关键词： chronic stroke   predictive models   Fugl-Meyer Assessment   machine learning   white matter disconnectivity  

摘要： Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 +/- 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R-2. Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient's postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients' response to therapy and, therefore, could be included in prospective studies.

关键词： Signal processing   Full waveform   Inversion  

摘要： Logistic regression, neural networks and support vector machines are tested for their effectiveness in isolating surface waves in seismic shot records. To distinguish surface waves from other arrivals, we train the algorithms on three distinguishing features of surface-wave dispersion curves in the k-omega domain: spectrum coherency of the trace's magnitude spectrum, local dip and the frequency range for a fixed wavenumber k in the spectrum. Numerical tests on synthetic data show that the kernel-based support vector machines algorithm gives the highest accuracy in predicting the surface-wave window in the k-omega domain compared to neural networks and logistic regression. This window is also used to automatically pick the fundamental dispersion curve. The other two methods correctly pick the low-frequency part of the dispersion curve but fail at higher frequencies where there is interference with higher-order modes.

关键词： Support vector machines  

摘要： Machine learning (ML) algorithms were explored for the prediction of the crystallization propensity based on molecular descriptors and fingerprints generated from 2D chemical structures and 3D molecular descriptors from 3D chemical structures optimized with empirical methods. In total, 57815 molecules were retrieved from the Reaxys (R) database, from those 53 998 molecules are recorded as crystalline (class A), 3097 as polymorphic (class B), and 720 as amorphous (class C). A training data set with 40 462 organic molecules was used to build the models, which were validated with an external test set comprising 17353 organic molecules. Several ML algorithms such as random forest (RF), support vector machines (SVM), and deep learning multilayer perceptron networks (MLP) were screened. The best performance was achieved with a consensus classification model obtained by RF, SVM, and MLP models, which predicted the external test set with an overall predictive accuracy (Q) of up to 80%.

关键词： 财务预警   机器学习   逻辑回归模型   支持向量机模型   决策树模型  

摘要： 当前我国上市公司信息披露仍存在着许多不规范的现象,个别上市公司在面临财务危机时,粉饰其财务风险,进行财务造假.本文利用上市超过3年的A股共3002家上市公司的财务数据,通过对ST、*ST和10家2019年退市的公司样本分析,根据不同的时间窗口,构建了财务预警因子库,共96个因子.分别采用机器学习中的逻辑斯蒂回归、支持向量机、决策树模型对因子数据进行训练.将数据集分为训练集和测试集,用训练集训练模型,并将模型应用在测试集中进行预测.结果如下:(1)三种模型均有较好的预测能力,其准确率都在94%以上;(2)决策树模型的预测效果最佳,支持向量机模型次之,逻辑斯蒂回归模型的预测效果最差;(3)三种模型都存在将较多的财务预警公司预测为正常公司的情况.本文通过将三种模型进行对比分析,以寻找更加优质的财务预警模型,来更好地帮助企业识别财务风险.

关键词： 机器学习方法   海量数据   硬件技术   深度学习   自编码   代谢组学   基因组学   生物医学领域  

摘要： 随着科技水平的不断提高,研究者们可以获得有关疾病的各类数据,例如基因组学、蛋白质组学、代谢组学、影像学等。这些海量数据的获得使得我们对疾病的了解更加深入。但随之而来的问题是,研究者们如何从海量数据中提取有用的信息,从而集中资源进行深入的研究,成为科研工作者面临的新挑战。近年来硬件技术的飞速发展和存储数据能力的增强,促进了大数据机器学习方法的发展,研究者已经将机器学习方法,特别是深度学习,应用于这类复杂数据的分析[1-4]。

