Rahul Kar – 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.

This book explores and highlights the latest techniques and applications of artificial intelligence (AI) and fuzzy logic in the development of intelligent transportation systems (ITS). It offers a detailed examination of how to leverage these technologies to solve complex transportation challenges, such as traffic congestion, route optimization, and autonomous vehicle navigation. Artificial Intelligence and Fuzzy Logic for Next Generation Intelligent Transportation employs several novel approaches and special design features. It includes case studies and real-world examples illustrating the practical applications of the technologies under review. Furthermore, it integrates interdisciplinary perspectives, combining insights from computer science, engineering, urban planning, and environmental science. Each chapter is self-contained, providing both theoretical background and practical insights, making it accessible to a broad audience. Additionally, this book features contributions from leading experts in the field, ensuring that the content is both current and relevant.Presents advanced methodologies in AI and fuzzy logic, specifically designed to enhance ITSsOffers a detailed examination of how technologies can be leveraged to solve complex transportation challenges, such as traffic congestion, route optimization, and autonomous vehicle navigationBridges the gap between theoretical research and practical implementation, demonstrating how emerging technologies can be applied to real-world problemsIncludes theoretical foundations, algorithmic developments, and practical case studies specifically related to AI in the context of smart transportationDiscusses topics such as predictive weather impact on traffic management, enhancing road safety with AI and fuzzy logic, and smart parking systems in urban areasIt is written for researchers, engineers, and practitioners in the field, as well as policymakers and academic scholars interested in the future of transportation.

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.

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.

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.

Fuzzy Logic Approaches to Sustainable Virtual Worlds explores how computational intelligence can shape digital societies through sustainability-driven design. By merging fuzzy logic models with ecological and ethical frameworks, the book presents strategies for resource management, governance, and resilience in virtual environments. Blending theory with applications, it serves as a reference for researchers, professionals, and policymakers aiming to align virtual ecosystems with real-world sustainability goals.Innovative Focus: First book dedicated to applying fuzzy logic in sustaining virtual worlds.Interdisciplinary Appeal: Connects AI, computer science, sustainability studies, digital environments, and social sciences.Applications: Demonstrates fuzzy logic’s role in decision-making, uncertainty management, environmental modelling, energy efficiency, and governance.Global Relevance: Aligns with UN SDGs, making it valuable for educators, policymakers, and technologists.Case Studies: Real-world implementations and simulations of green ICT, resource-efficient infrastructures, and sustainable digital societies.Audience:Researchers in AI, fuzzy systems, and sustainabilityProfessionals in VR, metaverse, gaming, and eco-digital designPolicymakers and educators in sustainable technologyGraduate students exploring emerging tech + sustainabilityPublisher Value Add: Aligns with the publishing trend of AI for sustainability, boosting visibility and citation potential.Competitive Edge: Niche yet timely—bridging fuzzy logic, sustainability, and virtual world creation.Teaching Potential: Adaptable for workshops, MOOCs, and conference tracks on sustainable computing and fuzzy systems