Computational Intelligence Methods and Applications – serie

After an introduction to renewable energy technologies, the authors present computational intelligence techniques for optimizing the manufacture of related technologies, including solar concentrators. In particular the authors present new applications for their neural classifiers for image and pattern recognition.The book will be of interest to researchers in computational intelligence, in particular in the domain of neural networks, and engineers engaged with renewable energy technologies.

This book describes how to use computational intelligence and artificial intelligence tools to improve the decision-making process in new product development.

This book describes how to use computational intelligence and artificial intelligence tools to improve the decision-making process in new product development.

This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks.

This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks.

This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver.Important research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. The book concludes with a chapter on topics such as the development of a problem-agnostic CMSA and the relation between large neighborhood search and CMSA. Combinatorial optimization problems used in the book as test cases include the minimum dominating set problem, the variable-sized bin packing problem, and an electric vehicle routing problem.The book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, artificial intelligence, or statistics.

This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver.Important research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. The book concludes with a chapter on topics such as the development of a problem-agnostic CMSA and the relation between large neighborhood search and CMSA. Combinatorial optimization problems used in the book as test cases include the minimum dominating set problem, the variable-sized bin packing problem, and an electric vehicle routing problem.The book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, artificial intelligence, or statistics.

This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

Covering a wide range of notions concerning hesitant fuzzy set and its extensions, this book provides a comprehensive reference to the topic.

Covering a wide range of notions concerning hesitant fuzzy set and its extensions, this book provides a comprehensive reference to the topic.

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems.

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems.

In manufacturing contexts, Industry 4.0 and Industry 5.0 are forefront names for the concepts, technologies, practices, and skills underlying smart factories and industries of the future. Industry 5.0 extends the technological pillars of Industry 4.0 by putting more emphasis on human centricity, sustainability and resilience in the engineering (architecture, design and development), deployment (strategic/technology road mapping, business/digital transformation, and operationalization), and management (operational excellence and continual improvement) of complex cyber-physical production systems.This book contributes to bridging some key research gaps that hinder a full adoption of these trends in industry. Key topics focus on how to ensure applicability of human centricity, sustainability and resilience requirements in all three aspects related to system design, transformation, and operation, namely (1) human behaviors, skills, and competencies; (2) activities and business processes; and (3) system structure (including technology and information), behavior (including planning, monitoring, and control), and outcomes (including products and services). Case studies are provided in various chapters of the book to illustrate these key topics.

mso-bidi-font-family: Calibri;">Operations research and data science play a crucial role in implementing circular economy principles by bridging the gap between data generated from new technologies and decision-making processes.

The escalating tide of waste and environmental threats demands a paradigm shift in how we produce and consume waste. The circular economy emerges as a beacon of hope, offering a transformative approach that breaks free from the traditional "take make dispose" model. This innovative system prioritizes sustainable practices throughout a product's lifecycle, promoting reuse, recycling, and regeneration. By fostering the development of robust and closed loop supply chains, the circular economy ensures that resources are kept in use for as long as possible, thus minimizing waste and environmental impact.To achieve these ambitious goals, the circular economy leverages cutting edge technologies such as operations research and data science. These tools empower businesses by constructing decision support systems that effectively handle complex environmental and economic constraints. Operational research helps in optimizing resource allocation, production processes, and logistics, while data science provides insights through data analysis and predictive modeling.This book aims to detail the application of operations research and data science methods as decision support systems to manage complex constraints. This book presents the most recent advancements and real-world applications of these methods within the framework of supply chain management in the circular economy. Additionally, it explores case studies that highlight successful implementations and the benefits achieved. The book offers comprehensive insights and practical applications, serving as a valuable guide for students, researchers, and professionals looking to drive sustainability in their organizations and communities.

This book draws on a range of intelligent computing methodologies to effectively detect and classify various carcinogenic diseases. Chapters 3, 4 and 5 elaborate on several intelligent gene selection methodologies such as filter methodologies and wrapper methodologies.

This book provides a comprehensive review of the latest research in the area of explainable artificial intelligence (XAI) in health informatics.

Spiking neural P systems are neural-like membrane computing models, representing an interdisciplinary field between membrane computing and artificial neural networks, and are considered one of the third-generation neural networks.

Membrane computing is a class of distributed and parallel computing models inspired by living cells. Spiking neural P systems are neural-like membrane computing models, representing an interdisciplinary field between membrane computing and artificial neural networks, and are considered one of the third-generation neural networks. Models and applications constitute two major research topics in spiking neural P systems. The entire book comprises two parts: models and applications. In the model part, several variants of spiking neural P systems and fuzzy spiking neural P systems are introduced. Subsequently, their computational completeness is discussed, encompassing digital generation/accepting devices, function computing devices, and language generation devices. This discussion is advantageous for researchers in the fields of membrane computing, biologically inspired computing, and theoretical computer science, aiding in understanding the distributed computing model of spiking neural P systems. In the application part, the application of spiking neural P systems in time series prediction, image processing, sentiment analysis, and fault diagnosis is examined. This offers a novel method and model for researchers in artificial intelligence, data mining, image processing, natural language processing, and power systems. Simultaneously, it furnishes engineering and technical personnel in these fields with a powerful, efficient, reliable, and user-friendly set of tools and methods.

Deep learning has achieved impressive results in image classification, computer vision and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floating-point operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, our book will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS due to its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge about machine learning and deep learning to better understand the methods described in this book.

More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space.

This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency.