关键词： 机器学习   web   shell   PHP  

摘要： 本文简述了web shell分类和基于PHP的web shell的变形及检测方法,回顾了用机器学习方法检测基于PHP的web shell的现状,最后从样本准备、特征提取和实现方法等方面给出了提高机器学习算法检测效果的改进方向。

关键词： 机器学习方法   非条件Logistic回归法   支持向量机法   评价模型   Ⅰ类切口手术患者   抗菌药物   处方点评   合理用药  

摘要： 目的:建立Ⅰ类切口手术患者使用抗菌药物合理性的评价模型,为临床药师进行处方点评提供依据。方法:以某院2017年1月1日-2017年12月31日住院的432例Ⅰ类切口手术患者作为研究对象,提取患者年龄、院内感染、使用抗菌药物的种类数等多项诊疗信息;结合临床药师对患者预防及治疗使用抗菌药物合理性的点评结果,以抗菌药物类别(预防或治疗使用)为因变量、患者诊疗信息为自变量,分别利用机器学习方法中的非条件Logistic回归法和支持向量机法,将药师点评结果转化为机器学习模型可识别的客观指标,建立Ⅰ类切口手术患者使用抗菌药物合理性的分类判别模型,并以灵敏度、特异度、约登指数等为指标,另采用61例Ⅰ类切口手术患者样本对建立的模型进行验证;收集使用模型前(人工点评,2017年1-12月)、后(2018年1-10月)Ⅰ类切口手术患者使用抗菌药物处方合理性点评情况,对模型效果进行应用评价。结果:以非条件Logistic回归法建立的模型的灵敏度为65.63%、特异度为75.00%、约登指数为40.63%;以支持向量机法建立的模型主要参数gamma为0.01、cost为10,其灵敏度为92.19%、特异度为87.50%、约登指数为79.69%,支持向量机法建立的模型优于非条件Logistic回归法;对采用支持向量机法建立的模型进行验证,结果灵敏度为100%、特异度为88.57%、约登指数为88.57%;与使用模型前比较,使用模型后处方点评比例由69.44%升高到100%,抗菌药物预防使用率由23.84%下降到16.43%,品种选用合理率由37.86%升高到54.39%,使用疗程由5.01 d缩短到3.26 d。结论:应用机器学习方法中的支持向量机法建立Ⅰ类切口手术患者使用抗菌药物的评价模型,可实现处方点评全覆盖,提高Ⅰ类切口手术患者使用抗菌药物的合理水平,同时为药师的处方点评工作提供了新思路。

关键词： natural gas price   natural gas price forecasting   prediction model   machine learning methods  

摘要： Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

关键词： 空间负荷预测   数据挖掘   地块   Softmax概率分类器   负荷曲线  

摘要： 提出了一种数据驱动空间负荷预测方法。将网格化体系下的功能地块作为空间负荷预测的基本单元,并且通过多维指标体系进行属性描述。基于大量调研数据,通过数据挖掘方法对不同类型地块的空间负荷密度分布规律和负荷曲线典型形态进行提取。建立Softmax多元概率分类模型对未知地块的负荷水平类型进行匹配。自下而上对相邻地块负荷预测结果进行时域叠加,得到更大区域的预测信息,包括其负荷量和预测负荷曲线。算例仿真结果表明提出的空间负荷预测方法在预测精度上有一定提升。

关键词： Metagenomics   microbiota   embedded feature selection   operational taxonomic units (OTUs)   classification  

摘要： "Metagenomics" is the study of genomic sequences obtained directly from environmental microbial communities with the aim to linking their structures with functional roles. The field has been aided in the unprecedented advancement through high-throughput omics data sequencing. The outcome of sequencing are biologically rich data sets. Metagenomic data consisting of microbial species which outnumber microbial samples, lead to the 'curse of dimensionality" in datasets. Hence, the focus in metagenomics studies has moved towards developing efficient computational models using Machine Learning (ML), reducing the computational cost. In this paper, we comprehensively assessed various ML approaches to classifying high-dimensional human microbiota effectively into their functional phenotypes. We propose the application of embedded feature selection methods, namely, Extreme Gradient Boosting and Penalized Logistic Regression to determine important microbial species. The resultant feature set enhanced the performance of one of the most popular state-of-the-art methods, Random Forest (RF) over metagenomic studies. Experimental results indicate that the proposed method achieved best results in terms of accuracy, area under the Receiver Operating Characteristic curve (ROC-AUC), and major improvement in processing time. It outperformed other feature selection methods of filters or wrappers over RF and classifiers such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), and k- Nearest Neighbors (k-NN).

关键词： Bioassay   Biological properties   Feature relevance   Machine learning   Pollution   Regression   Soil   Trace element   Waste  

