Mohamed Abdel-Basset – författare

Optimization Theory Based on Neutrosophic and Plithogenic Sets presents the state-of-the-art research on neutrosophic and plithogenic theories and their applications in various optimization fields. Its table of contents covers new concepts, methods, algorithms, modelling, and applications of green supply chain, inventory control problems, assignment problems, transportation problem, nonlinear problems and new information related to optimization for the topic from the theoretical and applied viewpoints in neutrosophic sets and logic.

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 discusses how the role of recent technologies applied to health settings can help fight virus outbreaks. Moreover, it provides guidelines on how governments and institutions should prepare and quickly respond to drastic situations using technology to support their communities in order to maintain life and functional as efficiently as possible. The book discusses topics such as AI-driven histopathology analysis for COVID-19 diagnosis, bioinformatics for subtype rational drug design, deep learning-based treatment evaluation and outcome prediction, sensor informatics for monitoring infected patients, and machine learning for tracking and prediction models.

In addition, the book presents AI solutions for hospital management during an epidemic or pandemic, along with real-world solutions and case studies of successful measures to support different types of communities. This is a valuable source for medical informaticians, bioinformaticians, clinicians and other healthcare workers and researchers who are interested in learning more on how recently developed technologies can help us fight and minimize the effects of global pandemics.

Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing metaheuristics techniques with machine learning for solving biomedical problems. This book is organized to present a stepwise progression beginning with the basics of metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of this book presents the fundamental concepts of metaheuristics and machine learning and provides a comprehensive taxonomic view of metaheuristics methods according to a variety of criteria such as data type, scope, and method. The second section of this book explains how to apply metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in metaheuristics for biomedical science. This book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice.

Multi-Criteria Decision-Making for Renewable Energy: Methods, Applications, and Challenges brings together the latest fuzzy and soft computing methods, models, and algorithms as applied to the field of renewable energy and supported by specific application examples and case studies. The book begins by approaching renewable energy sources, challenges and factors that affect their development, as well as green renewable energy sites and the utilization of fuzzy multi-criteria decision-making (MCDM) techniques in these broad contexts, as well as utilization in addressing the various environmental, economic, and social barriers to ensuring the sustainability of energy resources.

Detailed chapters focus on the application of multi-criteria decision-making methods for planning, modeling and prioritization in specific areas of renewable energy, including solar energy, wind farms, solar-powered hydrogen production plants, biofuel production, energy storage, hydropower, and marine energy. Finally, future opportunities and research directions are explored.

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks.

This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master’s students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications.

This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks.

This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master’s students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications.

This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.

Application of Advanced Optimization Techniques for Healthcare Analytics, 1st Edition, is an excellent compilation of current and advanced optimization techniques which can readily be applied to solve different hospital management problems. The healthcare system is currently a topic of significant investigation to make life easier for those who are disabled, old, or sick, as well as for young children. The emphasis of the healthcare system has evolved throughout time due to several emerging beneficial technologies, such as personal digital assistants (PDAs), data mining, the internet of things, metaheuristics, fog computing, and cloud computing.

Metaheuristics are strong technology for tackling several optimization problems in various fields, especially healthcare systems. The primary advantage of metaheuristic algorithms is their ability to find a better solution to a healthcare problem and their ability to consume as little time as possible. In addition, metaheuristics are more flexible compared to several other optimization techniques. These algorithms are not related to a specific optimization problem but could be applied to any optimization problem by making some small adaptations to become suitable to tackle it.

The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable optimization approach when making decisions to schedule patients under crowding environments with minimized human errors.

Application of Advanced Optimization Techniques for Healthcare Analytics, 1st Edition, is an excellent compilation of current and advanced optimization techniques which can readily be applied to solve different hospital management problems. The healthcare system is currently a topic of significant investigation to make life easier for those who are disabled, old, or sick, as well as for young children. The emphasis of the healthcare system has evolved throughout time due to several emerging beneficial technologies, such as personal digital assistants (PDAs), data mining, the internet of things, metaheuristics, fog computing, and cloud computing.

Metaheuristics are strong technology for tackling several optimization problems in various fields, especially healthcare systems. The primary advantage of metaheuristic algorithms is their ability to find a better solution to a healthcare problem and their ability to consume as little time as possible. In addition, metaheuristics are more flexible compared to several other optimization techniques. These algorithms are not related to a specific optimization problem but could be applied to any optimization problem by making some small adaptations to become suitable to tackle it.

The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable optimization approach when making decisions to schedule patients under crowding environments with minimized human errors.

This book applies both industrial engineering and computational intelligence to demonstrate intelligent machines that solve real-world problems in various smart environments. It presents fundamental concepts and the latest advances in multi-criteria decision-making (MCDM) techniques and their application to smart environments. Though managers and engineers often use multi-criteria analysis in making complex decisions, many core problems are too difficult to model mathematically or have simply not yet been modeled.

In response, as well as discussing AI-based approaches, Soft Computing for Smart Environments covers various optimization techniques, decision analytics, and data science in applying soft computing techniques to a defined set of smart environments, including smart and sustainable cities, disaster response systems, and smart campuses.

This state-of-the-art book will be essential reading for both undergraduate and graduate students, researchers, practitioners, and decision-makers interested in advanced MCDM techniques for management and engineering in relation to smart environments.

This book applies both industrial engineering and computational intelligence to demonstrate intelligent machines that solve real-world problems in various smart environments. It presents fundamental concepts and the latest advances in multi-criteria decision-making (MCDM) techniques and their application to smart environments. Though managers and engineers often use multi-criteria analysis in making complex decisions, many core problems are too difficult to model mathematically or have simply not yet been modeled.

In response, as well as discussing AI-based approaches, Soft Computing for Smart Environments covers various optimization techniques, decision analytics, and data science in applying soft computing techniques to a defined set of smart environments, including smart and sustainable cities, disaster response systems, and smart campuses.

This state-of-the-art book will be essential reading for both undergraduate and graduate students, researchers, practitioners, and decision-makers interested in advanced MCDM techniques for management and engineering in relation to smart environments.

Multi-Criteria Decision Making Theory and Applications in Sustainable Healthcare, 1st Edition, is an excellent compilation of current and advanced Multi-Criteria Decision Making (MCDM) techniques and their applications to multiple recent and innovative healthcare analytics problems. The healthcare business has expanded rapidly in recent years, and one of the top priorities in the sector is now the efficacy and efficiency of the various healthcare delivery systems. The entire performance of hospitals must be improved if the healthcare business wants to see an improvement in both the satisfaction and safety of their patients. Finding the best medical facility among many of its competitors may be difficult since there are so many, and they are so highly diverse in terms of features and performance trade-offs.

This book has brought together the introductory discussions, fundamental concepts, challenges, and insights of multiple advanced healthcare management problems along with the application of MCDM to obtain the best option among multiple alternatives. A few important takeaways from this book are:

The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable MCDM technique when making decisions to prioritize the selection criteria of any healthcare-related problems to ensure a sustainable practice.

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Multi-Criteria Decision Making Theory and Applications in Sustainable Healthcare, 1st Edition, is an excellent compilation of current and advanced Multi-Criteria Decision Making (MCDM) techniques and their applications to multiple recent and innovative healthcare analytics problems. The healthcare business has expanded rapidly in recent years, and one of the top priorities in the sector is now the efficacy and efficiency of the various healthcare delivery systems. The entire performance of hospitals must be improved if the healthcare business wants to see an improvement in both the satisfaction and safety of their patients. Finding the best medical facility among many of its competitors may be difficult since there are so many, and they are so highly diverse in terms of features and performance trade-offs.

This book has brought together the introductory discussions, fundamental concepts, challenges, and insights of multiple advanced healthcare management problems along with the application of MCDM to obtain the best option among multiple alternatives. A few important takeaways from this book are:

The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable MCDM technique when making decisions to prioritize the selection criteria of any healthcare-related problems to ensure a sustainable practice.

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More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications.

Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details.

Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.

More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications.

Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details.

Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.

