hybrid soft computing techniques (11 résultats)

Etat : New. In.

Paperback. Etat : new. Paperback. Soft computing approaches, such as fuzzy logic, neural networks, and genetic algorithms, can be integrated into the realms of data analysis and decision making. They can be applied to tackle complex data analysis tasks and support decision-making processes in various domains, including healthcare, finance, manufacturing, and transportation. By extracting meaningful patterns, soft computing techniques may increase the effectiveness and efficiency in handling large datasets. In this way, they may be useful for facilitating decision making in uncertain and dynamic environments. Hybrid Soft Computing Techniques for Machine Learning and Optimization bridges the gap between theoretical knowledge and practical applications in soft computing and data analysis. It explores advancements and innovations in industries where data-driven decision making is crucial. Covering topics such as learning, biomedical signal processing, and entity behaviors, this book is an excellent resource for computer scientists, engineers, practitioners, healthcare professionals, finance professionals, manufacturers, transportation specialists, professionals, researchers scholars, academicians, and more. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Etat : New. In.

Etat : New.

Etat : New.

Hardcover. Etat : new. Hardcover. Soft computing approaches, such as fuzzy logic, neural networks, and genetic algorithms, can be integrated into the realms of data analysis and decision making. They can be applied to tackle complex data analysis tasks and support decision-making processes in various domains, including healthcare, finance, manufacturing, and transportation. By extracting meaningful patterns, soft computing techniques may increase the effectiveness and efficiency in handling large datasets. In this way, they may be useful for facilitating decision making in uncertain and dynamic environments. Hybrid Soft Computing Techniques for Machine Learning and Optimization bridges the gap between theoretical knowledge and practical applications in soft computing and data analysis. It explores advancements and innovations in industries where data-driven decision making is crucial. Covering topics such as learning, biomedical signal processing, and entity behaviors, this book is an excellent resource for computer scientists, engineers, practitioners, healthcare professionals, finance professionals, manufacturers, transportation specialists, professionals, researchers scholars, academicians, and more. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Etat : new. Paperback. Soft computing approaches, such as fuzzy logic, neural networks, and genetic algorithms, can be integrated into the realms of data analysis and decision making. They can be applied to tackle complex data analysis tasks and support decision-making processes in various domains, including healthcare, finance, manufacturing, and transportation. By extracting meaningful patterns, soft computing techniques may increase the effectiveness and efficiency in handling large datasets. In this way, they may be useful for facilitating decision making in uncertain and dynamic environments. Hybrid Soft Computing Techniques for Machine Learning and Optimization bridges the gap between theoretical knowledge and practical applications in soft computing and data analysis. It explores advancements and innovations in industries where data-driven decision making is crucial. Covering topics such as learning, biomedical signal processing, and entity behaviors, this book is an excellent resource for computer scientists, engineers, practitioners, healthcare professionals, finance professionals, manufacturers, transportation specialists, professionals, researchers scholars, academicians, and more. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Etat : new. Hardcover. Soft computing approaches, such as fuzzy logic, neural networks, and genetic algorithms, can be integrated into the realms of data analysis and decision making. They can be applied to tackle complex data analysis tasks and support decision-making processes in various domains, including healthcare, finance, manufacturing, and transportation. By extracting meaningful patterns, soft computing techniques may increase the effectiveness and efficiency in handling large datasets. In this way, they may be useful for facilitating decision making in uncertain and dynamic environments. Hybrid Soft Computing Techniques for Machine Learning and Optimization bridges the gap between theoretical knowledge and practical applications in soft computing and data analysis. It explores advancements and innovations in industries where data-driven decision making is crucial. Covering topics such as learning, biomedical signal processing, and entity behaviors, this book is an excellent resource for computer scientists, engineers, practitioners, healthcare professionals, finance professionals, manufacturers, transportation specialists, professionals, researchers scholars, academicians, and more. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Etat : new. Hardcover. Soft computing approaches, such as fuzzy logic, neural networks, and genetic algorithms, can be integrated into the realms of data analysis and decision making. They can be applied to tackle complex data analysis tasks and support decision-making processes in various domains, including healthcare, finance, manufacturing, and transportation. By extracting meaningful patterns, soft computing techniques may increase the effectiveness and efficiency in handling large datasets. In this way, they may be useful for facilitating decision making in uncertain and dynamic environments. Hybrid Soft Computing Techniques for Machine Learning and Optimization bridges the gap between theoretical knowledge and practical applications in soft computing and data analysis. It explores advancements and innovations in industries where data-driven decision making is crucial. Covering topics such as learning, biomedical signal processing, and entity behaviors, this book is an excellent resource for computer scientists, engineers, practitioners, healthcare professionals, finance professionals, manufacturers, transportation specialists, professionals, researchers scholars, academicians, and more. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.