Ali Ahmadian – författare

This book describes different mathematical modeling and soft computing techniques used to solve practical engineering problems. It gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques. Through examples and case studies, the editors demonstrate and describe how problems with inherent uncertainty can be addressed and eventually solved through the aid of numerical models and methods. The chapters address several applications and examples in bioengineering science, drug delivery, solving inventory issues, Industry 4.0, augmented reality and weather forecasting. Other examples include solving fuzzy-shortest-path problems by introducing a new distance and ranking functions. Because, in practice, problems arise with uncertain data and most of them cannot be solved exactly and easily, the main objective is to develop models that deliver solutions with the aid of numerical methods. This is the reason behind investigating soft numerical computing in dynamic systems. Having this in mind, the authors and editors have considered error of approximation and have discussed several common types of errors and their propagations. Moreover, they have explained the numerical methods, along with convergence and consistence properties and characteristics, as the main objectives behind this book involve considering, discussing and proving related theorems within the setting of soft computing. This book examines dynamic models, and how time is fundamental to the structure of the model and data as well as the understanding of how a process unfolds • Discusses mathematical modeling with soft computing and the implementations of uncertain mathematical models • Examines how uncertain dynamic systems models include uncertain state, uncertain state space and uncertain state’s transition functions • Assists readers to become familiar with many soft numerical methods to simulate the solution function’s behaviorThis book is intended for system specialists who are interested in dynamic systems that operate at different time scales. The book can be used by engineering students, researchers and professionals in control and finite element fields as well as all engineering, applied mathematics, economics and computer science interested in dynamic and uncertain systems.Ali Ahmadian is a Senior Lecturer at the Institute of IR 4.0, The National University of Malaysia.Soheil Salahshour is an associate professor at Bahcesehir University.

This book describes different mathematical modeling and soft computing techniques used to solve practical engineering problems. It gives an overview of the current state of soft computing techniques and describes the advantages and disadvantages of soft computing compared to traditional hard computing techniques. Through examples and case studies, the editors demonstrate and describe how problems with inherent uncertainty can be addressed and eventually solved through the aid of numerical models and methods. The chapters address several applications and examples in bioengineering science, drug delivery, solving inventory issues, Industry 4.0, augmented reality and weather forecasting. Other examples include solving fuzzy-shortest-path problems by introducing a new distance and ranking functions. Because, in practice, problems arise with uncertain data and most of them cannot be solved exactly and easily, the main objective is to develop models that deliver solutions with the aid of numerical methods. This is the reason behind investigating soft numerical computing in dynamic systems. Having this in mind, the authors and editors have considered error of approximation and have discussed several common types of errors and their propagations. Moreover, they have explained the numerical methods, along with convergence and consistence properties and characteristics, as the main objectives behind this book involve considering, discussing and proving related theorems within the setting of soft computing. This book examines dynamic models, and how time is fundamental to the structure of the model and data as well as the understanding of how a process unfolds • Discusses mathematical modeling with soft computing and the implementations of uncertain mathematical models • Examines how uncertain dynamic systems models include uncertain state, uncertain state space and uncertain state’s transition functions • Assists readers to become familiar with many soft numerical methods to simulate the solution function’s behaviorThis book is intended for system specialists who are interested in dynamic systems that operate at different time scales. The book can be used by engineering students, researchers and professionals in control and finite element fields as well as all engineering, applied mathematics, economics and computer science interested in dynamic and uncertain systems.Ali Ahmadian is a Senior Lecturer at the Institute of IR 4.0, The National University of Malaysia.Soheil Salahshour is an associate professor at Bahcesehir University.

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

Prediction of the Future Pandemic: Artificial Intelligence and Nanotechnology Approaches provides a comprehensive account of the applications, challenges, and breakthroughs in using AI and nanotechnology for pandemic prediction. The book emphasizes interdisciplinary collaboration among experts and a global perspective on the prediction of future pandemics. Sections provide a comprehensive exploration of cutting-edge methodologies that leverage AI and nanotechnology to predict, analyze, and combat future pandemics. This book aims to bridge the gap between theoretical concepts and practical applications, offering insights into the integration of these advanced technologies for more accurate, timely, and ethical pandemic predictions.Offers an in-depth examination of the integration of AI and nanotechnology in pandemic predictionCovers many nanotechnology applications in pandemic prediction, such as nanorobotics for targeted intervention, the role of nanomaterials in prevention strategies, viral detection/nanosensors in quarantine enforcement, and nanocomposite resilience (building pandemic-proof materials)Helps readers get to grips with the fast-evolving landscape of AI, nanotechnology, and global healthCovers both the theory and real-world applications of AI and nanotechnology in predicting pandemicsDiscusses the fast-changing connection between AI, nanotechnology, and pandemic prediction

