Sunil Mathew

Delve into the intricate landscape of fuzzy mathematics, where the boundaries of traditional mathematical disciplines— analysis, abstract algebra, geometry, topology, and graph theory—are blurred to address pressing global issues. Through a rigorous examination of fuzzy sets and similarity measures, An Introduction to Fuzzy Mathematics: With Applications to Global Problems lays the groundwork for innovative solutions to complex problems, from medical diagnostics to sustainability, refugee crises, and the fight against human trafficking. Meanwhile, research projects and exercises integrated across chapters reinforce learning and apply fuzzy mathematics to real-world scenarios. Chapters are meticulously organized to guide readers through foundational concepts, including fuzzy sets, evidence theory, and implication operators, before advancing to applications in sustainability and climate change. Further, the book examines refugee dynamics and public health models, culminating in a thorough exploration of fuzzy algebraic structures, geometry, topology, and graph theory. This comprehensive resource not only enhances understanding of fuzzy mathematics but also equips readers—researchers, practitioners, and policymakers alike—with the tools to tackle critical global issues. By integrating mathematical rigor with real-life applications, the book serves as a vital reference for anyone seeking to navigate the complexities of our world through the lens of fuzzy mathematics.

This book reports on advanced concepts in fuzzy graph theory, showing a set of tools that can be successfully applied to understanding and modeling illegal human trafficking. Building on the previous book on fuzzy graph by the same authors, which set the fundamentals for readers to understand this developing field of research, this second book gives a special emphasis to applications of the theory. For this, authors introduce new concepts, such as intuitionistic fuzzy graphs, the concept of independence and domination in fuzzy graphs, as well as directed fuzzy networks, incidence graphs and many more.

This book reports on advanced concepts in fuzzy graph theory, showing a set of tools that can be successfully applied to understanding and modeling illegal human trafficking. Building on the previous book on fuzzy graph by the same authors, which set the fundamentals for readers to understand this developing field of research, this second book gives a special emphasis to applications of the theory. For this, authors introduce new concepts, such as intuitionistic fuzzy graphs, the concept of independence and domination in fuzzy graphs, as well as directed fuzzy networks, incidence graphs and many more.

This book provides a timely overview of fuzzy graph theory, laying the foundation for future applications in a broad range of areas. It introduces readers to fundamental theories, such as Craine’s work on fuzzy interval graphs, fuzzy analogs of Marczewski’s theorem, and the Gilmore and Hoffman characterization. It also introduces them to the Fulkerson and Gross characterization and Menger’s theorem, the applications of which will be discussed in a forthcoming book by the same authors. This book also discusses in detail important concepts such as connectivity, distance and saturation in fuzzy graphs. Thanks to the good balance between the basics of fuzzy graph theory and new findings obtained by the authors, the book offers an excellent reference guide for advanced undergraduate and graduate students in mathematics, engineering and computer science, and an inspiring read for all researchers interested in new developments in fuzzy logic and applied mathematics.

Fuzzy Mathematics, Graphs, and Similarity Measures provides a solid foundation in core analytical tracks of mathematics of uncertainty, from fuzzy mathematics to graphs and similarity measures with applications in a range of timely cases studies and world challenges. Following a full grounding in fuzzy graph indices, connectivity in fuzzy graph structures, lattice isomorphisms, and similarity measures, the book applies these models in analyzing world challenges, from human trafficking to modern slavery, global poverty, global hunger, homelessness, biodiversity, extinction, terrorism and bioterrorism, pandemics, and climate change. Connections and constructive steps forward are tied throughout to UN Sustainable Development Goals (SDGs). The authors demonstrate and instruct readers in applying techniques from mathematics of uncertainty in examining issues where accurate data is impossible to obtain. In addition to a diverse range of cases studies, exercises reinforce key concepts in each chapter, and an online instructor’s manual supports teaching across a range of course contexts.

One of the most preeminent ways of applying mathematics in real-world scenario modeling involves graph theory. A graph can be undirected or directed depending on whether the pairwise relationships among objects are symmetric or not. Nevertheless, in many real-world situations, representing a set of complex relational objects as directed or undirected is not su¢ cient. Weighted graphs o§er a framework that helps to over come certain conceptual limitations. We show using the concept of an isomorphism that weighted graphs have a natural connection to fuzzy graphs. As we show in the book, this allows results to be carried back and forth between weighted graphs and fuzzy graphs. This idea is in keeping with the important paper by Klement and Mesiar that shows that many families of fuzzy sets are lattice isomorphic to each other. We also outline the important work of Head and Weinberger that show how results from ordinary mathematics can be carried over to fuzzy mathematics. We focus on the concepts connectivity, degree sequences and saturation, and intervals and gates in weighted graphs.