This book provides a broad overview of the areas of artificial intelligence (AI) that can be used for smart farming applications, through either successful engineering or ground-breaking research. Among them, the highlighted tactics are soil management, water management, crop management, livestock management, harvesting, and the integration of Internet of Things (IoT) in smart farming.

Artificial Intelligence and Internet of Things in Smart Farming explores different types of smart framing systems for achieving sustainability goals in the real environment. The authors discuss the benefits of smart harvesting systems over traditional harvesting methods, including decreased labor requirements, increased crop yields, increased probabilities of successful harvests, enhanced visibility into crop health, and lower overall harvest and production costs. It explains and describes big data in terms of its potential five dimensions—volume, velocity, variety, veracity, and valuation—within the framework of smart farming. The authors also discuss the recent IoT technologies, such as fifth-generation networks, blockchain, and digital twining, to improve the sustainability and productivity of smart farming systems. The book identifies numerous issues that call for conceptual innovation and has the potential to progress machine learning (ML), resulting in significant impacts. As an illustration, the authors point out how smart farming offers an intriguing field for interpretable ML. The book then delves into the function of AI techniques, such as AI in accelerating the development of nano-enabled agriculture, thereby facilitating safe-by-design nanomaterials for various consumer products and medical applications.

This book is for undergraduate students, graduate students, researchers, and AI engineers who pursue a strong understanding of the practical methods of machine learning in the agriculture domain. Practitioners and stakeholders would be able to follow this book to understand the potential of ML in their farming projects and agricultural solutions.

Features:• Explores different types of smart framing systems for achieving sustainability goals in the real environment • Explores ML-based analytics such as generative adversarial networks (GAN), autoencoders, computational imaging, and quantum computing • Examines the development of intelligent machines to provide solutions to real-world problems, emphasizing smart farming applications, which are not modeled or are extremely difficult to model mathematically • Emphasizes methods for better managing crops, soils, water, and livestock, urging investors and businesspeople to occupy the existing vacant market area • Discusses AI-empowered Nanotechnology for smart farming

This book provides a broad overview of the areas of artificial intelligence (AI) that can be used for smart farming applications, through either successful engineering or ground-breaking research. Among them, the highlighted tactics are soil management, water management, crop management, livestock management, harvesting, and the integration of Internet of Things (IoT) in smart farming.

Artificial Intelligence and Internet of Things in Smart Farming explores different types of smart framing systems for achieving sustainability goals in the real environment. The authors discuss the benefits of smart harvesting systems over traditional harvesting methods, including decreased labor requirements, increased crop yields, increased probabilities of successful harvests, enhanced visibility into crop health, and lower overall harvest and production costs. It explains and describes big data in terms of its potential five dimensions—volume, velocity, variety, veracity, and valuation—within the framework of smart farming. The authors also discuss the recent IoT technologies, such as fifth-generation networks, blockchain, and digital twining, to improve the sustainability and productivity of smart farming systems. The book identifies numerous issues that call for conceptual innovation and has the potential to progress machine learning (ML), resulting in significant impacts. As an illustration, the authors point out how smart farming offers an intriguing field for interpretable ML. The book then delves into the function of AI techniques, such as AI in accelerating the development of nano-enabled agriculture, thereby facilitating safe-by-design nanomaterials for various consumer products and medical applications.

This book is for undergraduate students, graduate students, researchers, and AI engineers who pursue a strong understanding of the practical methods of machine learning in the agriculture domain. Practitioners and stakeholders would be able to follow this book to understand the potential of ML in their farming projects and agricultural solutions.

Features:• Explores different types of smart framing systems for achieving sustainability goals in the real environment • Explores ML-based analytics such as generative adversarial networks (GAN), autoencoders, computational imaging, and quantum computing • Examines the development of intelligent machines to provide solutions to real-world problems, emphasizing smart farming applications, which are not modeled or are extremely difficult to model mathematically • Emphasizes methods for better managing crops, soils, water, and livestock, urging investors and businesspeople to occupy the existing vacant market area • Discusses AI-empowered Nanotechnology for smart farming