Nature-Inspired Optimization Methodologies in Biomedical and Healthcare [electronic resource] / edited by Janmenjoy Nayak, Asit Kumar Das, Bighnaraj Naik, Saroj K. Meher, Sheryl Brahnam.

Colaborador(es): Nayak, Janmenjoy [editor.] | Das, Asit Kumar [editor.] | Naik, Bighnaraj [editor.] | Meher, Saroj K [editor.] | Brahnam, Sheryl [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Intelligent Systems Reference Library ; 233Editor: Cham : Springer International Publishing : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XVIII, 293 p. 111 illus., 77 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031175442Tema(s): Computational intelligence | Biomedical engineering | Medical informatics | Computational Intelligence | Biomedical Engineering and Bioengineering | Health InformaticsFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q342Recursos en línea: Libro electrónicoTexto
Contenidos:
Nature-Inspired Optimization Algorithms: Past to Present -- Preventing the early spread of infectious diseases using Particle Swarm Optimization -- Optimized gradient boosting tree-based model for obesity level prediction from patient's physical condition and eating habits.
En: Springer Nature eBookResumen: This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the healthcare institutions and hospitals for practical analysis, storage, and data analysis. It explores the distinct nature-inspired optimization-based approaches that are able to handle more accurate outcomes for the current biomedical and healthcare problems. In addition to providing a state-of-the-art and advanced intelligent methods, it also enlightens an insight for solving diversified healthcare problems such as cancer and diabetes.
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Nature-Inspired Optimization Algorithms: Past to Present -- Preventing the early spread of infectious diseases using Particle Swarm Optimization -- Optimized gradient boosting tree-based model for obesity level prediction from patient's physical condition and eating habits.

This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the healthcare institutions and hospitals for practical analysis, storage, and data analysis. It explores the distinct nature-inspired optimization-based approaches that are able to handle more accurate outcomes for the current biomedical and healthcare problems. In addition to providing a state-of-the-art and advanced intelligent methods, it also enlightens an insight for solving diversified healthcare problems such as cancer and diabetes.

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