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Titlebook: Nature-Inspired Optimization Methodologies in Biomedical and Healthcare; Janmenjoy Nayak,Asit Kumar Das,Sheryl Brahnam Book 2023 The Edito

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发表于 2025-3-21 19:36:35 | 显示全部楼层 |阅读模式
书目名称Nature-Inspired Optimization Methodologies in Biomedical and Healthcare
编辑Janmenjoy Nayak,Asit Kumar Das,Sheryl Brahnam
视频video
概述Presents recent research on nature-inspired optimization methodologies in biomedical and health care.Covers advanced methodologies, challenges, and solutions to diversified healthcare issues.Presents
丛书名称Intelligent Systems Reference Library
图书封面Titlebook: Nature-Inspired Optimization Methodologies in Biomedical and Healthcare;  Janmenjoy Nayak,Asit Kumar Das,Sheryl Brahnam Book 2023 The Edito
描述.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..
出版日期Book 2023
关键词SWARM INTELLIGENCE; NATURE INSPIRED ALGORITHMS; EVOLUTIONARY ALGORITHM; BIO-INSPIRED COMPUTATION; HEALTH
版次1
doihttps://doi.org/10.1007/978-3-031-17544-2
isbn_softcover978-3-031-17546-6
isbn_ebook978-3-031-17544-2Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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,Hybridization of Fuzzy Theory and Nature-Inspired Optimization for Medical Report Summarization,rocessing in this chapter using sentence tokenization, then stopword removal, stemming operations, and ultimately vectorization using the BioBERT model. Consequently, a structured data is generated to process each report in feature extraction process and then clustered the similar sentences by Fuzzy
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Advance Machine Learning and Nature-Inspired Optimization in Heart Failure Clinical Records Datasetich certainly derive the application of ML algorithms to enhance and systematize the automate processes. A population-based Natured inspired swarm algorithms is proposed to extract the relevant parameters of Tree-based ML algorithms by using hyperparameter tuning. The proposed framework attains the
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Book 2023roaches 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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