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19 produkter
19 produkter
E-bok
Engelska, 20242 599 kr
Läs direkt efter köp
Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bio-inspired techniques such as modelling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by the extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modelling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks in force in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with the extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modelling of data obtained by continuous glucose monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modelling, prediction, and classification.- Addresses the online identification of diabetes mellitus using a high-order recurrent neural network trained online by an extended Kalman filter.- Covers parametric identification of compartmental models used to describe diabetes mellitus.- Provides modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia.
Häftad, Engelska, 2024
1 826 kr
Skickas inom 10-15 vardagar
Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bioinspired techniques such as modeling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by an extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modeling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in the early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks present in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with an extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose-tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modeling, prediction, and classification.Addresses the online identification of diabetes mellitus using a high-order recurrent neural network trained online by an extended Kalman filter.Covers parametric identification of compartmental models used to describe diabetes mellitus.Provides modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia.
Inbunden, Engelska, 2022
993 kr
Skickas inom 10-15 vardagar
The use of metaheuristic algorithms (MA) has been increasing in recent years, and the image processing field is not the exempted of their application. In the last two years a big amount of MA has been introduced as alternatives for solving complex optimization problems. This book collects the most prominent MA of the 2019 and 2020 and verifies its use in image processing tasks. In addition, literature review of both MA and digital image processing is presented as part of the introductory information. Each algorithm is detailed explained with special focus in the tuning parameters and the proper implementation for the image processing tasks. Besides several examples permits to the reader explore and confirm the use of this kind of intelligent methods. Since image processing is widely used in different domains, this book considers different kinds of datasets that includes, magnetic resonance images, thermal images, agriculture images, among others. The reader then can have some ideas of implementation that complement the theory exposed of each optimization mechanism. Regarding the image processing problems this book consider the segmentation by using different metrics based on entropies or variances. In the same way, the identification of different shapes and the detection of objects are also covered in the corresponding chapters. Each chapter is complemented with a wide range of experiments and statistical analysis that permits the reader to judge about the performance of the MA. Finally, there is included a section that includes some discussion and conclusions. This section also provides some open questions and research opportunities for the audience.
Häftad, Engelska, 2024
326 kr
Skickas inom 10-15 vardagar
The use of metaheuristic algorithms (MA) has been increasing in recent years, and the image processing field is not the exempted of their application. In the last two years a big amount of MA has been introduced as alternatives for solving complex optimization problems. This book collects the most prominent MA of the 2019 and 2020 and verifies its use in image processing tasks. In addition, literature review of both MA and digital image processing is presented as part of the introductory information. Each algorithm is detailed explained with special focus in the tuning parameters and the proper implementation for the image processing tasks. Besides several examples permits to the reader explore and confirm the use of this kind of intelligent methods. Since image processing is widely used in different domains, this book considers different kinds of datasets that includes, magnetic resonance images, thermal images, agriculture images, among others. The reader then can have some ideas of implementation that complement the theory exposed of each optimization mechanism. Regarding the image processing problems this book consider the segmentation by using different metrics based on entropies or variances. In the same way, the identification of different shapes and the detection of objects are also covered in the corresponding chapters. Each chapter is complemented with a wide range of experiments and statistical analysis that permits the reader to judge about the performance of the MA. Finally, there is included a section that includes some discussion and conclusions. This section also provides some open questions and research opportunities for the audience.
Inbunden, Engelska, 2026
2 063 kr
Skickas inom 10-15 vardagar
Designing new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.Maintaining diversity among the population elements throughout the iterative process is also essential. This diversity facilitates a more thorough and efficient exploration of the search space. In swarm intelligence algorithms, there are multiple methods to measure diversity, each with its own advantages and disadvantages.This book presents the theory behind the initialization process and the different mechanisms. Additionally, it includes a comparative study of various diversity indicators. It explores different methodologies to compute its value and explains how it can be incorporated as a mechanism for deciding when to apply operators during the optimization process. Multiple examples are provided in the book using two classical algorithms: Differential Evolution and Particle Swarm Optimization. It includes MATLAB® code and offers several exercises that readers can use for experimentation and design purposes.
E-bok
PDF, Engelska, 20262 454 kr
Läs direkt efter köp
Designing new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.Maintaining diversity among the population elements throughout the iterative process is also essential. This diversity facilitates a more thorough and efficient exploration of the search space. In swarm intelligence algorithms, there are multiple methods to measure diversity, each with its own advantages and disadvantages.This book presents the theory behind the initialization process and the different mechanisms. Additionally, it includes a comparative study of various diversity indicators. It explores different methodologies to compute its value and explains how it can be incorporated as a mechanism for deciding when to apply operators during the optimization process. Multiple examples are provided in the book using two classical algorithms: Differential Evolution and Particle Swarm Optimization. It includes MATLAB(R) code and offers several exercises that readers can use for experimentation and design purposes.
