Nature-Inspired Optimization Methodologies in Biomedical and Healthcare
Springer International Publishing (Verlag)
978-3-031-17543-5 (ISBN)
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.
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.
| Erscheinungsdatum | 16.11.2022 |
|---|---|
| Reihe/Serie | Intelligent Systems Reference Library |
| Zusatzinfo | XVIII, 293 p. 111 illus., 77 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 625 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Technik | |
| Schlagworte | bio-inspired computation • biomedical • evolutionary algorithm • Healthcare • Nature Inspired Algorithms • Swarm intelligence |
| ISBN-10 | 3-031-17543-3 / 3031175433 |
| ISBN-13 | 978-3-031-17543-5 / 9783031175435 |
| Zustand | Neuware |
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