关键词： 软件安全   结合语义   机器学习   方法  

摘要： 随着计算机信息技术的不断发展,各类软件已被人们广泛应用到社会生活、生产的各个领域。但是,随着信息系统漏洞的不断增多,病毒种类的千变万化,软件安全却一直是人们感到棘手的难题。在信息技术发展的同时,近20多年新兴的机器学习也在蓬勃的发展,并且已经在很多领域得到成功的应用。本文先分析机器学习的主要策略,再分析当今软件安全的现状,最后再探索在软件安全中结合语义的机器学习方法。

关键词： machine learning   support vector machines   artificial neural networks   CART classification trees   k-nearest neighbour   large-dimensional data   cross-validation   SUPPORT VECTOR MACHINES   NEURAL-NETWORKS   ENTREPRENEURIAL INTENT   DECISION TREES   IMPACT   START   CART  

摘要： Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

关键词： image analysis   fruit detection   machine learning   young fruit   tomato   APPLE FRUITS   GREEN APPLES   RGB IMAGES   COLOR   YIELD   SEGMENTATION   FEATURES   ORCHARD   NUMBER   SHAPE  

摘要： Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detect only mature and immature fruits, although the number of young fruits is more important for the prediction of long-term fluctuations in yield. In this study, we aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches. The developed method did not require an adjustment of threshold values for fruit detection from each image because image segmentation was conducted based on classification models generated in accordance with the color, shape, texture and size of the images. The results of fruit detection in the test images showed that the developed method achieved a recall of 0.80, while the precision was 0.88. The recall values of mature, immature and young fruits were 1.00, 0.80 and 0.78, respectively.

关键词： 提升小波   支持向量机   核函数   交叉验证   肝癌和肺癌基因芯片  

摘要： 针对肿瘤的早期诊断,提出了一种基于提升小波变换的特征提取的方法,对肿瘤数据样本进行分析鉴别.该方法利用提升小波变换对190例肝癌(包括对照)和107例肺癌(包括对照)基因表达谱芯片数据进行处理后,提取信号的低频信息,经支持向量机训练学习,构造分类器模型,用于癌和非癌样本的区分甄别.实验结果表明,经提升小波变换提取的特征基因,送入分类器中能得到较高的分类率,且在支持向量机中选取线性核函数或径向基函数都能达到较好的分类效果.通过随机选取的20例基因表改谱芯片样本,对所建立的模型进行了测试,获得了很好的效果,因此,本文提出的方法对肿瘤的诊断有一定的应用意义.

关键词： 结节病   肺结核   DCT   NB   SVM  

摘要： 结节病和肺结核的临床鉴别诊断目前仍然是困难的。搜集了106例结节病和肺结核的对比资料,并筛选出对分类有意义的临床指标作为特征,将其进行必要的量化和缩放形成训练数据,然后分别用支持向量机(SVM:Support Vector Machine)、决策分类树(DCT:Decision Classification Tree)、朴素贝叶斯(NB:Nave Bayes)3种不同的方法进行训练,并用5倍交叉验证评估各种不同的模型的有效性。实验结果表明,这3种方法在识别结节病时对应的ROC曲线下的面积分别为0.978,0.96,0.690,得到的测试精度分别达到100%,96.15%,96.15%,训练精度分别为95.28%,90.57%,92.38%。用这3种方法得到的分类器对19例临床未能确诊的病患进行预测,DCT方法的预测结果与SVM方法的结果高度吻合(19例中仅1例预测结果不同),而NB方法预测结果稍差(19例中有3例与SVM预测结果不一致)。实验结果表明,3种方法中,SVM方法的分类能力和分类精度最高。临床实验结果表明,19例临床未能确诊的病患按照SVM算法预测的结果进行治疗均得到了康复。

关键词： agriculture   ASTER satellite images   object-oriented image analysis   hierarchical classification   neural networks   NEURAL-NETWORKS   LAND-COVER   LOGISTIC-REGRESSION   IRRIGATED CROPS   ALGORITHMS   ACCURACY   MATRIX   AREA  

摘要： The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.

关键词： Bayesian analysis   Statistical data interpretation   Systems biology   DIMENSIONALITY REDUCTION METHOD   CELLULAR CONTROL PROCESSES   GENE-GENE INTERACTIONS   BAYESIAN NETWORKS   SYSTEM   CANCER   IDENTIFICATION   MATHEMATICS   DISCOVERY   PATHWAYS  

摘要： In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

关键词： Landsat   ALOS/PALSAR   L-band   maximum likelihood classification   support vector machines   neural networks   random forest   topographic effects   Hyrcanian mountainous forest   Iran   TOPOGRAPHIC CORRECTION METHODS   SUPPORT VECTOR MACHINES   PALSAR L-BAND   COVER CLASSIFICATION   ALOS PALSAR   SUPERVISED CLASSIFICATION   ABOVEGROUND BIOMASS   TROPICAL FOREST   TM   LANDSCAPE  

摘要： Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (i.e., maximum likelihood classification (MLC)) as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA) = 65%). This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%). However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%). The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%). However, this shows a slight improvement compared to the ETM+ classification (OA = 84%). The machine learning classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data.

关键词： 电子商务交易量   站内搜索数据   商品分类  

摘要： 对搜索型商品(3C)、体验型商品(零食)、中间型商品(女装)分别建立基于淘宝网站内搜索数据对商品在线交易量的预测模型。研究结果表明,搜索数据对于搜索型商品的在线交易量具有最佳的预测效果,而对体验型商品的预测效果较差。

关键词： 数据挖掘   分类预测   敏感性   特异性  

摘要： 数据挖掘技术己经引起了信息产业界的广泛关注。分类预测是其中一个主要的研究方向,有着广泛的应用价值。对数据挖掘中的三种分类预测挖掘算法(C5.0、BP-人工神经网络和TAN贝叶斯网络)进行了研究和探讨,并通过一个实际例子对三种算法做了分析和比较,三种算法的精确度分别为87.26%、85.71%和88.7%。TAN贝叶斯网络的精确度和敏感性均最高,C5.0算法的特异性最好。