Tofigh Allahviranloo – författare

Soft Numerical Computing in Uncertain Dynamic Systems is intended for system specialists interested in dynamic systems that operate at different time scales. The book discusses several types of errors and their propagation, covering numerical methods-including convergence and consistence properties and characteristics-and proving of related theorems within the setting of soft computing. Several types of uncertainty representation like interval, fuzzy, type 2 fuzzy, granular, and combined uncertain sets are discussed in detail. The book can be used by engineering students in control and finite element fields, as well as all engineering, applied mathematics, economics, and computer science students.

One of the important topics in applied science is dynamic systems and their applications. The authors develop these models and deliver solutions with the aid of numerical methods. Since they are inherently uncertain, soft computations are of high relevance here. This is the reason behind investigating soft numerical computing in dynamic systems. If these systems are involved with complex-uncertain data, they will be more practical and important. Real-life problems work with this type of data and most of them cannot be solved exactly and easily-sometimes they are impossible to solve.

Clearly, all the numerical methods need to consider error of approximation. Other important applied topics involving uncertain dynamic systems include image processing and pattern recognition, which can benefit from uncertain dynamic systems as well. In fact, the main objective is to determine the coefficients of a matrix that acts as the frame in the image. One of the effective methods exhibiting high accuracy is to use finite differences to fill the cells of the matrix.

Explores dynamic models, how time is fundamental to the structure of the model and data, and how a process unfolds Investigates the dynamic relationships between multiple components of a system in modeling using mathematical models and the concept of stability in uncertain environments Exposes readers to many soft numerical methods to simulate the solution function's behavior

Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems.Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.Introduces mathematics of intelligent systems which provides the usage of mathematical rigor such as precise definitions, theorems, results, and proofsProvides extended and new comprehensive methods which can be used efficiently in a fuzzy environment as well as optimization problems and related fieldsCovers applications and elaborates on the usage of the developed methodology in various fields of industry such as software technologies, biomedicine, image processing, and communications

Quantum Theory, Decision Making, and Social Dynamics is a detailed exploration of the connection between quantum theory, decision-making, and social networks. As quantum theory expands into various fields, there is an increasing demand for accessible resources that clarify its principles and uses. This book aims to address that need by explaining the complex relationship between quantum theory and social dynamics, especially in decision-making contexts. It discusses the challenges of understanding and applying quantum theory in social settings and provides readers with the knowledge to leverage its potential in decision-making processes. The book is divided into eleven chapters, each focusing on a specific aspect of quantum theory and its applications. Chapter 1 introduces quantum theory, fuzzy logic, and social network analysis, highlighting key concepts like superposition, entanglement, and fuzzy influence within networks. Chapter 2 examines fuzzy sets, membership functions, and inference systems, with applications in devices, traffic management, and healthcare. Chapter 3 covers the mathematical framework of quantum mechanics and its philosophical paradoxes, connecting them to fuzzy logic models of uncertainty. Chapter 4 links social networks to quantum graphs, defining their topology, centrality, and entangled edges. Chapter 5 models social identity as a fuzzy quantum superposition, exploring identity collapse and coherence within networks. Chapter 6 relates quantum entanglement to social ties, proposing fuzzy-quantum graph models for interconnected systems. Chapter 7 analyses measures of irregularity in quantum graphs and applies these to financial networks. Chapter 8 integrates quantum cognition with fuzzy MCDM, employing various probability evaluation methods. Chapter 9 features case studies of fuzzy systems and their integration with quantum fuzzy graphs. Chapter 10 develops a quantum graph-based link prediction model for dynamic social networks. Chapter 11 concludes with a summary of the quantum-fuzzy framework, discussing its contributions, limitations, and future directions.Integrated Framework - Combines quantum theory, fuzzy logic, and social network analysis for modeling uncertainty and complexity.Novel Models - Presents quantum graphs, fuzzy-quantum identity, and entanglement-based social network representations.Real-World Applications - Includes practical case studies in decision-making, network analysis, and predictive modeling.

