找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復(fù)制鏈接]
查看: 25656|回復(fù): 51
樓主
發(fā)表于 2025-3-21 19:36:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱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
叢書名稱Intelligent Systems Reference Library
圖書封面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

書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare影響因子(影響力)




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare影響因子(影響力)學(xué)科排名




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare網(wǎng)絡(luò)公開度




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare被引頻次




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare被引頻次學(xué)科排名




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare年度引用




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare年度引用學(xué)科排名




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare讀者反饋




書目名稱Nature-Inspired Optimization Methodologies in Biomedical and Healthcare讀者反饋學(xué)科排名




單選投票, 共有 1 人參與投票
 

0票 0.00%

Perfect with Aesthetics

 

1票 100.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 23:38:52 | 只看該作者
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
板凳
發(fā)表于 2025-3-22 01:10:50 | 只看該作者
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.
地板
發(fā)表于 2025-3-22 05:15:56 | 只看該作者
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
5#
發(fā)表于 2025-3-22 12:40:20 | 只看該作者
6#
發(fā)表于 2025-3-22 13:34:24 | 只看該作者
,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
7#
發(fā)表于 2025-3-22 20:23:25 | 只看該作者
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
8#
發(fā)表于 2025-3-22 21:12:26 | 只看該作者
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
9#
發(fā)表于 2025-3-23 02:19:26 | 只看該作者
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
10#
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..
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-8 16:42
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
瑞金市| 威宁| 天水市| 朝阳区| 鞍山市| 新竹市| 合水县| 临湘市| 丹棱县| 南澳县| 双峰县| 东明县| 江川县| 敖汉旗| 阆中市| 治多县| 阿城市| 筠连县| 汪清县| 宜阳县| 苗栗县| 阿拉善右旗| 桐庐县| 镇巴县| 台安县| 杭州市| 抚松县| 黄冈市| 廉江市| 灵川县| 普安县| 武威市| 旬阳县| 东乌珠穆沁旗| 贡嘎县| 伊宁县| 海兴县| 牡丹江市| 岳阳县| 邹城市| 长阳|