Machine learning model predicts the occurrence of acute kidney injury after open surgery for abdominal aortic aneurysm repair. [PDF]
Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2023 Sheng C, Liao M, Zhou H, Yang P.
europepmc +1 more source
[Efficacy of machine learning models versus Cox regression model for predicting prognosis of esophagogastric junction adenocarcinoma]. [PDF]
Nan Fang Yi Ke Da Xue Xue Bao, 2023 Gao K, Wang Y, Cao H, Jia J.
europepmc +1 more source
[An interpretable machine learning-based prediction model for risk of death for patients with ischemic stroke in intensive care unit]. [PDF]
Nan Fang Yi Ke Da Xue Xue Bao, 2023 Luo X, Cheng Y, Wu C, He J.
europepmc +1 more source
Construction of a predictive model for radiation proctitis after radiotherapy for female pelvic tumors based on machine learning. [PDF]
Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2022 Xie H, Gong M, Zhang J, Li Q.
europepmc +1 more source
Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm. [PDF]
Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2023 Jiang Z +4 more
europepmc +1 more source
基于迁移学习和红外热成像的金属氧化物避雷器表面污染状态检测方法
Dianci bileiqi金属氧化物避雷器外壳受灰尘、盐、碱等污染物长期沉积的影响，会导致其过早失效，因此，准确地检测金属氧化物避雷器表面的污染状态是非常重要的。基于此，面向饱和积污提出了一种利用红外热成像技术和迁移学习检测金属氧化物避雷器外壳污染严重程度的新方法。该方法通过拍摄不同污染状态下金属氧化物避雷器的红外热图像，经过适当的预处理后，捕获的红外热图像被送入预训练卷积神经网络“ResNet50”进行自动特征提取。提取的深度特征被输入到4个机器学习分类器，即k-最近邻分类器、支持向量机分类器 ...
沈顺群 +5 more
doaj
[Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2023 Li Z, Tian B, Liang H.
europepmc +1 more source
[Identification of Characteristic lncRNA Molecular Markers in Osteoarthritis by Integrating GEO Database and Machine Learning Strategies and Experimental Validation]. [PDF]
Sichuan Da Xue Xue Bao Yi Xue Ban, 2023 Zhou Q +5 more
europepmc +1 more source
基于模拟退火法与多层感知机的变压器故障诊断模型及其泛化性能研究
Gaoya dianqi为诊断电力变压器内部的潜伏性故障，以溶解气体分析（DGA）数据为特征量，提出了一种基于多层感知机（MLP）的变压器故障诊断模型。以实际运行变压器的故障数据为学习样本，利用模拟退火法实现多层感知机内部节点之间的连接权重优化。以不同特征组合作为MLP的输入，对比、分析了MLP诊断故障类型的正确率；研究了MLP拓扑结构、参数正则化等对诊断模型泛化性能的影响。使用训练数据以外的变压器故障数据测试学习完成的诊断模型，获得较高的测试准确率。
高超 +9 more
doaj
[Research on gait recognition and prediction based on optimized machine learning algorithm]. [PDF]
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2022 Gao J, Ma C, Su H, Wang S, Xu X, Yao J.
europepmc +1 more source