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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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發(fā)表于 2025-3-21 19:36:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Nature-Inspired Optimization Methodologies in Biomedical and Healthcare
編輯Janmenjoy Nayak,Asit Kumar Das,Sheryl Brahnam
視頻videohttp://file.papertrans.cn/663/662080/662080.mp4
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
叢書(shū)名稱(chēng)Intelligent Systems Reference Library
圖書(shū)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
關(guān)鍵詞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
The information of publication is updating

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Preventing the Early Spread of Infectious Diseases Using Particle Swarm Optimization, quadratic programming problem. The version is derived from training data which provides insight into the force of the spread of the latest infectious illnesses. The proposed version performs on par with the advanced Bayesian Monte Carlo version. The anticipated approach empirically assesses authori
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Multi-Objective Optimization Algorithms in Medical Image Analysis,olution refinement by Nelder—Mead Algorithm. Our experiments show that for all colors from palette error according CIEDE2000 is less than 1. If error of CIEDE2000 for colors after matching is more than 1 the difference between color will be visible for observer.
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Heart Failure Detection from Clinical and Lifestyle Information using Optimized XGBoost with Gravitd (iii) Optimizing the various hyperparameters of XGBoost such as learning rate, subsample, L2 regularization, L1 regularization, max depth, and max delta step by using Gravitational search algorithm (GSA). The proposed approach has been evaluated by using various performance metrics and compared wi
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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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An Optimistic Bayesian Optimization Based Extreme Learning Machine for Polycystic Ovary Syndrome Diom Kaggle. Further, the efficacy of the proposed ELM and Bayesian optimization algorithm has been compared with SVM, MLP, ELM and ELM and Genetic algorithm. The experimental results reveal that ELM and Bayesian optimization attained better performance of 99.31% accuracy when compared with other mach
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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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Early Detection of Chronic Obstructive Pulmonary Disease Using LSTM-Firefly Based Deep Learning Modd superior results than the LSTM-Random Search and LSTM-Hyperband. Therefore, the adoption of LSTM-Firefly is beneficial in terms of COPD detection and diagnosis with clinically acceptable performance compared to LSTM—Random Search, LSTM—Hyperband, LSTM, and other machine learning algorithms such as
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發(fā)表于 2025-3-23 06:49:44 | 只看該作者
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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