Biswadip Basu Mallik – författare

Advances on Mathematical Modeling and Optimization with Its Applications discusses optimization, equality, and inequality constraints and their application in the versatile optimizing domain. It further covers non-linear optimization methods such as global optimization, and gradient-based non-linear optimization, and their applications.Discusses important topics including multi-component differential equations, geometric partial differential equations, and computational neural systemsCovers linear integer programming and network design problems, along with an application of the mixed integer problemsDiscusses constrained and unconstrained optimization, equality, and inequality constraints, and their application in the versatile optimizing domainElucidates the application of statistical models, probability models, and transfer learning conceptsShowcases the importance of multi-attribute decision modeling in the domain of image processing and soft computingThe text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science.

Advances on Mathematical Modeling and Optimization with Its Applications discusses optimization, equality, and inequality constraints and their application in the versatile optimizing domain. It further covers non-linear optimization methods such as global optimization, and gradient-based non-linear optimization, and their applications.Discusses important topics including multi-component differential equations, geometric partial differential equations, and computational neural systemsCovers linear integer programming and network design problems, along with an application of the mixed integer problemsDiscusses constrained and unconstrained optimization, equality, and inequality constraints, and their application in the versatile optimizing domainElucidates the application of statistical models, probability models, and transfer learning conceptsShowcases the importance of multi-attribute decision modeling in the domain of image processing and soft computingThe text is primarily for senior undergraduate and graduate students, and academic researchers in the fields of mathematics, statistics, and computer science.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:An overview of applications and computing devicesDeep learning impacts in the field of AIDeep learning as state-of-the-art approach to AIExploring deep learning architecture for cutting-edge AI solutionsOperations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines:An overview of applications and computing devicesDeep learning impacts in the field of AIDeep learning as state-of-the-art approach to AIExploring deep learning architecture for cutting-edge AI solutionsOperations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.

The model-based approach for carrying out the classification and identification of tasks has led to progression of the machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomenon. Such fields as object classification, speech recognition, and face detection have sought extensive applications of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also become increasingly common in the domains of agriculture, health sectors, and insurance.Operations research is the branch of mathematics used to perform many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects to aid in decision making. Arriving at the proper decision depends on a number of factors; this book examines how AI and ML can be used to model equations and define constraints to solve problems more easily and discover proper and valid solutions. This book also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies examine how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI-enabled technologies.

Implementing the circular economy necessitates innovative approaches capable of managing the complexity and uncertainty inherent in the shift from linear to circular systems. Fuzzy logic, a computational approach inspired by human reasoning, provides a promising framework for navigating these complexities. With a practical approach, this book offers how fuzzy logic can be utilized to implement the circular economy.Engineering the Circular Economy: Leveraging Fuzzy Logic for Implementation discusses the significance of incorporating fuzzy logic into circular economy strategies. This integration enables the development of adaptive models for resource management, waste reduction, and supply chain optimization. The book showcases various case studies and practical examples to demonstrate the application of fuzzy logic in implementing circular economy principles across diverse industries. Additionally, it addresses the challenges and future directions within a circular economy framework, emphasizing the importance of identifying these challenges and exploring future directions for continuous improvement in sustainability efforts. The potential of combining engineering, fuzzy logic, and the circular economy to address global sustainability challenges is highlighted, showcasing how leveraging fuzzy logic facilitates informed decision-making and the creation of innovative solutions as we work toward a more sustainable future.With its forward-thinking approach and focus on practical applications, this book is an essential resource for practitioners, researchers, engineers, scholars, and policymakers looking to promote innovation and sustainability in today’s rapidly changing economic and environmentally conscious environment.

MATHEMATICS AND COMPUTER SCIENCE This first volume in a new multi-volume set gives readers the basic concepts and applications for diverse ideas and innovations in the field of computing together with its growing interactions with mathematics. This new edited volume from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. The chapters focus on recent advances in computer science, and mathematics, and where the two intersect to create value for end users through practical applications of the theory. The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library.

MATHEMATICS AND COMPUTER SCIENCE This second volume in a new multi-volume set builds on the basic concepts and fundamentals laid out in the previous volume, presenting the reader with more advanced and cutting-edge topics being developed in this exciting field. This second volume in a new series from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. Building on what was presented in volume one, the chapters focus on more advanced topics in computer science, mathematics, and where the two intersect to create value for end users through practical applications. The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library.

