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Kategorie szczegółowe BISAC

Nature-Inspired Optimization Methodologies in Biomedical and Healthcare

ISBN-13: 9783031175435 / Angielski / Twarda / 2022 / 293 str.

Janmenjoy Nayak; Asit Kumar Das; Bighnaraj Naik
Nature-Inspired Optimization Methodologies in Biomedical and Healthcare Janmenjoy Nayak Asit Kumar Das Bighnaraj Naik 9783031175435 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Nature-Inspired Optimization Methodologies in Biomedical and Healthcare

ISBN-13: 9783031175435 / Angielski / Twarda / 2022 / 293 str.

Janmenjoy Nayak; Asit Kumar Das; Bighnaraj Naik
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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.

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.

Kategorie:
Technologie
Kategorie BISAC:
Technology & Engineering > Engineering (General)
Technology & Engineering > Biomedical
Medical > Informatics
Wydawca:
Springer
Seria wydawnicza:
Intelligent Systems Reference Library
Język:
Angielski
ISBN-13:
9783031175435
Rok wydania:
2022
Dostępne języki:
Numer serii:
000385804
Ilość stron:
293
Waga:
0.61 kg
Wymiary:
23.39 x 15.6 x 1.91
Oprawa:
Twarda
Dodatkowe informacje:
Wydanie ilustrowane

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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