E-bok
Engelska, 20262 454 kr
Läs direkt efter köp
Designing new algorithms in swarm intelligence is a complex undertaking. Two critical factors have been seen to have a direct correlation with positive results. First is initialization, which serves as the initial step for all swarm intelligence techniques. Candidate solutions are generated to form the initial population, which are subsequently modified during the iterative process. A well-initialized population increases the algorithm's chances of avoiding local optima and finding the global optimum in fewer iterations. Although random distributions are commonly used for initialization, there are various ways to initialize the population elements.Maintaining diversity among the population elements throughout the iterative process is also essential. This diversity facilitates a more thorough and efficient exploration of the search space. In swarm intelligence algorithms, there are multiple methods to measure diversity, each with its own advantages and disadvantages.This book presents the theory behind the initialization process and the different mechanisms. Additionally, it includes a comparative study of various diversity indicators. It explores different methodologies to compute its value and explains how it can be incorporated as a mechanism for deciding when to apply operators during the optimization process. Multiple examples are provided in the book using two classical algorithms: Differential Evolution and Particle Swarm Optimization. It includes MATLAB(R) code and offers several exercises that readers can use for experimentation and design purposes.
Del 865 - Advances in Intelligent Systems and Computing
Trends and Applications in Software Engineering
Proceedings of the 7th International Conference on Software Process Improvement (CIMPS 2018)
Häftad, Engelska, 2018
1 668 kr
Skickas inom 10-15 vardagar
This book gathers a selection of papers presented at the 2018 International Conference on Software Process Improvement (CIMPS 2018). CIMPS 2018 offered a global forum for researchers and practitioners to present and discuss the latest innovations, trends, findings, experiences and concerns in Software Engineering, embracing several aspects such as Software Processes, Security in Information and Communication Technology, and Big Data. Two of the conference’s main aims were to support the drive toward a holistic symbiosis of the academic world, society, industry, government and business community, and to promote the creation of networks by disseminating the results of recent research in order to align their needs. CIMPS 2018 was made possible by the support of the CIMAT A.C., CUCEI (Universidad de Guadalajara, México), AISTI (Associação Ibérica de Sistemas e Tecnologas de Informação), and ReCIBE (Revista electrónica de Computación, Informática, Biomédica y Electrónica).
E-bok
Engelska, 20182 110 kr
Läs direkt efter köp
This book gathers a selection of papers presented at the 2018 International Conference on Software Process Improvement (CIMPS 2018). CIMPS 2018 offered a global forum for researchers and practitioners to present and discuss the latest innovations, trends, findings, experiences and concerns in Software Engineering, embracing several aspects such as Software Processes, Security in Information and Communication Technology, and Big Data.
Two of the conference’s main aims were to support the drive toward a holistic symbiosis of the academic world, society, industry, government and business community, and to promote the creation of networks by disseminating the results of recent research in order to align their needs. CIMPS 2018 was made possible by the support of the CIMAT A.C., CUCEI (Universidad de Guadalajara, México), AISTI (Associação Ibérica de Sistemas e Tecnologas de Informação), and ReCIBE (Revista electrónica de Computación, Informática, Biomédica y Electrónica).
Häftad, Engelska, 2018
1 114 kr
Skickas inom 10-15 vardagar
This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip thoseof the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
Inbunden, Engelska, 2023
1 727 kr
Skickas inom 10-15 vardagar
Recently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. These new methods introduce interesting mechanisms and innovative collaborative strategies that facilitate the efficient exploration and exploitation of extensive search spaces characterized by numerous dimensions. The objective of this book is to present advancements that discuss novel alternative metaheuristic developments that have demonstrated their effectiveness in tackling various complex problems. This book encompasses a variety of emerging metaheuristic methods and their practical applications. The content is presented from a teaching perspective, making it particularly suitable for undergraduate and postgraduate students in fields such as science, electrical engineering, and computational mathematics. The book aligns well with courses in artificial intelligence, electrical engineering, and evolutionary computation. Furthermore, the material offers valuable insights to researchers within the metaheuristic and engineering communities. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques. These methods transcend mere theoretical tools that have been adapted to effectively address the significant real-world problems commonly encountered in engineering domains.
E-bok
Engelska, 20232 215 kr
Läs direkt efter köp
Recently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. These new methods introduce interesting mechanisms and innovative collaborative strategies that facilitate the efficient exploration and exploitation of extensive search spaces characterized by numerous dimensions. The objective of this book is to present advancements that discuss novel alternative metaheuristic developments that have demonstrated their effectiveness in tackling various complex problems. This book encompasses a variety of emerging metaheuristic methods and their practical applications. The content is presented from a teaching perspective, making it particularly suitable for undergraduate and postgraduate students in fields such as science, electrical engineering, and computational mathematics. The book aligns well with courses in artificial intelligence, electrical engineering, and evolutionary computation. Furthermore, the material offers valuable insights to researchers within the metaheuristic and engineering communities. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques. These methods transcend mere theoretical tools that have been adapted to effectively address the significant real-world problems commonly encountered in engineering domains.
