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盆底超声组学与压力性尿失禁患者病情严重程度的关系评价研究
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作者 秦汉科 《影像研究与医学应用》 2024年第14期48-50,54,共4页
目的:探讨与评价盆底超声组学与压力性尿失禁患者病情严重程度的关系。方法:选择2023年1月—2024年2月在新疆伊犁哈萨克自治州新华医院诊治的60例压力性尿失禁患者作为研究对象,均给予病情严重程度判定,同时给予盆底超声组学与诊断价值... 目的:探讨与评价盆底超声组学与压力性尿失禁患者病情严重程度的关系。方法:选择2023年1月—2024年2月在新疆伊犁哈萨克自治州新华医院诊治的60例压力性尿失禁患者作为研究对象,均给予病情严重程度判定,同时给予盆底超声组学与诊断价值分析。结果:在60例患者中,病情严重程度轻度48例,中重度12例,分别占比80.00%和20.00%。受试者工作特征(ROC)曲线分析显示LR、支持向量分类(SVC)、轻量梯度提升机(LightGBM)和RF四种机器学习算法模型判断压力性尿失禁患者病情严重程度的曲线下面积分别为0.809、0.854、0.858和0.910,预测中重度压力性尿失禁的灵敏度与特异度均≥75.00%。结论:盆底超声组学可为压力性尿失禁患者提供很好的病情严重程度评估价值。 展开更多
关键词 压力性尿失禁 盆底超声组学 机器学习算法模型 诊断价值
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基于连续小波变换下的土壤有害元素砷含量估测
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作者 王雪梅 玉米提·买明 +2 位作者 黄晓宇 李锐 刘东 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2023年第1期206-212,共7页
与传统检测方法相比,利用高光谱技术进行土壤有害元素砷含量的估算,具有快速、准确,成本低的特点,可对干旱区绿洲土壤有害元素砷污染进行动态监测。基于新疆渭干河-库车河三角洲绿洲耕层土壤样品的采集,获取土壤光谱数据和有害元素砷含... 与传统检测方法相比,利用高光谱技术进行土壤有害元素砷含量的估算,具有快速、准确,成本低的特点,可对干旱区绿洲土壤有害元素砷污染进行动态监测。基于新疆渭干河-库车河三角洲绿洲耕层土壤样品的采集,获取土壤光谱数据和有害元素砷含量。通过bior1.3,db4,gaus4和mexh这4种小波基函数对土壤原始光谱反射率进行连续小波变换,并将变换后光谱数据与有害元素砷进行相关分析,以筛选出的敏感小波系数为自变量,采用偏最小二乘回归、支持向量机回归、BP神经网络和随机森林回归方法对有害元素砷含量进行高光谱反演。研究结果显示:(1)4种小波基函数在3~8尺度的光谱分解效果明显优于其他尺度,特别是4~6尺度的连续小波变换有效提升了光谱反射率与土壤有害元素砷之间的相关性,通过显著性检验的小波系数数量有了明显增多(p<0.01),在可见光的400~700 nm以及近红外的1100~1700和2200~2400 nm附近具有较强的相关性;(2)通过比较4种小波基函数对光谱数据中有效信息的辨识能力,认为小波基函数bior1.3和mexh要优于db4和gaus4,其中bior1.3的光谱分解效果最好,gaus4相对最弱;通过bior1.3第5尺度的光谱变换,与土壤有害元素砷显著相关的波段数量最多,为507个(p<0.01);(3)比较4种建模方法的反演结果发现,SVMR,BPNN和RFR模型相较于PLSR模型具有更强的估测能力,模型的估测精度更高。综合分析各模型的稳定性及估测精度后,认为bior1.3-25-RFR模型可作为研究区土壤有害元素砷的最佳估测模型。该模型的训练集和验证集的R 2分别为0.893和0.639,RMSE为1.075和1.651 mg·kg^(-1),RPD分别为2.89和1.64,表明模型估测效果较好,稳定性较强。采用合适的小波基函数进行连续小波变换可减少土壤高光谱数据中的白噪声,挖掘出土壤光谱数据中的有效信息,对土壤有害元素砷含量的准确估测提供有力的技术保障。 展开更多
关键词 小波基函数 分解尺度 小波系数 机器学习算法模型 有害元素砷
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Prediction of dust fall concentrations in urban atmospheric environment through support vector regression 被引量:2
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作者 焦胜 曾光明 +3 位作者 何理 黄国和 卢宏玮 高青 《Journal of Central South University》 SCIE EI CAS 2010年第2期307-315,共9页
Support vector regression (SVR) method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study... Support vector regression (SVR) method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters (insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ) are 0.001, 0.5, and 2 000, respectively. 展开更多
关键词 support vector regression urban air quality dust fall soeio-economic factors radial basis function
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Parallel solving model for quantified boolean formula based on machine learning
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作者 李涛 肖南峰 《Journal of Central South University》 SCIE EI CAS 2013年第11期3156-3165,共10页
A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance ... A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance in QBF parallel solving system,and the experimental evaluation scheme was also designed.It shows that the characterization factor of clause and cube influence the solving performance markedly in our experiment.At the same time,the heuristic machine learning algorithm was applied,support vector machine was chosen to predict the performance of QBF parallel solving system based on clause sharing and cube sharing.The relative error of accuracy for prediction can be controlled in a reasonable range of 20%30%.The results show the important and complex role that knowledge sharing plays in any modern parallel solver.It shows that the parallel solver with machine learning reduces the quantity of knowledge sharing about 30%and saving computational resource but does not reduce the performance of solving system. 展开更多
关键词 machine learning quantified boolean formula parallel solving knowledge sharing feature extraction performance prediction
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