Consensus: Fueling Blockchain Innovation and DApp Expansion explores the complexities of consensus mechanisms in order to shed light on emerging trends, best practices, and real-world applications that can fuel blockchain innovation while encouraging the dissemination of DApps across various industries. Additionally, the book bridges a crucial gap in the literature by providing in-depth insights into the role of consensus mechanisms in shaping the future of blockchain technology and decentralized applications. This book delves into the fundamentals of blockchain technology along with the roles and significance of vital consensus mechanisms, their underlying principles, formal specifications, functional characteristics, architecture, frameworks, and potential across diverse blockchain applications. Moreover, the book meticulously explores classification, performance metrics, and design parameters. It offers a comprehensive comparative analysis of these mechanisms, shedding light on their computational and communication complexity, strengths, weaknesses, and suitability. Additionally, the book delves into future research directions, highlighting emerging trends and areas requiring further investigation. It also addresses the efforts underway to address existing challenges and open issues within the realm of consensus mechanisms, ensuring a comprehensive understanding of the state-of-the-art in this pivotal aspect of blockchain technology. Due to the wide range of availability and evolving new consensus mechanisms, selecting an optimal and suitable consensus for a specific blockchain application is one of the crucial challenges in the development and innovation of blockchain systems. This book has also a discussion on appropriate selection algorithms based on multi-attribute decision-making for specific blockchain systems and DApps development.Provides readers with an in-depth understanding of the fundamental concepts of blockchain technology and its numerous essential consensus mechanismsProvides insights into emerging trends and future developments in consensus mechanisms and their potential implications for blockchain innovation and DApp expansionOffers a comprehensive examination of consensus mechanisms, highlighting their pivotal role in driving blockchain innovation and expanding decentralized applications (DApps)Presents practical insights into real-world implementations, alongside comparative analyses of different consensus models, empowering readers with decision-making tools to select the most suitable mechanism for their blockchain systems and applicationsIncludes discussions on multi-attribute decision-making for consensus selection, addressing a critical challenge faced by developers

Learning-Driven Game Theory for AI: Concepts, Models, and Applications offers in-depth coverage of recent methodological and conceptual advancements in various disciplines of Dynamic Games, namely differential and discrete-time dynamic games, evolutionary games, repeated and stochastic games, and their applications in a variety of fields, such as computer science, biology, economics, and management science. In this book, the authors bridge the gap between traditional game theory and its modern applications in artificial intelligence (AI) and related technological fields. The dynamic nature of contemporary problems in robotics, cybersecurity, machine learning, and multi-agent systems requires game-theoretic solutions that go beyond classical methods. The book delves into the rapidly growing intersection of pursuit differential games and AI, focusing on how these advanced game-theoretic models can be applied to modern AI systems, making it an indispensable resource for both academics and professionals. The book also provides a variety of applications demonstrating the practical integration of AI and game theory across various disciplines, such as autonomous systems, federated learning, and distributed decision-making frameworks. The book also explores the use of game theory in reinforcement learning, swarm intelligence, multi-agent coordination, and cybersecurity. These are critical areas where AI and dynamic games converge. Each chapter covers a different facet of dynamic games, offering readers a comprehensive yet focused exploration of topics such as differential and discrete-time games, evolutionary dynamics, and repeated and stochastic games. The absence of static games ensures a concentrated focus on the dynamic, evolving problems that are most relevant today.Offers comprehensive coverage of advanced games while focusing on cutting-edge AI applicationsIncludes case studies that illustrate the application of game theory in AI-driven fields like reinforcement learning, swarm intelligence, and cybersecurityProvides readers with a practical focus, combined with the inclusion of emerging methodologies like learning-based approaches to pursuit-evasion gamesEquips readers with tools and frameworks to tackle the complex, dynamic challenges in their fields

Artificial Intelligence in Clean Energy Systems: From Energy Hubs to Smart Grids investigates the integration of AI and machine learning into future clean and modern energy systems, focusing on applying such technologies to optimal operation of energy hubs, smart grids, and efficient utilization of renewable energy sources. The book provides an in-depth overview of AI-based forecasting techniques, optimization, automation, and control approaches that enhance energy efficiency and sustainability in modern energy systems. It presents significant issues such as AI-based load demand forecasting, smart grid automation, integration of renewable energy, cybersecurity, and electric vehicle charging demand schedulingCovers AI-driven forecasting, optimization and automation techniques specifically tailored for energy hubs and smart gridIncludes industry case studies and real-world implementations of AI in clean energy systems, helping readers bridge theory with practiceExplores the latest advancements in AI, machine learning, and deep learning for energy management, making it a valuable resource for researchers and professionalsProvides AI-based solutions to key energy challenges such as demand forecasting, grid stability, renewable energy integration, and cybersecurity

Reimagine the future of healthcare with a deep dive into hyperscale computing and distributed networks In AI-Driven Smart Healthcare: Powered by Hyperscale Computing and Next Generation Networks, a team of distinguished researchers delivers an insightful and practical discussion of the healthcare applications of artificial intelligence and fog-enabled next-generation networks. The book provides practical insights and methodologies for the design, development, and deployment of these technologies throughout the healthcare industry. Readers will explore key areas of recent advancement, including the Internet of Things, fog computing, artificial intelligence, machine learning, serverless computing, and blockchain in a way that allows them to assess the feasibility and scalability of a variety of technological healthcare solutions. The book also includes: A thorough introduction to the integration of AI and fog computing into smart healthcare systemsComprehensive explorations of how these technologies enhance healthcare delivery, with examples like remote patient monitoring and advanced diagnostic modelsPractical discussions of the advantages, challenges, and potential solutions associated with AI and fog computingAn interdisciplinary focus for professionals working at the intersection of AI, machine learning, fog computing, and healthcarePerfect for researchers, practitioners, and other healthcare stakeholders, AI-Driven Smart Healthcare will also benefit technologists, educators, hospital administrators, and other professionals with an interest in the application of the latest technologies to recurrent and significant issues in the field of healthcare.