This book focuses on the relationship between Artificial Intelligence (AI) and the Sustainable Development Goals (SDGs). One of the most important issues the world is facing is sustainability. All members of the United Nations adopted Agenda 2030 and the Sustainable Development Goals. Associated with the SDGs are many serious global problems. These problems include climate change, global poverty, global hunger, human trafficking, homelessness, biodiversity, extinction, and terrorism. The author’s purpose is to develop a textbook concerning the SDGs and how they relate to these problems. A recent issue has emerged. That is Artificial Intelligence. AI has tremendous potential to do both good and harm. The book is designed in such a way that it can be used as an undergraduate textbook on sustainability. Consequently, the mathematics involved is kept at a reasonable level. One of the author’s goals is to prepare the reader to further their study of global issues. In particular, to provide them the background needed to examine the three books in the Springer Series Studies in Systems, Decision and Control, volumes 299, 353, and 391. Exercises are provided in the text as well as an instructor's manual. Some topics examined in the book include country achievement of the SDGs, the relationship between human trafficking and the SDGs, climate change, and global problems mentioned above.

This book examines some issues involving climate change, human trafficking, and other serious world challenges made worse by climate change. Climate change increases the risk of natural disasters and thus creates poverty and can cause situations of conflict and instability. Displacement can occur giving traffickers an opportunity to exploit affected people. In the fuzzy graph theory part of the book, the relatively new concepts of fuzzy soft semigraphs and graph structures are used to study human trafficking, as well as its time intuitionistic fuzzy sets that have been introduced to model forest fires. The notion of legal and illegal incidence strength is used to analyze immigration to the USA. The examination of return refugees to their origin countries is undertaken. The neighborhood connectivity index is determined for trafficking in various regions in the world. The cycle connectivity measure for the directed graph of the flow from South Americato the USA is calculated. It is determined that there is a need for improvement in government response by countries. Outside the area of fuzzy graph theory, a new approach to examine climate change is introduced. Social network theory is used to study feedback processes that effect climate forcing. Tipping points in climate change are considered. The relationship between terrorism and climate change is examined. Ethical issues concerning the obligation of business organizations to reduce carbon emissions are also considered. Nonstandard analysis is a possible new area that could be used by scholars of mathematics of uncertainty. A foundation is laid to aid the researcher in the understanding of nonstandard analysis. In order to accomplish this, a discussion of some basic concepts from first-order logic is presented as some concepts of mathematics of uncertainty. An application to the theory of relativity is presented.

One of the most preeminent ways of applying mathematics in real-world scenario modeling involves graph theory. A graph can be undirected or directed depending on whether the pairwise relationships among objects are symmetric or not. Nevertheless, in many real-world situations, representing a set of complex relational objects as directed or undirected is not su¢ cient. Weighted graphs o§er a framework that helps to over come certain conceptual limitations. We show using the concept of an isomorphism that weighted graphs have a natural connection to fuzzy graphs. As we show in the book, this allows results to be carried back and forth between weighted graphs and fuzzy graphs. This idea is in keeping with the important paper by Klement and Mesiar that shows that many families of fuzzy sets are lattice isomorphic to each other. We also outline the important work of Head and Weinberger that show how results from ordinary mathematics can be carried over to fuzzy mathematics. We focus on the concepts connectivity, degree sequences and saturation, and intervals and gates in weighted graphs.

This book examines some issues involving climate change, human trafficking, and other serious world challenges made worse by climate change. Climate change increases the risk of natural disasters and thus creates poverty and can cause situations of conflict and instability. Displacement can occur giving traffickers an opportunity to exploit affected people. In the fuzzy graph theory part of the book, the relatively new concepts of fuzzy soft semigraphs and graph structures are used to study human trafficking, as well as its time intuitionistic fuzzy sets that have been introduced to model forest fires. The notion of legal and illegal incidence strength is used to analyze immigration to the USA. The examination of return refugees to their origin countries is undertaken. The neighborhood connectivity index is determined for trafficking in various regions in the world. The cycle connectivity measure for the directed graph of the flow from South Americato the USA is calculated. It is determined that there is a need for improvement in government response by countries. Outside the area of fuzzy graph theory, a new approach to examine climate change is introduced. Social network theory is used to study feedback processes that effect climate forcing. Tipping points in climate change are considered. The relationship between terrorism and climate change is examined. Ethical issues concerning the obligation of business organizations to reduce carbon emissions are also considered. Nonstandard analysis is a possible new area that could be used by scholars of mathematics of uncertainty. A foundation is laid to aid the researcher in the understanding of nonstandard analysis. In order to accomplish this, a discussion of some basic concepts from first-order logic is presented as some concepts of mathematics of uncertainty. An application to the theory of relativity is presented.

This book provides an examination of major problems facing the world using mathematics of uncertainty. These problems include climate change, coronavirus pandemic, human tracking, biodiversity, and other grand challenges. Mathematics of uncertainty is used in a modern more general sense than traditional mathematics. Since accurate data is impossible to obtain concerning human tracking and other global problems, mathematics of uncertainty is an ideal discipline to study these problems. The authors place several scientific studies into different mathematical settings such as nonstandard analysis and soft logic. Fuzzy differentiation is used to model the spread of diseases such as the coronavirus. The book uses fuzzy graph theory to examine the problems of human tracking and illegal immigration. The book is an excellent reference source for advanced under-graduate and graduate students in mathematics and the social sciences as well as for researchers and teachers.

