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• Introduction.- Optimization.- Metaheuristic optimization.- Image processing.- Image Segmentation using metaheuristics.- Multilevel thresholding for image segmentation based on metaheuristic Algorithms.- Otsu's between class variance and the tree seed algorithm.- Image segmentation using Kapur's entropy and a hybrid optimization algorithm.- Tsallis entropy for image thresholding.- Image segmentation with minimum cross entropy.- Fuzzy entropy approaches for image segmentation.- Image segmentation by gaussian mixture.- Image segmentation as a multiobjective optimization problem.- Clustering algorithms for image segmentation.- Contextual information in image thresholding.
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This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.
(source: Nielsen Book Data)

• V.
• 1. Estudio introductorio, compilación y comentarios / prólogo Raúl Eduardo González

• Segmentation Of Thermal Images By Means Of Metaheuristic Algorithms, For Failure Detection And Preventive Maintenance Of Electronic Systems.- A Survey on Image Processing for Hyperspectral and Remote Sensing Images.- Hybrid Grey-Wolf Optimizer Based Fractional Order Optimal Filtering for Texture Aware Quality Enhancement for Remotely Sensed Images.- Robust K- Means Technique For Band Reduction On Hyperspectral Image Segmentation.- Ethnic Characterization In Amalgamated People For Airport Security Using A Repository Of Images And Pigeon-Inspired Optimization (Pio) Algorithm For The Improvement Of Their Results}.- Multi-Level Image Thresholding Segmentation Using Non-Local Means 2d Histogram And Metaheuristics Algorithms.- Comparison Of Metaheuristic Methods For Template Matching.- Novel Feature Extraction Strategies Supporting 2d Shape Description And Retrieval.- Clustering Data Using Techniques Of Image Processing Erode And Dilate To Avoid The Use Of Euclidian Distance.- Estimation Of The Homography Matrix To Image Stitching.- Active Contour Model in Deep Learning Era: A Revise and Review.- Comparison of Evolutionary Techniques for Car Accident Prediction.- Salp Swarm Algorithm: A Comprehensive Review.- Segmentation Of Magnetic Resonance Brain Images Through The Self-Adaptive Differential Evolution Algorithm And The Minimum Cross-Entropy Criterion.- Automatic Detection Of Malignant Masses In Digital Mammograms Based On A Mcet-Hho Approach.- Cancer Cell Prediction using Machine Learning and Evolutionary Algorithms.- Meta Heuristic Approach of RMDL Classification of Parkinson's disease.- Fuzzy- Crow Search Optimization for Medical Image Segmentation.- Intelligent System For The Visual Support Of Caloric Intake Of Food In Inhabitants Of A Smart City Using A Deep Learning Model.- Image Thresholding with Metaheuristic Algorithms for Cerebral Injuries.- Generative Adversarial Network And Retinal Image Segmentation.
• (source: Nielsen Book Data)

This book presents a collection of the most recent hybrid methods for image processing. The algorithms included consider evolutionary, swarm, machine learning and deep learning. The respective chapters explore different areas of image processing, from image segmentation to the recognition of objects using complex approaches and medical applications. The book also discusses the theory of the methodologies used to provide an overview of the applications of these tools in image processing. The book is primarily intended for undergraduate and postgraduate students of science, engineering and computational mathematics, and can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence. Further, it is a valuable resource for researchers from the evolutionary computation, artificial intelligence and image processing communities.
(source: Nielsen Book Data)

• Preface.- Introduction.- Multilevel segmentation in digital images.- Multi-Circle detection on images.- Template matching.- Motion estimation.- Photovoltaic cell design.- Parameter identification of induction motors.- White blood cells Detection in images.- Estimation of view transformations in images.- Filter Design.
• (source: Nielsen Book Data)

This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.
(source: Nielsen Book Data)