FUZZY LOGIC APPLICATIONS IN COMPUTER SCIENCE AND MATHEMATICSTICS The prime objective of developing this book is to provide meticulous details about the basic and advanced concepts of fuzzy logic and its all-around applications to different fields of mathematics and engineering. The basic steps of fuzzy inference systems starting from the core foundation of the fuzzy concepts are presented in this book. The fuzzy theory is a mathematical concept and, at the same time, it is applied to many versatile engineering fields and research domains related to computer science. The fuzzy system offers some knowledge about uncertainty and is also related to the theory of probability. A fuzzy logic-based model acts as the classifier for many different types of data belonging to several classes. Covered in this book are topics such as the fundamental concepts of mathematics, fuzzy logic concepts, probability and possibility theories, and evolutionary computing to some extent. The combined fields of neural network and fuzzy domain (known as the neuro-fuzzy system) are explained and elaborated. Each chapter has been produced in a very lucid manner, with grading from simple to complex to accommodate the anticipated different audiences. The application-oriented approach is the unique feature of this book. Audience This book will be read and used by a broad audience including applied mathematicians, computer scientists, and industry engineers.

Mathematical Modeling for Computer ApplicationsThe mathematical sciences are part of nearly all aspects of everyday life. The discipline has underpinned such beneficial modern capabilities as internet searches, medical imaging, computer animation, numerical weather predictions, and all types of digital communications. This outstanding new volume in the series, "Mathematics and Computer Science," examines the current state of mathematical science and explores the changes needed for the discipline to be in a strong position and able to maximize its contributions.Besides covering important practical applications for the areas where mathematics and computer science intersect, the editors of this new volume recommend that training for future generations of mathematical scientists should be re-assessed in light of the increasingly cross-disciplinary nature of the mathematical sciences. In addition, because of the valuable interplay between ideas and people from all parts of the mathematical sciences, this group of curated papers emphasizes that universities and governments need to continue to invest in the full spectrum of the mathematical sciences in order for the whole enterprise to continue to flourish long-term. Emphasis on important developments in applied mathematics and modeling, analysis and its applications, applied algebra and its applications, geometry and its applications, algebraic statistics and its applications as well as algebraic topology and its applications are covered. Whether for the veteran engineer, new hire, or student, this is a must-have volume for any library.

Mathematics and Computer Science for Real-World Applications gives invaluable insights into how mathematical and computer sciences drive essential modern innovations that enhance everyday life, making it a must-read for anyone interested in the intersection of mathematics and technology and their real-world applications. Mathematical sciences are part of nearly all aspects of everyday life. The discipline has underpinned beneficial modern capabilities, including internet searches, medical imaging, computer animation, numerical weather predictions, and digital communication. Mathematics and computer science are constantly evolving and contributing to most areas of science and engineering, therefore, future generations of mathematical scientists should reassess the increasingly cross-disciplinary nature of the mathematical sciences. Mathematics and Computer Science for Real-World Applications presents current scientific and technological innovations from leading academics, researchers, and experts across the globe in mathematical sciences and computing. The volume will discuss new technical ideas and features that can be incorporated into day-to-day life for the benefit of society. A diversified spectrum of scientific advancements is discussed, including applications of differential and integral equations, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning, and artificial intelligence. Readers will explore diverse ideas and innovations in the field of computing and its growing connections to various fields of mathematics.

This book is a vital resource for anyone looking to understand the essential role of graph theory as the unifying thread that connects and provides innovative solutions across a wide spectrum of modern computer science disciplines. Graph theory is a traditional mathematical discipline that has evolved as a basic tool for modeling and analyzing the complex relationships between different technological landscapes. Graph theory helps explain the semantic and syntactic relationships in natural language processing, a technology behind many businesses. Disciplinary and industry developments are seeing a major transition towards more interconnected and data-driven decision-making, and the application of graph theory will facilitate this transition. Disciplines such as parallel and distributive computing will gain insights into how graph theory can help with resource optimization and job scheduling, creating considerable change in the design and development of scalable systems. This book provides comprehensive coverage of how graph theory acts as the thread that connects different areas of computer science to create innovative solutions to modern technological problems. Using a multi-faceted approach, the book explores the fundamentals and role of graph theory in molding complex computational processes across a wide spectrum of computer science.

Explore the cutting edge of scientific computing with this volume, which provides a comprehensive look at the interdependency between mathematics and computer science. Within the evolving landscape of computer science, mathematics is increasingly playing a pivotal role. Disciplines like linear algebra, statistics, calculus, and discrete mathematics serve as the cornerstone for comprehension and innovation within various computer science domains. This book underscores the deep-seated interdependency between the realms of mathematics and scientific computing, exploring how each discipline mutually reinforces and advances the other. With its rich theoretical framework and analytical rigor, mathematics provides the bedrock upon which many computational concepts and methodologies are built. In turn, computer science offers a practical avenue for applying mathematical abstractions to tackle real-world problems efficiently and effectively. Cutting-edge technologies, such as scientific computing, deep learning, and computer vision, require not only a mastery of foundational mathematics, but a diverse interdisciplinary approach. This book sheds light on the burgeoning frontiers of computer science, bringing together researchers with expertise across multiple industries, making it an essential resource for beginners and experienced practitioners alike.

Fuzzy logic, a set of rules that can be used to reach logical conclusions from fuzzy sets of data, is a useful way of determining relevant relationships from imprecise data and has many uses in real-life applications. This new volume provides in-depth information and research on fuzzy logic and its many beneficial and versatile applications in diverse fields of engineering and other areas. It explores the varied applications of fuzzy logic in many sectors, such as in manufacturing and in the automotive industry, in the healthcare and allied health sciences, in mathematics and allied domains, in business and finance, in electronics and computer science, and more.

This new book looks at the fast-growing field of computational intelligence in a wide range of areas and offers new perspectives for using AI to enhance effectiveness as well as deal with real-life issues and problems. The diverse case examples highlight the integration of computational intelligence in drug discovery and the manufacturing of pharmaceuticals, in electronic science, in agricultural science, in manufacturing and industrial design, in finance and economics, in cybersecurity, in election integrity, in cancer prediction and analysis, for technical and vocational education, and more.

This new volume, Applications of Quantum Computing in a Variety of Domains, provides a comprehensive overview of the rapidly evolving field of quantum computing and its diverse applications across various domains. Quantum computing has emerged as one of the most promising frontiers in computer science, revolutionizing computation by leveraging the principles of quantum mechanics to perform complex calculations at speeds unattainable by classical computers. As this technology progresses, it has the potential to tackle some of the most challenging problems in science, engineering, finance, healthcare, and beyond.This volume has gathered insights from leading experts and researchers on the fundamental concepts of quantum computing and the latest advancements in quantum algorithms and hardware. It examines the practical applications of quantum computing across a range of industries and social media.The volume first lays the groundwork with an introduction to the principles of quantum mechanics and quantum computation, providing readers with a solid understanding of the theoretical underpinnings of quantum computing. From there, the book delves into the various quantum computing models, architectures, and algorithms, highlighting their unique advantages and challenges in the application of quantum computing in the many fields of computer security and cyber security, genetics, manufacturing and industrial design, chemistry, data science, machine learning, renewable energy, and weather forecasting and climate modeling.This volume, highlighting the diverse applications of quantum computing in a variety of domains, provides new perspectives for computing professionals as well as for faculty and students.

The Proceedings of the Fifth International Conference on Emerging Trends in Mathematical Sciences & Computing (IEMSC-24) contains papers that present the current scientific as well as technological innovations by leading academicians, eminent researchers, and experts throughout the globe in the twin domain of Mathematical Sciences as well as Computing. The papers focus on the recent advances in the field of Theoretical Computer Science as well as its blending with Mathematical techniques. The book aims to disseminate new technical ideas and features that can be incorporated in day-to-day life for the benefit of the society. The research papers exhibit scientific advancements in diversified spectrum that includes Differential as well as Integral Equations with applications, Computational Fluid Dynamics, Nanofluids, Network Theory & Optimization, Control Theory, Machine Learning & Artificial Intelligence, Big Data Analytics, IoT, Cryptography, Fuzzy Automata, Statistics, and many more. The proceedings primarily focus on the amalgamation of mathematical methods with computing. The potential readers will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. This book serves as a valuable reference resource for researchers in academia and industry.

The Proceedings of the Fifth International Conference on Emerging Trends in Mathematical Sciences & Computing (IEMSC-24) contains papers that present the current scientific as well as technological innovations by leading academicians, eminent researchers, and experts throughout the globe in the twin domain of Mathematical Sciences as well as Computing. The papers focus on the recent advances in the field of Theoretical Computer Science as well as its blending with Mathematical techniques. The book aims to disseminate new technical ideas and features that can be incorporated in day-to-day life for the benefit of the society. The research papers exhibit scientific advancements in diversified spectrum that includes Differential as well as Integral Equations with applications, Computational Fluid Dynamics, Nanofluids, Network Theory & Optimization, Control Theory, Machine Learning & Artificial Intelligence, Big Data Analytics, IoT, Cryptography, Fuzzy Automata, Statistics, and many more. The proceedings primarily focus on the amalgamation of mathematical methods with computing. The potential readers will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. This book serves as a valuable reference resource for researchers in academia and industry.

Such fuzzy logic variations can readily overcome the drawbacks of the simple fuzzy model.The book has been developed keeping in view about readers of different categories starting from the students to the professionals and researchers as well.

This book bridges the gap between theoretical modelling and everyday problems by showing the application of fuzzy logic to address those problems that are complex, uncertain and human-centric. Its innovative approach stresses real applications in various kinds of domains, promoting interdisciplinary understanding and innovation. In general, it deals with engineering, management, social systems and decision-making scenarios, establishing itself as an extremely useful tool for researchers, practitioners and graduate students looking for workable methodologies. It finds application for problem-solving in academic, industrial and policymaking circles, thus making it useful for study as well as for implementation.