摘要： Purpose The full understanding of the effect of mineral waste-based fertilizer in soil is still unrelieved, because of the extreme complex chemical composition and plethora of their action pathways. The purposes of this paper is to quantify the input of PG into the soil ecosystem process, considering the direct effects of PG as a whole on soil environment using of a plethora of chemical, toxicological, and biological tests. Materials and methods Greenhouse experiment includes different PG doses (0, 1%, 3%, 7.5%, 15%, 25%, and 40%) and two-time collection points after treatments-7 and 28 days. For each treatment and each time collection point, we measure (i) soil pH, bioavailable (H(2)0 and NH4COOH-extractable) element content (S, P, K, Na, Mg, Ca, Fe, Zn, Sr, Ba, F);(ii) soil enzyme activities-dehydrogenase, urease, acid phosphatase, FDA;(iii) soil CO2 respiration activity with and without glucose addition;(iv) Eisenia fetida, Sinapis alba, and Avena sativa responses. Finally, we combine the ordinary chemical, toxicology, and biological measuring of soil properties with state-of-the-art mathematical analysis, namely (i) support vector machines (used for prediction), (ii) mutual information test (variable importance tasks), (iii) t-SNE and LLE algorithms (used for unsupervised classification). Results and discussion The results show similarity between the 0%, 1%, and 3% PG treatments in all collection times based on the toxicological and biological properties. Beyond 7.5% PG, some biological test was significantly inhibited in response to trace element stress. Among all tested parameters, soil urease activities, soil respiration activities after glucose addition, S. alba root lengths, and E. fetida survival rates show sensitivity to PG addition. Furthermore, the machine learning algorithms revealed that only several elements (mobile and water-soluble forms of Ca, Ba, Sr, S, and Na;water-soluble F) could be responsible to elevated soil toxicity for those indicators. S

关键词： Gross primary productivity (GPP)   Sentinel-2 (S2)   Landsat 8   Machine learning (ML)   Neural networks (NN)   Radiative transfer modeling (RTM)   Hybrid approach   Soil-canopy-observation of photosynthesis and the energy balance (SCOPE)   C3 crops  

摘要： Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP. With this approach, we by-pass the need for an intermediate step to retrieve the set of vegetation biophysical parameters needed to accurately model photosynthesis, while still accounting for the complex processes of the original physically-based model. Several implementations of the machine learning models are tested and validated using simulated and flux tower-based GPP data. Our final neural network model is able to estimate GPP at the tested flux tower sites with r(2) of 0.92 and RMSE of 1.38 gC d(-1) m(-2), which outperforms empirical models based on vegetation indices. The first test of applicability of this model to Landsat 8 data showed good results (r(2) of 0.82 and RMSE of 1.97 gC d(-1) m(-2)), which suggests that our approach can be further applied to other sensors. Modeling and testing is restricted to C3 crops in this study, but can be extended to C4 crops by producing a new training dataset with SCOPE that accounts for the different photosynthetic pathways. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local info

关键词： Prosthetic hip implant   Polyethylene wear   Pin-on-disc   Machine learning  

摘要： Pin-on-disc (PoD) experiments are widely used to quantify and rank wear of different material couples for prosthetic hip implant bearings. However, polyethylene wear results obtained from different PoD experiments are sometimes difficult to compare, which potentially leaves information inaccessible. We use machine learning methods to implement several data-driven models, and subsequently validate them by quantifying the prediction error with respect to published experimental data. A data-driven model can supplement results from PoD wear experiments, and enables predicting polyethylene wear of new PoD experiments based on its operating parameters. It also reveals the relative contribution of individual PoD operating parameters to the resulting polyethylene wear, thus informing design of experiments, and potentially reducing the need for time consuming PoD wear measurements.

关键词： Domain-specific text   Machine learning   Readability   Support vector machine   Text mining  

摘要： Text readability assessment is a challenging interdisciplinary endeavor with rich practical implications. It has long drawn the attention of researchers internationally, and the readability models since developed have been widely applied to various fields. Previous readability models have only made use of linguistic features employed for general text analysis and have not been sufficiently accurate when used to gauge domain-specific texts. In view of this, this study proposes a latent-semantic-analysis (LSA)-constructed hierarchical conceptual space that can be used to train a readability model to accurately assess domain-specific texts. Compared with a baseline reference using a traditional model, the new model improves by 13.88% to achieve 68.98% of accuracy when leveling social science texts, and by 24.61% to achieve 73.96% of accuracy when assessing natural science texts. We then combine the readability features developed for the current study with general linguistic features, and the accuracy of leveling social science texts improves by an even higher degree of 31.58% to achieve 86.68%, and that of natural science texts by 26.56% to achieve 75.91%. These results indicate that the readability features developed in this study can be used both to train a readability model for leveling domain-specific texts and also in combination with the more common linguistic features to enhance the efficacy of the model. Future research can expand the generalizability of the model by assessing texts from different fields and grade levels using the proposed method, thus enhancing the practical applications of this new method.

关键词： classification models   decision tree (DT)   GIIA sPLA(2) inhibitors   random forest (RF)   support vector machine (SVM)  

摘要： GIIA secreted phospholipase A(2) (GIIA sPLA(2)) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA(2), we built 24 classification models by three machine learning algorithms including support vector machine (SVM), decision tree (DT), and random forest (RF) based on 452 compounds. The molecules were represented by CORINA descriptors, MACCS fingerprints, and ECFP4 fingerprints, respectively. The dataset was split into a training set containing 312 compounds and a test set containing 140 compounds by Kohonen's self-organizing map (SOM) strategy and a random strategy. A recursive feature elimination (RFE) method and an information gain (IG) method were used in the selection of molecular descriptors. Three favorable performing models were obtained. They were built by SVM algorithm with CORINA descriptors (Models 1A and 2A) and ECFP4 fingerprints (Model 10A). In the prediction of test set of Model 10A, the accuracy reached 90.71%, and the Matthews correlation coefficient (MCC) values reached 0.82. In addition, the 452 inhibitors were clustered into eight subsets by K-Means algorithm for analyzing their structural features. It was found that highly active inhibitors mainly contained indole scaffold or indolizine scaffold and four side chains.