• Color Spaces Advantages and Disadvantages in Image Color Clustering Segmentation.- Multi-objective Whale Optimization Algorithm for Multi-level Thresholding Segmentation.- Evaluating Swarm Optimization Algorithms for Segmentation of Liver Images.- Thermal Image Segmentation Using Evolutionary Computation Techniques.- News Videos Segmentation Using Dominant Colors Representation.
• (source: Nielsen Book Data)

This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.
(source: Nielsen Book Data)

• Cross Entropy Based Thresholding Segmentation of Magnetic Resonance Prostatic Images Using Metaheuristic Algorithms.- Hyperparameter Optimization in a Convolutional Neural Network Using Metaheuristic Algorithms.- Diagnosis of collateral effects in climate change through the identification of leaf damage using a novel heuristics and machine learning framework.- Feature engineering for Machine Learning and Deep Learning assisted Wireless Communication.- Genetic operators and their impact on the training of deep neural networks.- Implementation of metaheuristics with Extreme Learning Machines.- Architecture optimization of convolutional neural networks by micro genetic algorithms.- Optimising Connection Weights in Neural Networks using a Memetic Algorithm Incorporating Chaos Theory.- A review of metaheuristic optimization algorithms for wireless sensor networks.- A Metaheuristic Algorithm for Classification of White Blood Cells in Healthcare Informatics.- A Review of multi-level thresholding image segmentation using nature-inspired optimization algorithms.- Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering.- Variable Mesh Optimization for Continuous Optimization and Multimodal Problems.- Traffic control using image processing and deep learning techniques.- Drug Design and Discovery: Theory, Applications, Open Issues and Challenges.- Thresholding algorithm applied to Chest X-Ray images with Pneumonia.- Artificial neural networks for stock market prediction: a comprehensive review.- Image classification with Convolutional Neural Networks.- Applied Machine Learning Techniques to Find Patterns and Trends in the Use of Bicycle Sharing Systems Influenced by Traffic Accidents and Violent Events in Guadalajara, Mexico.- Machine Reading Comprehension (LSTM) Review (state of art).- A Survey of Metaheuristic Algorithms for Solving Optimization Problems.- Integrating metaheuristic algorithms and minimum cross entropy for image segmentation in mist conditions.- A Machine Learning application for Particle Physics: Mexico's involvement in the Hyper- Kamiokande observatory.- A novel metaheuristic approach for Image Contrast Enhancement based on gray-scale mapping.- Geospatial Data Mining Techniques Survey.- Integration of Internet of Things and cloud computing for Cardiac health recognition.- Combinatorial Optimization for Artificial Intelligence Enabled Mobile Network Automation.- Performance Optimization of PID Controller based on Parameters Estimation using Meta-Heuristic Techniques : A Comparative Study.- Solar Irradiation Changes Detection for Photovoltaic Systems through ANN trained with a Metaheuristic Algorithm.- Genetic Algorithm based Global and Local Feature Selection Approach for Handwritten Numeral Recognition.
• (source: Nielsen Book Data)

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities. .
(source: Nielsen Book Data)

• Analysis of Structural Bias in Differential Evolution Configurations.- Spherical Model of Population Dynamics in Differential Evolution.- Reinforcement Learning-based Differential Evolution for Global Optimization.- Analytical Study on the Role of Scale Factor Parameter of Differential Evolution Algorithm on its Convergence Nature.- The Trap of Sisyphus Work in Differential Evolution and How to Avoid It.- Investigations on Distributed Differential Evolution Framework with Fault Tolerance Mechanisms.- Differential Evolution for Water Management Problems.- Sobol Sequence Based MOSADE Algorithm for Multi-Objective Design of Water Distribution Networks.- A Comparative Study on Parameter Estimation of Covid Epidemiological Models using Differential Evolution Algorithm.- Applications of Differential Evolution in Electric Power Systems.- Detection of Heavy Sandstorm Regions using Composite Differential Evolution Algorithm.- A Hybrid Artificial Differential Evolution Gorilla Troops Optimizer for High Dimensional Optimization Problems.
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This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.
(source: Nielsen Book Data)