This book ranks countries with respect to their achievement of the Sustainable Development Goals and their vulnerability to climate change. Human livelihoods, stable economies, health, and high quality of life all depend on a stable climate and earth system, and a diversity of species and ecosystems. Climate change significantly impacts human trafficking, modern slavery, and global hunger. This book examines these global problems using techniques from mathematics of uncertainty. Since accurate data concerning human trafficking and modern slavery is impossible to obtain, mathematics of uncertainty is an ideal discipline to study these problems. The book also considers the interconnection between climate change, world hunger, human trafficking, modern slavery, and the coronavirus. Connectivity properties of fuzzy graphs are used to examine trafficking flow between regions in the world. The book is an excellent reference source for advanced undergraduate and graduate students in mathematics and the social sciences as well as for researchers and teachers.

This book provides an examination of major problems facing the world using mathematics of uncertainty. These problems include climate change, coronavirus pandemic, human tracking, biodiversity, and other grand challenges. Mathematics of uncertainty is used in a modern more general sense than traditional mathematics. Since accurate data is impossible to obtain concerning human tracking and other global problems, mathematics of uncertainty is an ideal discipline to study these problems. The authors place several scientific studies into different mathematical settings such as nonstandard analysis and soft logic. Fuzzy differentiation is used to model the spread of diseases such as the coronavirus. The book uses fuzzy graph theory to examine the problems of human tracking and illegal immigration. The book is an excellent reference source for advanced under-graduate and graduate students in mathematics and the social sciences as well as for researchers and teachers.

This book uses mathematics of uncertainty to examine how well countries are achieving the 17 Sustainable Development Goals (SDGs) set by the members of the United Nations, with a focus on climate change, human trafficking and modern slavery. Although this approach has never been used before, mathematics of uncertainty is well suited to exploring these topics due to the lack of accurate data available. The authors place several scientific studies in a mathematical setting to pave the way for future research on issues of sustainability, climate change, human trafficking and modern slavery to using a wide range of mathematical techniques. Moreover, the book ranks countries in terms of their achievement of not only the SDGs, but in particular those SDGs pertinent to climate change, human trafficking, and modern slavery, and highlights the deficiencies in the foster care system that lead to human trafficking. As such it is an excellent reference resource for advanced undergraduate and graduate students in mathematics and the social sciences, as well as for researchers and teachers.

Bottlenecks always create delay and traffic jams in networks. Allowing more paths and cycles of larger capacities can ease this problem to some extent. A container in a graph is a family of internally disjoint paths between pairs of vertices. Analogously, a container in a fuzzy graph is a collection of internally disjoint strongest paths. Strongest paths always contribute the maximum towards the flow of a network. Hence the study of strongest paths and containers helps in analysing and controlling their performance. The size of a path in a fuzzy graph is the sum of membership values of its edges. A path of maximum size is said to be a heavy path. Some results on heavy paths and heavy cycles are discussed. The concept of a Hamiltonian fuzzy graph is also studied. A sufficient condition for a fuzzy graph to be Hamiltonian is obtained. A generalisation for Dirac's theorem in graph theory is discussed in a fuzzy set up. Also two classic results on graph degrees are generalised to fuzzy graphs. Algorithms for the determination of heavy paths and heavy Hamilton cycles and an application of heavy paths in human trafficking are also proposed towards the end.

This book ranks countries with respect to their achievement of the Sustainable Development Goals and their vulnerability to climate change. Human livelihoods, stable economies, health, and high quality of life all depend on a stable climate and earth system, and a diversity of species and ecosystems. Climate change significantly impacts human trafficking, modern slavery, and global hunger. This book examines these global problems using techniques from mathematics of uncertainty. Since accurate data concerning human trafficking and modern slavery is impossible to obtain, mathematics of uncertainty is an ideal discipline to study these problems. The book also considers the interconnection between climate change, world hunger, human trafficking, modern slavery, and the coronavirus. Connectivity properties of fuzzy graphs are used to examine trafficking flow between regions in the world. The book is an excellent reference source for advanced undergraduate and graduate students in mathematics and the social sciences as well as for researchers and teachers.

This book uses mathematics of uncertainty to examine how well countries are achieving the 17 Sustainable Development Goals (SDGs) set by the members of the United Nations, with a focus on climate change, human trafficking and modern slavery. Although this approach has never been used before, mathematics of uncertainty is well suited to exploring these topics due to the lack of accurate data available. The authors place several scientific studies in a mathematical setting to pave the way for future research on issues of sustainability, climate change, human trafficking and modern slavery to using a wide range of mathematical techniques. Moreover, the book ranks countries in terms of their achievement of not only the SDGs, but in particular those SDGs pertinent to climate change, human trafficking, and modern slavery, and highlights the deficiencies in the foster care system that lead to human trafficking. As such it is an excellent reference resource for advanced undergraduate and graduate students in mathematics and the social sciences, as well as for researchers and teachers.