Advanced Studies in Complex Systems – serie

Nonlinear Control for Blood Glucose Regulation of Diabetic Patients: An LMI-Based Approach exposes readers to the various existing mathematical models that define the dynamics of glucose-insulin for Type 1 diabetes patients. After providing insights into the mathematical model of patients, the authors discuss the need and emergence of new control techniques that can lead to further development of an artificial pancreas. The book presents various nonlinear control techniques to address the challenges that Type 1 diabetic patients face in maintaining their blood glucose level in the safe range (70-180 mg/dl).

The closed-loop solution provided by the artificial pancreas depends mainly on the effectiveness of the control algorithm, which acts as the brain of the system. APS control algorithms require a mathematical model of the gluco-regulatory system of the T1D patients for their design. Since the gluco-regulatory system is inherently nonlinear and largely affected by external disturbances and parametric uncertainty, developing an accurate model is very difficult.

Presents control-oriented modeling of the gluco-regulatory system of Type 1 diabetic patients using input-output data Demonstrates the design of a robust insulin delivery mechanism utilizing state estimation information with parametric uncertainties and exogenous disturbance in the framework of Linear Matrix Inequality (LMI) Introduces readers to the relevance and effectiveness of powerful nonlinear controllers for the Artificial Pancreas Provides the first book on LMI-based nonlinear control techniques for the Artificial Pancreas

Fractional Order Systems and Applications in Engineering presents the use of fractional calculus (calculus of non-integer order) in the description and modelling of systems and in a range of control design and practical applications. The book covers the fundamentals of fractional calculus together with some analytical and numerical techniques, and provides MATLAB� codes for the simulation of fractional-order control (FOC) systems. The use of fractional calculus can improve and generalize well-established control methods and strategies. Many different FOC schemes are presented for control and dynamic systems problems. These extend to the challenging control engineering design problems of robust and nonlinear control. Practical material relating to a wide variety of applications including, among others, mechatronics, civil engineering, irrigation and water management, and biological systems is also provided. All the control schemes and applications are presented with either system simulation results or real experimental results, or both. Fractional Order Systems and Applications in Engineering introduces readers to the essentials of FOC and imbues them with a basic understanding of FOC concepts and methods. With this knowledge readers can extend their use of FOC in other industrial system applications, thereby expanding their range of disciplines by exploiting this versatile new set of control techniques. Provides the most recent and up-to-date developments on the Fractional-order Systems and their analyzing process Integrates recent advancements of modeling of real phenomena (on Fractional-order Systems) via different-different mathematical equations with demonstrated applications in numerous seemingly diverse and widespread fields of science and engineering Provides readers with illustrative examples of how to use the presented theories of Fractional-order Systems in specific cases with associated MATLAB code

Computational Intelligence and Blockchain in Complex Systems: System Security and Interdisciplinary Applications provides readers with a guide to understanding the dynamics of AI, machine learning, and computational intelligence in blockchain, and how these rapidly developing technologies are revolutionizing a variety of interdisciplinary research fields and applications. This book examines the role of computational intelligence and machine learning in the development of algorithms to deploy blockchain technology across a number of applications, including healthcare, insurance, smart grid, smart contracts, digital currency, precision agriculture, and supply chain. The authors cover the unique and developing intersection between cyber security and blockchain in modern networks, as well as in-depth studies on cybersecurity challenges and multidisciplinary methods in modern blockchain networks. Readers will find mathematical equations throughout the book as part of the underlying concepts and foundational methods, especially the complex algorithms involved in blockchain security aspects for hashing, coding, and decoding. This book also provides readers with the most in-depth technical guide to the intersection of computational intelligence and blockchain, two of the most important technologies for the development of next-generation complex systemsCovers the research issues and concepts of machine learning technology in blockchainProvides in-depth information about handling and managing personal data by machine learning methods in blockchainHelps readers understand the links between computational intelligence, blockchain, complex systems, and developing secure applications in multidisciplinary sectors

Methods of Mathematical Modeling: Advances and Applications delves into recent progress in this field, highlighting innovative methods and their uses in different domains. This book covers convergence analysis involving nonlinear integral equations and boundary value problems, Navier-Stokes equations in Sobolev-Gevrey spaces, magneto-hydrodynamics of ternary nanofluids with heat transfer effects, vortex nerve complexes in video frame shape approximation, hybrid schemes for computing hyperbolic conservation laws, and solutions to new fractional differential equations. Additionally, the book examines dynamics of Leslie-Gower type predator-prey models and models for the dynamics of generic crop and water availability.Readers will find diverse approaches, techniques, and applications needed for modeling various physical and natural systems. Each chapter is self-contained, encouraging independent study and application of the modeling examples to individual research projects. This book serves as a valuable resource for researchers, students, educators, scientists, and practitioners involved in different aspects of modeling.Provides new mathematical methods and techniques for modeling various physical and natural systemsIncludes new hybrid computational schemes and procedures for handling wave interactionsIncludes advanced-level convergence analysis and generalized Navier-Stokes equationsProvides readers with the dynamics of predator-prey, generic crop, and water availability models

Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications introduces readers to the methodology and AI/ML algorithms as well as cutting-edge applications to medicine, such as cancer, precision medicine, critical care, personalized medicine, telemedicine, drug discovery, molecular characterization, and patient mental health. Research in medicine and tailored clinical treatment are being quickly transformed by artificial intelligence (AI) and machine learning (ML). The content in this book is tailored to the reader's needs in terms of both type and fundamentals. It covers the current ethical issues and potential developments in this field.This book will be beneficial for academics, professionals in the IT industry, educators, students, and anyone else involved in the use and development of AI in the medical field.Covers the basic concepts of Artificial Intelligence and Machine Learning, methods and practices, and advanced topics and applications to clinical and precision medicinePresents readers with an understanding of how AI is revolutionizing medicine by demonstrating the applications of computational intelligence to the field, along with an awareness of how AI can improve upon traditional medical structuresProvides researchers, practitioners, and project stakeholders with a complete guide for applying AI techniques in their projects and solutions

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

Mathematical Modeling in Bioscience: Theory and Applications provides readers with tools and techniques for mathematical modeling in bioscience through a wide range of novel and intriguing topics. The book concentrates on larger elements of mathematical modeling in bioscience, including topics such as modeling of the Topp--Leone new power generalized Weibull-G distribution family, vector-borne disease modeling, transmission modeling of SARS-COV-2 among other infectious diseases, pattern formulation models, compartmental models for HIV/AIDS transmission, population models, irrigation scheduling models, and predator--prey models. The readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in bioscience modeling. This book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in mathematical modeling, including mathematics, statistics, biology, biomedical engineering, computer science, and applied sciences.Provides key concepts for advanced mathematical methods for modeling in bioscienceIncludes statistical, delay, random, and stochastic mathematical modelsFocuses on broader aspects of mathematical models in bioscienceProvides the readers with several types of dynamic representative applications

Recent Trends in Fractional Calculus and Its Applications addresses the answer to this very basic question: "Why is Fractional Calculus important?" Until recent times, Fractional Calculus was considered as a rather esoteric mathematical theory without applications, but in the last few decades there has been an explosion of research activities on the application of Fractional Calculus to very diverse scientific fields ranging from the physics of diffusion and advection phenomena, to control systems to finance and economics. An important part of mathematical modelling of objects and processes is a description of their dynamics.The term Fractional Calculus is more than 300 years old. It is a generalization of the ordinary differentiation and integration to noninteger (arbitrary) order. The subject is as old as the calculus of differentiation and goes back to times when Leibniz, Gauss, and Newton invented this kind of calculation. Several mathematicians contributed to this subject over the years. People like Liouville, Riemann, and Weyl made major contributions to the theory of Fractional Calculus. In recent decades the field of Fractional Calculus has attracted the interest of researchers in several areas, including mathematics, physics, chemistry, engineering, finance, and social sciences.Provides the most recent and up-to-date developments in the Fractional Calculus and its application areasPresents pre-preparation ideas to help researchers/scientists/clinicians face the new challenges in the application of fractional differential equationsHelps researchers and scientists understand the importance of the Fractional Calculus to solve many problems in Biomedical Engineering and applied sciences

Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others.The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science.

Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithmsEncourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision designProvides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision

Picture Fuzzy Logic and Its Applications in Decision Making Problems provides methodological frameworks and the latest empirical research findings in the field of picture fuzzy operators and their applications in scientific research and real-world engineering problems. In this book, picture fuzzy sets are investigated, and different types of operators are defined to solve a number of important decision-making and optimization problems. The hybrid operator on picture fuzzy set based on the combination of picture fuzzy weighted averaging operators and picture fuzzy weighted geometric operators is developed and named Hybrid Picture Fuzzy Weighted Averaging Geometric (H-PFWAG) operator. In addition, another operator is developed for interval-valued picture fuzzy environment, which is named Hybrid Interval-Valued Picture Fuzzy Weighted Averaging Geometric (H-IVPFWAG) operator. These two operators are then demonstrated as solutions to Multiple-Attribute Decision-Making (MADM) problems. The picture fuzzy soft weighted aggregation operators (averaging and geometric) are defined, and these are applied to develop a multi-criteria group decision making system.Provides in-depth explanations of picture fuzzy logic and its application to computational modeling problemsHelps readers understand the difference between various fuzzy logic methodsProvides concepts used to develop and solve problems within the picture fuzzy environment