Häftad, Engelska, 2024
1 727 kr
Skickas inom 10-15 vardagar
Recently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. These new methods introduce interesting mechanisms and innovative collaborative strategies that facilitate the efficient exploration and exploitation of extensive search spaces characterized by numerous dimensions. The objective of this book is to present advancements that discuss novel alternative metaheuristic developments that have demonstrated their effectiveness in tackling various complex problems. This book encompasses a variety of emerging metaheuristic methods and their practical applications. The content is presented from a teaching perspective, making it particularly suitable for undergraduate and postgraduate students in fields such as science, electrical engineering, and computational mathematics. The book aligns well with courses in artificial intelligence, electrical engineering, and evolutionary computation. Furthermore, the material offers valuable insights to researchers within the metaheuristic and engineering communities. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques. These methods transcend mere theoretical tools that have been adapted to effectively address the significant real-world problems commonly encountered in engineering domains.
Del 100 - Intelligent Systems Reference Library
Applications of Evolutionary Computation in Image Processing and Pattern Recognition
Inbunden, Engelska, 2015
1 082 kr
Skickas inom 10-15 vardagar
This book presents the use of efficientEvolutionary Computation (EC) algorithms for solving diverse real-world imageprocessing and pattern recognition problems. It provides an overview of thedifferent aspects of evolutionary methods in order to enable the reader inreaching a global understanding of the field and, in conducting studies onspecific evolutionary techniques that are related to applications in imageprocessing and pattern recognition. It explains the basic ideas of the proposedapplications in a way that can also be understood by readers outside of thefield. Image processing and pattern recognition practitioners who are notevolutionary computation researchers will appreciate the discussed techniquesbeyond simple theoretical tools since they have been adapted to solvesignificant problems that commonly arise on such areas. On the other hand,members of the evolutionary computation community can learn the way in whichimage processing and pattern recognition problems can be translated into anoptimization task. The book has been structured so that each chapter can beread independently from the others. It can serve as reference book for studentsand researchers with basic knowledge in image processing and EC methods.
E-bok
PDF, Engelska, 20151 416 kr
Läs direkt efter köp
This book presents the use of efficientEvolutionary Computation (EC) algorithms for solving diverse real-world imageprocessing and pattern recognition problems. It provides an overview of thedifferent aspects of evolutionary methods in order to enable the reader inreaching a global understanding of the field and, in conducting studies onspecific evolutionary techniques that are related to applications in imageprocessing and pattern recognition. It explains the basic ideas of the proposedapplications in a way that can also be understood by readers outside of thefield. Image processing and pattern recognition practitioners who are notevolutionary computation researchers will appreciate the discussed techniquesbeyond simple theoretical tools since they have been adapted to solvesignificant problems that commonly arise on such areas. On the other hand,members of the evolutionary computation community can learn the way in whichimage processing and pattern recognition problems can be translated into anoptimization task. The book has been structured so that each chapter can beread independently from the others. It can serve as reference book for studentsand researchers with basic knowledge in image processing and EC methods.
Del 100 - Intelligent Systems Reference Library
Applications of Evolutionary Computation in Image Processing and Pattern Recognition
Häftad, Engelska, 2016
1 082 kr
Skickas inom 10-15 vardagar
This book presents the use of efficientEvolutionary Computation (EC) algorithms for solving diverse real-world imageprocessing and pattern recognition problems. It provides an overview of thedifferent aspects of evolutionary methods in order to enable the reader inreaching a global understanding of the field and, in conducting studies onspecific evolutionary techniques that are related to applications in imageprocessing and pattern recognition. It explains the basic ideas of the proposedapplications in a way that can also be understood by readers outside of thefield. Image processing and pattern recognition practitioners who are notevolutionary computation researchers will appreciate the discussed techniquesbeyond simple theoretical tools since they have been adapted to solvesignificant problems that commonly arise on such areas. On the other hand,members of the evolutionary computation community can learn the way in whichimage processing and pattern recognition problems can be translated into anoptimization task. The book has been structured so that each chapter can beread independently from the others. It can serve as reference book for studentsand researchers with basic knowledge in image processing and EC methods.
Inbunden, Engelska, 2018
1 114 kr
Skickas inom 10-15 vardagar
This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip thoseof the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
E-bok
Engelska, 20181 459 kr
Läs direkt efter köp
This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip thoseof the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
Inbunden, Engelska, 2026
2 345 kr
Skickas inom 5-8 vardagar