning wavelet theory and its diverse applications. This book is suitable for master’s or PhD students, senior researchers, or scientists working in industrial settings, where wavelets are used to model real-world phenomena and data needs (such as finance, medicine, engineering, transport, images, signals, etc.).Features: Offers a self-contained discussion of wavelet theory Suitable for a wide audience of post-graduate students, researchers, practitioners, and theorists Provides researchers with detailed proofs Provides guides for readers to help them understand and practice wavelet analysis in different areas

Wavelet Analysis: Basic Concepts and Applications provides a basic and self-contained introduction to the ideas underpinning wavelet theory and its diverse applications. This book is suitable for master’s or PhD students, senior researchers, or scientists working in industrial settings, where wavelets are used to model real-world phenomena and data needs (such as finance, medicine, engineering, transport, images, signals, etc.).Features: Offers a self-contained discussion of wavelet theory Suitable for a wide audience of post-graduate students, researchers, practitioners, and theorists Provides researchers with detailed proofs Provides guides for readers to help them understand and practice wavelet analysis in different areas

Fractional Differential Equations: Theoretical Aspects and Applications presents the latest mathematical and conceptual developments in the field of Fractional Calculus and explores the scope of applications in research science and computational modeling. The book delves into these methods and applied computational modelling techniques, including analysis of equations involving fractional derivatives, fractional derivatives and the wave equation, analysis of FDE on groups, direct and inverse problems, functional inequalities, and computational methods for FDEs in physics and engineering. Other modeling techniques and applications explored include general fractional derivatives involving the special functions in analysis and fractional derivatives with respect to other functions. Fractional Calculus, the field of mathematics dealing with operators of differentiation and integration of arbitrary real or even complex order, extends many of the modelling capabilities of conventional calculus and integer-order differential equations and finds its application in various scientific areas, such as physics, mechanics, engineering, economics, finance, biology, and chemistry, among others.Provides the most recent and up-to-date developments in the theory and scientific applications Fractional Differential EquationsIncludes transportable computer source codes for readers in MATLAB, with code descriptions as it relates to the mathematical modelling and applicationsProvides readers with a comprehensive foundational reference for this key topic in computational modeling, which is a mathematical underpinning for most areas of scientific and engineering research

Computational Approaches to Emotion in Artificial Psychology provides readers with a comprehensive introduction to how emotions can be processed by AI systems. It offers theoretical and practical guidance on data preprocessing and emotion analysis techniques, explores diverse real-world applications, and bridges the gap between AI and psychology.Beginning with an introduction to the emerging field of artificial psychology, it explores the study, understanding, and recognition of emotions in various bodily signals, including facial expressions, voice, heart rate, and neural mechanisms. The book delves into data preprocessing for embodied emotion analysis, encompassing multiple data modalities like text, audio, visual, and gaze data, with a focus on Python basics for emotional AI. Additionally, it discusses EEG-based emotion decoding, emotional insights from medical imaging, affective image analysis, text-based emotion recognition, multimodal data integration, unsupervised learning for embodied emotion discovery, reinforcement learning, emotion elicitation, and predicting personality and emotional abilities using machine learning. The book concludes by examining the close relationship between cognition and emotion from the perspective of the universal structure of language and describing the use of deep fuzzy cognitive maps in diagnosing coronary artery disease.By promoting research and innovation through case studies and experiments, it addresses the current lack of comprehensive resources in this interdisciplinary field, making it an essential reference for researchers, practitioners, students, and professionals seeking to navigate the intersection of AI and emotions.

Computational Approaches to Emotion in Artificial Psychology provides readers with a comprehensive introduction to how emotions can be processed by AI systems. It offers theoretical and practical guidance on data preprocessing and emotion analysis techniques, explores diverse real-world applications, and bridges the gap between AI and psychology.Beginning with an introduction to the emerging field of artificial psychology, it explores the study, understanding, and recognition of emotions in various bodily signals, including facial expressions, voice, heart rate, and neural mechanisms. The book delves into data preprocessing for embodied emotion analysis, encompassing multiple data modalities like text, audio, visual, and gaze data, with a focus on Python basics for emotional AI. Additionally, it discusses EEG-based emotion decoding, emotional insights from medical imaging, affective image analysis, text-based emotion recognition, multimodal data integration, unsupervised learning for embodied emotion discovery, reinforcement learning, emotion elicitation, and predicting personality and emotional abilities using machine learning. The book concludes by examining the close relationship between cognition and emotion from the perspective of the universal structure of language and describing the use of deep fuzzy cognitive maps in diagnosing coronary artery disease.By promoting research and innovation through case studies and experiments, it addresses the current lack of comprehensive resources in this interdisciplinary field, making it an essential reference for researchers, practitioners, students, and professionals seeking to navigate the intersection of AI and emotions.

The book is devoted to recent developments in the theory of fractional calculus and its applications. Particular attention is paid to the applicability of this currently popular research field in various branches of pure and applied mathematics. In particular, the book focuses on the more recent results in mathematical physics, engineering applications, theoretical and applied physics as quantum mechanics, signal analysis, and in those relevant research fields where nonlinear dynamics occurs and several tools of nonlinear analysis are required. Dynamical processes and dynamical systems of fractional order attract researchers from many areas of sciences and technologies,ranging from mathematics and physics to computer science.

This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

The aim of this book is to provide a basic and self-contained introduction to the ideas underpinning fractal analysis. The book illustrates some important applications issued from real data sets, real physical and natural phenomena as well as real applications in different fields, and consequently, presents to the readers the opportunity to implement fractal analysis in their specialties according to the step-by-step guide found in the book.Besides advanced undergraduate students, graduate students and senior researchers, this book may also serve scientists and research workers from industrial settings, where fractals and multifractals are required for modeling real-world phenomena and data, such as finance, medicine, engineering, transport, images, signals, among others.For the theorists, rigorous mathematical developments are established with necessary prerequisites that make the book self-containing. For the practitioner often interested in model building and analysis, we provide the cornerstone ideas.

The book is focused on mathematical modeling of various problems arising in science and engineering and new efficient numerical approaches for solving linear nonlinear problems and rigorous mathematical theories, which can be used to analyze different kinds of mathematical models.