Artificial Intelligence of Things (AIoT): Current and Future Trends brings together researchers and developers from a wide range of domains to share ideas on how to implement technical advances, create application areas for intelligent systems, and how to develop new services and smart devices connected to the Internet. Section One covers AIoT in Everything, providing a wide range of applications for AIoT methods and technologies. Section Two gives readers comprehensive guidance on AIoT in Societal Research and Development, with practical case studies of how AIoT is impacting cultures around the world. Section Three covers the impact of AIoT in educational settings.The book also covers new capabilities such as pervasive sensing, multimedia sensing, machine learning, deep learning, and computing power. These new areas come with various requirements in terms of reliability, quality of service, and energy efficiency.Provides readers with up-to-date and comprehensive information on the latest advancements in AIoT, including wireless technologies, pervasive sensing, multimedia sensing, machine learning, deep learning, and computing powerExplores the possibilities of new domains, services, and business models that can be created using AIoTDiscusses the potential impact of AIoT on society, including its potential to improve efficiency, reduce costs, and enhance quality of life

Mathematical Methods in Medical and Biological Sciences presents mathematical methods for computational models arising in the medical and biological sciences. The book presents several real-life medical and biological models, such as infectious and non-infectious diseases that can be modeled mathematically to accomplish?profound research in virtual environments when the cost of laboratory expenses is relatively high. It focuses on mathematical techniques that provide global solutions for models arising in medical and biological sciences by considering their long-term benefits.In addition, the book provides leading-edge developments and insights for a range of applications, including epidemiological modeling of pandemic dynamics, viral infection developments, cancer developments, blood oxygen dynamics, HIV infection spread, reaction-diffusion models, polio infection spread, and chaos modeling with fractional order derivatives.

Presents the mathematical treatment of a wide range of real-life medical and biological models, including both infectious and non-infectious diseasesProvides in-depth analysis of the spread of Covid-19, polio, and HIV, including discussion of computational methods and applicationsIncludes computational modeling methods, along with their practical applications, providing the basis for further exploration and research in epidemiology and applied biomedical sciences

Fractional Calculus: Bridging Theory with Computational and Contemporary Advances is an authoritative and comprehensive guide that delves into the world of fractional calculus, offering a unique blend of theoretical foundations, numerical algorithms, practical applications, and innovative perspectives. This book explores the mathematical framework of fractional calculus and its relevance across various disciplines, providing readers with a deep understanding of this rapidly growing field. The author presents a rigorous yet accessible approach to fractional calculus, making it suitable for mathematicians, researchers, academics, graduate students, and professionals in engineering and applied sciences. The book covers a wide range of topics, including numerical methods for fractional calculus equations, fractional differential equations, fractal dynamics, and fractional control systems. It also explores applications in areas such as physics, engineering, signal processing, and data analysis. Fractional Calculus: Bridging Theory with Computational and Contemporary Advances equips readers with the necessary tools to tackle challenging problems involving fractional calculus, empowering them to apply these techniques in their research, professional work, or academic pursuits. The book provides a comprehensive introduction to the fundamentals of fractional calculus, explaining the theoretical concepts and key definitions in a clear and accessible manner. This helps readers build a strong foundation in the subject. The book then covers a range of numerical algorithms specifically designed for fractional calculus problems, explaining the underlying principles, step-by-step implementation, and computational aspects of these algorithms. This enables readers to apply numerical techniques to solve fractional calculus problems effectively. The book also provides examples that illustrate how fractional calculus is applied to solve real-world problems, providing readers with insights into the wide-ranging applications of the subject.Provides a comprehensive introduction to the fundamentals of fractional calculus, explaining the theoretical concepts and key definitions in a clear and accessible mannerCovers a range of numerical algorithms specifically designed for fractional calculus problemsIncludes practical examples and case studies from various fields such as physics, biology, finance, and signal processing

Fixed Point Optimization Algorithms and Their Applications discusses how the relationship between fixed point algorithms and optimization problems is connected and demonstrates hands-on applications of the algorithms in fields such as image restoration, signal recovery, and machine learning. The book is divided into nine chapters beginning with foundational concepts of normed linear spaces, Banach spaces, and Hilbert spaces, along with nonlinear operators and useful lemmas and theorems for proving the book’s main results. The author presents algorithms for nonexpansive and generalized nonexpansive mappings in Hilbert space, and presents solutions to many optimization problems across a range of scientific research and real-world applications. From foundational concepts, the book proceeds to present a variety of optimization algorithms, including fixed point theories, convergence theorems, variational inequality problems, minimization problems, split feasibility problems, variational inclusion problems, and equilibrium problems. Fixed Point Optimization Algorithms and Their Applications equips readers with the theoretical mathematics background and necessary tools to tackle challenging optimization problems involving a range of algebraic methods, empowering them to apply these techniques in their research, professional work, or academic pursuits.Demonstrates how to create hybrid algorithms for many optimization problems with non-expansive mappings to solve real-world problemsShows readers how to solve image restoration problems using optimization algorithmsIncludes coverage of signal recovery problems using optimization algorithmsShows readers how to solve data classification problems using optimization algorithms in machine learning with many types of datasets, such as those used in medicine, mathematics, computer science, and engineering