• Simulation of the relation between the number of COVID-19 death cases as a result of the number of handwashing facilities by using artificial intelligence.- Big data and big data analytics for COVID-19 epidemic.- Big data in COVID-19 assistance - Concepts, motivations, advances and applications in real-world.- Two Layer Hybrid Scheme of IMO and PSO for Optimization of Local Aligner: COVID-19 as a case study.- Factors affecting medical mask purchase decision in COVID-19 pandemic.- Classification approach for COVID-19 gene based on Harris hawks optimization. .
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This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations. .
(source: Nielsen Book Data)

• Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow.- Design and Performance Evaluation of Objective Functions Based on Various Measures of Fuzzy Entropies for Image Segmentation using Grey Wolf Optimization.- Improved Artificial Bee Colony Algorithm with Adaptive Pursuit Based Strategy Selection.- Beetle Antennae Search Algorithm for the Motion Planning of Industrial Manipulator.- Solving Optimal Power Flow with Considering Placement of TCSC and FACTS Cost Using Cuckoo Search Algorithm.
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The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving. The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.
(source: Nielsen Book Data)

• Particle swarm optimization based optimization for in-dustry inspection.- Ant Algorithms: from Drawback Identification to Quality and Speed Improvement.- Fault location techniques based on traveling waves with application in the protection of distribution systems with renewable energy and particle swarm optimization.- Improved Particle Swarm Optimization and Non-Quadratic Penalty Method for Non-Linear Programming Problems with Equality Constraints.- Recent Trends in Face Recognition Using Metaheuristic Optimization.
• (source: Nielsen Book Data)

This book presents recent contributions and significant development, advanced issues, and challenges. In real-world problems and applications, most of the optimization problems involve different types of constraints. These problems are called constrained optimization problems (COPs). The optimization of the constrained optimization problems is considered a challenging task since the optimum solution(s) must be feasible. In their original design, evolutionary algorithms (EAs) are able to solve unconstrained optimization problems effectively. As a result, in the past decade, many researchers have developed a variety of constraint handling techniques, incorporated into (EAs) designs, to counter this deficiency. The main objective for this book is to make available a self-contained collection of modern research addressing the general constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.
(source: Nielsen Book Data)

• Combined Optimization Algorithms for Incorporating DG in Distribution Systems.- Intelligent computational models for cancer diagnsis: A Comprehensive Review.- Elitist-Ant System metaheuristic for ITC
• 2021- Sports Timetabling.- Swarm intelligence algorithms-based Machine Learning Framework for Medical Diagnosis: A Comprehensive Review.- Aggregation of Semantically Similar News Articles with the help of Embedding Techniques and Unsupervised Machine Learning Algorithms: A Machine Learning Application with Semantic Technologies.- Integration of Machine Learning and Optimization Techniques for Cardiac Health Recognition.- Metaheuristics for Parameter Estimation of Solar Photovoltaic Cells: A Comprehensive Review.- Big Data Analysis using Hybrid Meta-heuristic Optimization Algorithm and MapReduce Framework.- Deep Neural Network for Virus Mutation Prediction: A Comprehensive Review.- 2D Target/Anomaly Detection in Time Series Drone Images using Deep Few-Shot Learning in Small Training Dataset.- Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons.- Early Detection of Coronary Artery Disease Using a PSO-based Neuroevolution Model.- Review for meta-heuristic optimization propels machine learning computations execution on spam comment area under digital security aegis region.- Solving reality based optimization trajectory problems with different metaphor inspired metaheuristic algorithms.- Parameter Tuning of PID controller Based on Arithmetic Optimization Algorithm in IOT systems.- Testing and Analysis of Predictive Capabilities of Machine Learning Algorithms.- AI Based Technologies for Digital and Banking Fraud During COVID -19.- Gradient-Based Optimizer for structural optimization problems.- Aquila Optimizer based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing.
• (source: Nielsen Book Data)

This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities. .
(source: Nielsen Book Data)

• Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup.- Metaheuristic algorithms in IoT: Optimized Edge Node Localization.- Jaya algorithm versus differential evolution: a comparative case study on optic disc localization in eye fundus images.- Minimum transmission power control for the Internet of Things with swarm intelligence algorithms.- An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System.- A meta-heuristic algorithm based on the happiness model.- Application of Metaheuristic Techniques for Enhancing the Financial Profitability of Wind Power Generation Systems.- Optimization of Demand Response.- Fitting curves of ruminal degradation using a metaheuristic approach.- Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques.- Multi-Circle Detection Using Multimodal Optimization.
• (source: Nielsen Book Data)

This book is a collection of various methodologies that make it possible for metaheuristics and hyper-heuristics to solve problems that occur in the real world. This book contains chapters that make use of metaheuristics techniques. The application fields range from image processing to transmission power control, and case studies and literature reviews are included to assist the reader. Furthermore, some chapters present cutting-edge methods for load frequency control and IoT implementations. In this sense, the book offers both theoretical and practical contents in the form of metaheuristic algorithms. The researchers used several stochastic optimization methods in this book, including evolutionary algorithms and Swarm-based algorithms. The chapters were written from a scientific standpoint. As a result, the book is primarily aimed at undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, but it can also be used in courses on Artificial Intelligence, among other things. Similarly, the material may be beneficial to research in evolutionary computation and artificial intelligence communities.
(source: Nielsen Book Data)

• Implementation of an intelligent model based on Convolutional Neural Network for the detection of diseases in citrus crops caused by bird pests using an intelligent drone.- Intelligent application to detection of arachnid bites in children implementing deep learning techniques, an AmI-based solution.- Financial analysis over the smartest companies. .
• (source: Nielsen Book Data)

This book includes chapters related to the analysis of cultural differences as a tool to enrich tacit knowledge and make processes more efficient, the factors that influence job satisfaction and the value of social capital as a competitive strategy to achieve productivity and competitiveness of organizations, in addition to research of the utmost importance to discover the facets of the diamond with respect to the symbolic capital of the organizations where Generation Z will work and how it will discover the best time to establish an innovation ecosystem that will influence its work trajectory. Industry 4.0 requires a major paradigm shift, since human capital is a source of competitive advantage. Being competitive enables to a company, a region, a society or a country the power to advance in different areas, contributing to the benefit of a social group, therefore, and organizations need to make efforts that lead to adding value and generate a competitive advantage. Industrial applications based on artificial intelligence can change our lives in just one generation. The chapters in this book show progress and challenges related to real-world applications, as well as the need to strengthen human capital to achieve systemic and comprehensive competitiveness required in the XXI century. .
(source: Nielsen Book Data)

• Determining and applying productive, environmental and economical indicators and Indexes to a cyber physical system for greening process of supply chain.- Design of material delivery routes with towing equipment for the automotive sector using a Von Neumann topology of PSO under the Logistics 4.0 paradigm.- Industrial Accident Induced Coma: a multi-disciplinary perspective within the industry 4.0 paradigm.
• (source: Nielsen Book Data)

This book helps the reader to identify how different organizations in the context of diverse societies deploy their resources and leverage their capabilities to achieve better performance of its various labor skills, marketing, social responsibility and management capacity. Intelligent Logistics is a complex phenomenon that has become critical for companies to reach their development locally and internationally. On the one hand, macro-factors and market structure influence in business competitiveness, but also in a regional or sector context. The internal aspects and the use of various business tools contribute to the ability to create value in an organization. It is of utmost importance to understand the relevance of crucial aspects in the technological future that should be known and implemented by the Z generation of its incidence in the use of organizational models linked to artificial intelligence. Every innovative aspect in the use of new technologies for the distribution of goods and services will be crucial in a globalized world. An avant-garde society will require improved decision-making regarding Logistics 4.0 and its implementation in our lives respecting the environment and being sustainable together with invaluable principles of generating tacit knowledge for future generations. .
(source: Nielsen Book Data)