Numerical analysis forms a cornerstone of numeric computing and optimization, in particular recently, interval numerical computations play an important role in these topics. The interest of researchers in computations involving uncertain data, namely interval data opens new avenues in coping with real-world problems and deliver innovative and efficient solutions. This book provides the basic theoretical foundations of numerical methods, discusses key technique classes, explains improvements and improvements, and provides insights into recent developments and challenges.The theoretical parts of numerical methods, including the concept of interval approximation theory, are introduced and explained in detail. In general, the key features of the book include an up-to-date and focused treatise on error analysis in calculations, in particular the comprehensive and systematic treatment of error propagation mechanisms, considerations on the quality of data involved in numerical calculations, and a thorough discussion of interval approximation theory.Moreover, this book focuses on approximation theory and its development from the perspective of linear algebra, and new and regular representations of numerical integration and their solutions are enhanced by error analysis as well. The book is unique in the sense that its content and organization will cater to several audiences, in particular graduate students, researchers, and practitioners.

Numerical analysis forms a cornerstone of numeric computing and optimization, in particular recently, interval numerical computations play an important role in these topics. The interest of researchers in computations involving uncertain data, namely interval data opens new avenues in coping with real-world problems and deliver innovative and efficient solutions. This book provides the basic theoretical foundations of numerical methods, discusses key technique classes, explains improvements and improvements, and provides insights into recent developments and challenges.The theoretical parts of numerical methods, including the concept of interval approximation theory, are introduced and explained in detail. In general, the key features of the book include an up-to-date and focused treatise on error analysis in calculations, in particular the comprehensive and systematic treatment of error propagation mechanisms, considerations on the quality of data involved in numerical calculations, and a thorough discussion of interval approximation theory.Moreover, this book focuses on approximation theory and its development from the perspective of linear algebra, and new and regular representations of numerical integration and their solutions are enhanced by error analysis as well. The book is unique in the sense that its content and organization will cater to several audiences, in particular graduate students, researchers, and practitioners.

This book identifies the important uncertainties to use in real-world problem modeling. Presenting problems that can be represented using mathematical models to create a system of linear equations, this book discusses the latest insights into uncertain information.

This book identifies the important uncertainties to use in real-world problem modeling. Presenting problems that can be represented using mathematical models to create a system of linear equations, this book discusses the latest insights into uncertain information.

This book contains new and useful materials concerning fuzzy fractional differential and integral operators and their relationship. Most of the real problems in the biological and engineering sciences involve dynamic models, which are defined by fuzzy fractional operators in the form of fuzzy fractional initial value problems.

This book contains new and useful materials concerning fuzzy fractional differential and integral operators and their relationship. Most of the real problems in the biological and engineering sciences involve dynamic models, which are defined by fuzzy fractional operators in the form of fuzzy fractional initial value problems.

The concept of uncertainty has long been used in applied science, especially decision making and a logical decision must be made in the field of uncertainty or in the real-life environment that is formed and combined with vague concepts and data.

The first part points out the fuzzy differential equations and the second one is related to the fuzzy integral equations. The book contains nine chapters that six chapters are about fuzzy differential equations and three of them are about fuzzy integral equations.

The first part points out the fuzzy differential equations and the second one is related to the fuzzy integral equations. The book contains nine chapters that six chapters are about fuzzy differential equations and three of them are about fuzzy integral equations.

This book delivers a concise and carefully structured introduction to probability and random variables. Each chapter includes solved examples at varying levels (both introductory and advanced) in addition to problems that demonstrate the relevance of the probability and random variables in engineering.

This book delivers a concise and carefully structured introduction to probability and random variables. Each chapter includes solved examples at varying levels (both introductory and advanced) in addition to problems that demonstrate the relevance of the probability and random variables in engineering.

This book suggests that the numerical analysis subjects’ matter are the important tools of the book topic, because numerical errors and methods have important roles in solving integral equations. Therefore, all needed topics including a brief description of interpolation are explained in the book. The integral equations have many applications in the engineering, medical, and economic sciences, so the present book contains new and useful materials about interval computations including interval interpolations that are going to be used in interval integral equations.The concepts of integral equations are going to be discussed in two directions, analytical concepts, and numerical solutions which both are necessary for these kinds of dynamic systems. The differences between this book with the others are a full discussion of error topics and also using interval interpolations concepts to obtain interval integral equations. All researchers and students in the field of mathematical, computer, and also engineering sciences can benefit the subjects of the book.

This book suggests that the numerical analysis subjects’ matter are the important tools of the book topic, because numerical errors and methods have important roles in solving integral equations. Therefore, all needed topics including a brief description of interpolation are explained in the book. The integral equations have many applications in the engineering, medical, and economic sciences, so the present book contains new and useful materials about interval computations including interval interpolations that are going to be used in interval integral equations.The concepts of integral equations are going to be discussed in two directions, analytical concepts, and numerical solutions which both are necessary for these kinds of dynamic systems. The differences between this book with the others are a full discussion of error topics and also using interval interpolations concepts to obtain interval integral equations. All researchers and students in the field of mathematical, computer, and also engineering sciences can benefit the subjects of the book.

The authors of this book tried to make these experiences available to those interested, considering the experience of several years of training, research, and implementation of projects in the supply chain performance evaluation field.This book intends to identify the current performance and competitive position of that supply chain compared to other supply chains by presenting and reviewing the techniques and models for measuring the efficiency and performance of the supply chain. Determining the performance of a supply chain is a good description of the status quo (what is). Determining the performance of a supply chain is useful for describing the past and present of supply chain processes, and on the other hand, it can be used to set performance goals and initiate the improvement process. To realize this, a strategic framework or model is needed to be able to extract indicators related to the efficiency of the supply chain and design the appropriate model.

The authors of this book tried to make these experiences available to those interested, considering the experience of several years of training, research, and implementation of projects in the supply chain performance evaluation field.This book intends to identify the current performance and competitive position of that supply chain compared to other supply chains by presenting and reviewing the techniques and models for measuring the efficiency and performance of the supply chain. Determining the performance of a supply chain is a good description of the status quo (what is). Determining the performance of a supply chain is useful for describing the past and present of supply chain processes, and on the other hand, it can be used to set performance goals and initiate the improvement process. To realize this, a strategic framework or model is needed to be able to extract indicators related to the efficiency of the supply chain and design the appropriate model.

In preferential voting, voters vote for multiple candidates, and how the candidates are arranged on the ballot is important.

In preferential voting, voters vote for multiple candidates, and how the candidates are arranged on the ballot is important.

The combination of DEA and ratio analysis is introduced as a suitable field for evaluating the performance of DMUs. Therefore, the relationship between DEA and DEA-R provides a suitable field for researchers in the field of evaluating the performance of DMUs.

The combination of DEA and ratio analysis is introduced as a suitable field for evaluating the performance of DMUs. Therefore, the relationship between DEA and DEA-R provides a suitable field for researchers in the field of evaluating the performance of DMUs.

Authored by a leading expert in the field, this book introduces an innovative methodology that harnesses the power of fuzzy logic to enhance decision-making in multi-attribute scenarios. In a world of complexity and uncertainty, effective decision-making is paramount. Springer proudly presents a cutting-edge publication that revolutionizes decision analysis: "Fuzzy Decision Analysis: Multi-attribute Decision-Making Approach."  This book stands at the forefront of decision analysis, introducing the integration of fuzzy logic into multi-attribute decision-making. It is a transformative journey into the realm of advanced decision analysis. It book not only equips you with the knowledge to comprehend the theoretical underpinnings but also empowers you to apply these insights in practical scenarios. This book serves as your indispensable companion. Its comprehensive coverage serves as a beacon, guiding you through the intricate maze of fuzzy logic and multi-attribute decision-making, ultimately empowering you to embrace innovation and master the art of making well-informed decisions in an ever-changing world.

Authored by a leading expert in the field, this book introduces an innovative methodology that harnesses the power of fuzzy logic to enhance decision-making in multi-attribute scenarios. Springer proudly presents a cutting-edge publication that revolutionizes decision analysis: "Fuzzy Decision Analysis: Multi-attribute Decision-Making Approach."

This book chooses the topic which is due to the editors' experience in modeling projects in healthcare systems. To this end, the new aspect of this book is the lack of reference needed to carry out projects in the field of health for researchers whose main expertise is not modeling.

This book chooses the topic which is due to the editors' experience in modeling projects in healthcare systems. To this end, the new aspect of this book is the lack of reference needed to carry out projects in the field of health for researchers whose main expertise is not modeling.

The main target of this book is to present a new concept of Ulam-type stability, i.e., multi-stability, through the classical, well-known special functions and to obtain the best approximation error estimates by a different concept of perturbation stability including fuzzy approaches for uncertainty considerations.

The main target of this book is to present a new concept of Ulam-type stability, i.e., multi-stability, through the classical, well-known special functions and to obtain the best approximation error estimates by a different concept of perturbation stability including fuzzy approaches for uncertainty considerations.