This book discusses the technical, economic, and environmental aspects of electric vehicles and their impact on electrical grids and energy systems. The book is divided into three parts that include load modeling, integration and optimization, and environmental evaluation. Theoretical background and practical examples accompany each section and the authors include helpful tips and hints in the load modeling and optimization sections. This book is intended to be a useful tool for undergraduate and graduate students, researchers and engineers who are trying to solve power and engineering problems related electric vehicles.Provides optimization techniques and their applications for energy systems;Discusses the economic and environmental perspectives of electric vehicles;Contains the most comprehensive information about electric vehicles in a single source.

This book discusses the technical, economic, and environmental aspects of electric vehicles and their impact on electrical grids and energy systems. The book is divided into three parts that include load modeling, integration and optimization, and environmental evaluation. Theoretical background and practical examples accompany each section and the authors include helpful tips and hints in the load modeling and optimization sections. This book is intended to be a useful tool for undergraduate and graduate students, researchers and engineers who are trying to solve power and engineering problems related electric vehicles.Provides optimization techniques and their applications for energy systems;Discusses the economic and environmental perspectives of electric vehicles;Contains the most comprehensive information about electric vehicles in a single source.

This book brings together cross-disciplinary research on carbon capture, utilization, and storage (CCUS) to examine the impact of implementing CCUS tools and technologies on emissions reduction and sustainable development in cities and large metropolitan areas. An expert group of global contributors provides in-depth technical discussions, case studies, and examples with an emphasis on the worldwide application of the latest developments in technology, protocols, implementation, and application of CCUS in power and energy systems.Carbon Capture, Utilization, and Storage Technologies: Towards More Sustainable Cities is an essential multidisciplinary reference for researchers and industry practitioners from engineering, energy, computer science, data science, economics, and operational research working in the energy and environmental fields.

This book brings together cross-disciplinary research on carbon capture, utilization, and storage (CCUS) to examine the impact of implementing CCUS tools and technologies on emissions reduction and sustainable development in cities and large metropolitan areas. An expert group of global contributors provides in-depth technical discussions, case studies, and examples with an emphasis on the worldwide application of the latest developments in technology, protocols, implementation, and application of CCUS in power and energy systems.Carbon Capture, Utilization, and Storage Technologies: Towards More Sustainable Cities is an essential multidisciplinary reference for researchers and industry practitioners from engineering, energy, computer science, data science, economics, and operational research working in the energy and environmental fields.

This book offers an extensive examination of the operational and strategic considerations for sustainable energy within the context of smart cities, incorporating cutting-edge advancements in technology, protocols, implementation strategies, and the utilization of energy resources. It examines recent sustainability strategies formulated by various nations emphasizing worldwide application, accompanied by a thorough analysis of economic, technical, and environmental dimensions. The authors present a comprehensive exploration of energy provisioning and administration, underscoring the deployment of data analysis methodologies and tools, such as machine learning and deep learning-based approaches for practical energy management studies. Additionally, it showcases the application of mathematical programming in modeling real-world case studies, leveraging optimization techniques that have broad-reaching global relevance.Presents the latest developments in the application of sustainable energy resources in smart cities;Showcases machine learning and deep learning-based approaches for practical energy management;Includes international case studies.

This book bridges the gap between conventional materials science and emerging data-driven methodologies, highlighting the integration of AI, machine learning, and deep learning technologies to enhance the design, analysis, and optimization of smart materials. It provides a holistic perspective essential for researchers, engineers, and students exploring the intersection of materials engineering and AI technologies.The book examines the connection between recent advancements in materials science and multiscale machine learning, facilitating predictive and prescriptive modeling for assessing material behavior based on composition, structure, and processing. It includes comprehensive discussions on smart material design, optimization, complexity analysis, and advanced computational methods for synthesizing and characterizing materials. Challenges in multiscale modeling, such as biologically inspired material design and the influence of nanotechnology on current trends, are thoroughly explored.Emphasizing the critical role of multiscale machine learning and nanotechnology in creating sustainable smart materials, the book also addresses the ethical implications of this research. It discusses opportunities and challenges in biomaterials, particularly in healthcare and biomedical applications, and anticipates future trends in machine learning for sustainable materials design. The book provides insights into how predictive and prescriptive modeling through machine learning can accelerate the material discovery process, guiding researchers toward promising candidates for further exploration.Serving as a roadmap for researchers and scientists, this book offers valuable insights into innovative approaches that support the future of materials science.