关键词： 变量排序   机器学习方法   模型不确定性   小样本问题   非线性特征   学习视角   实证研究   传统分析方法  

摘要： 经过近30年的发展,跨国实证研究中主流的回归分析范式日益显示出方法论困境。由于样本有限性带来的模型不确定性问题,对传统跨国实证分析方法所得结论的归纳、总结与进一步发展形成了显著的制约。针对这一困境,论文提出可以充分利用新近的机器学习方法,有效缓解或克服传统分析方法存在的局限,从而对传统分析范式形成有益补充。论文系统阐述了机器学习方法的三个优势:适应小样本问题;处理变量排序、选择问题;捕捉样本非线性特征。

关键词： Water monitoring   Water quality   Surrogate parameters   Random forests   Soft-sensors   Machine learning  

摘要： Continuous high frequency water quality monitoring is becoming a critical task to support water management. Despite the advancements in sensor technologies, certain variables cannot be easily and/or economically monitored in-situ and in real time. In these cases, surrogate measures can be used to make estimations by means of data-driven models. In this work, variables that are commonly measured in-situ are used as surrogates to estimate the concentrations of nutrients in a rural catchment and in an urban one, making use of machine learning models, specifically Random Forests. The results are compared with those of linear modelling using the same number of surrogates, obtaining a reduction in the Root Mean Squared Error (RMSE) of up to 60.1%. The profit from including up to seven surrogate sensors was computed, concluding that adding more than 4 and 5 sensors in each of the catchments respectively was not worthy in terms of error improvement. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： 词干切分   统计学习模型   最大熵模型   条件随机场模型  

摘要： 自然语言处理任务中词处理是基础性的工作,其结果直接影响后续任务的效果。词干和构形附加成分是哈萨克语单词的组成成分,其中词干显示单词的主要意义,而构形附加成分中包含着词法和句法信息,因此词干切分是对哈萨克语进行有效处理的基础。文中构建了哈萨克语词干切分语料库,并通过将哈萨克语词干切分看作是序列化标注问题,提出一种有效的哈萨克语词标注方法,并基于最大熵模型和条件随机场模型构建了对比词干切分实验。结果表明基于条件随机场模型的词干切分准确率比现有最好的哈萨克语词干切分系统的准确率有15%的提高。该方法对哈萨克语词干切分相较于基于规则的方法有了一定的提升。

关键词： 电路故障诊断   故障字典技术   机器学习   支持向量机   人工神经网络   隐马尔科夫模型  

摘要： 应用机器学习方法能够有效降低电路故障诊断计算量和复杂度。介绍了电路故障诊断技术中的试验前仿真方法和试验后仿真方法,分析了试验前仿真方法中基于故障字典技术的机器学习方法的优势。阐述了四种机器学习方法——支持向量机、人工神经网络、模糊分类和隐马尔科夫模型,并对机器学习方法在常用模拟电路和电力设备中的故障诊断应用实例进行了评析。研究表明:机器学习方法能够与电路故障诊断相结合,使电路尤其是模拟电路的稳定性和效率得到很大的提升。介绍的应用实例为电路故障诊断研究明确了方向,对机器学习方法在电路故障诊断中的进一步应用和发展具有重要启示意义。

关键词： 时间序列分析   机器学习   肺结核   预测  

摘要： 目的研究时间序列分析与机器学习方法在预测肺结核发病趋势中的应用。方法使用江苏省2009-2018年肺结核月度发病率数据,构建时间序列分析(ARIMA模型)、机器学习方法(支持向量回归(SVR)、BP神经网络)和两者的组合方法(ARIMA-SVR、ARIMA-BPANN)共5种预测模型,分析评价各模型预测性能。结果 ARIMA、SVR、BP神经网络、ARIMA-SVR、ARIMA-BPANN均方误差分别为0.0356、0.0364、0.0384、0.0329、0.0336;平均相对误差分别为5.76%、6.19%、6.20%、5.63%、5.70%。结论时间序列分析优于机器学习方法,而二者组合模型预测效果优于单独方法,ARIMA-SVR模型在江苏省肺结核发病趋势预测分析中具有较好的应用价值。