Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A two stage statistical framework for cold start spare part demand forecasting.

PloS one·2026
Same author

Event-preserving feature engineering for intermittent demand forecasting using SHOS.

Scientific reports·2026
Same author

Cross-modal attentive fusion network for tri-modal lesion growth prediction.

Scientific reports·2026
Same author

Topology-alloy interactions governing deformation and failure in LPBF-fabricated A286 and Inconel 718 lattice structures.

Scientific reports·2026
Same author

Prediction of the surface roughness of Ti-6Al-4 V alloy during surface grinding using machine learning models.

Scientific reports·2026
Same author

Experimental modeling and multi-objective optimization of Micro Arc Oxidation process parameters of aluminium alloy joints.

Scientific reports·2026
Same journal

Novel Parent Survey Measures Sensory Behaviors Incorporating Sensory Modality and Stimulus Intensity.

Heliyon·2026
Same journal

Expression of concern: "SQSTM1/p62 promotes the progression of gastric cancer through epithelial-mesenchymal transition" [Heliyon 10 (2024) e24409].

Heliyon·2026
Same journal

Expression of concern: "TL1A promotes metastasis and EMT process of colorectal cancer" [Heliyon 10 (2024) e24392].

Heliyon·2026
Same journal

Expression of concern: "Factors affecting timing of surgery following neoadjuvant chemoradiation for esophageal cancer" [Heliyon 9 (2023) e23212].

Heliyon·2026
Same journal

Expression of concern: "On stratified single-valued soft topogenous structures" [Heliyon 10 (2024) e27926].

Heliyon·2026
Same journal

Expression of concern: "Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN" [Heliyon 10 (2024) e27198].

Heliyon·2026
查看所有相关文章

相关实验视频

Updated: Jun 15, 2026

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth
08:15

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth

Published on: February 2, 2024

811

多目标肝癌算法:一种用于解决工程设计问题的新算法.

Kanak Kalita1,2, Janjhyam Venkata Naga Ramesh3, Robert Čep4

  • 1Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India.

Heliyon
|March 15, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了灵感来自瘤生长的多目标肝癌算法 (MOLCA),用于工程设计优化. MOLCA增强了搜索功能,有效地找到最佳解决方案,在各种基准上优于现有的算法.

关键词:
工程设计优化工程设计优化肝癌算法 肝癌算法莫尔卡 (MOLCA) 是一个水母.多目标优化优化多目标优化不占主导地位的溶液.帕雷托的前面帕雷托解决方案的解决方案

更多相关视频

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
12:24

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

Published on: September 30, 2021

5.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

相关实验视频

Last Updated: Jun 15, 2026

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth
08:15

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth

Published on: February 2, 2024

811
A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
12:24

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

Published on: September 30, 2021

5.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

科学领域:

  • 计算智能是一种计算智能.
  • 生物启发的计算 生物启发的计算
  • 工程优化工程优化

背景情况:

  • 多目标优化问题 (MOP) 在工程设计中很普遍.
  • 现有的算法在平衡勘探和开采中面临挑战,用于帕雷托前线识别.
  • 需要新的生物启发方法来解决复杂的MOP.

研究的目的:

  • 介绍解决MOP的多目标肝癌算法 (MOLCA).
  • 模拟肝脏瘤生长动态,以提高优化.
  • 在帕雷托最佳阵线上改善解决方案的融合和分布.

主要方法:

  • 摩尔卡将遗传操作员与基于随机对立的学习 (ROBL) 结合起来.
  • 集成精英非主导分类 (NDS),信息反机制 (IFM) 和拥挤距离 (CD) 选择.
  • 在ZDT,DTLZ,约束 (CONSTR,TNK,SRN,BNH,OSY,KITA) 和现实工程问题上进行评估.

主要成果:

  • 对于NSGWO,MOMVO,NSGA-II,MOEA/D和MOMPA来说,MOLCA表现出了竞争力的表现.
  • 量化指标 (GD,IGD,SP,SD,HV,RT) 显示了MOLCA在融合和分配方面的有效性.
  • 通过帕雷托前图进行定性分析,可视化证实了MOLCA的解决方案质量.

结论:

  • 摩尔卡为多目标工程优化提供了一种新且有效的生物灵感方法.
  • 该算法在识别帕雷托最佳前线方面表现出卓越的性能.
  • 摩尔卡为应对复杂的工程设计挑战提供了一种有价值的工具.