A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

超细水泥封堵技术

注水开发后期日益出现的地质问题及油套管的严重损坏,制约了油田的开发效果。针对河南油田注水开发中存在的上述问题,通过对超细水泥堵剂配方、堵剂性能、适应性等方面的研究,可实现对油井的高含水层、注水井的停注无用层、油水井套管外窜槽、穿孔、丝扣处的漏失等方面的封堵,取得了显著效果。自1998年8月-2001年12月在河南油田应用61井次,有效率85.3%,其中油井36井次,年度累计增油1.441×104t,降水7.6123×104m3;水井封堵25井次,累计增注14.32×104m3,控制无效注水16.95×104m3,创效益1800多万元,投入产出比达1:11.36,具有广泛的推广应用价值。     

Employing optimized combinations of one-class classifiers for automated currency validation

Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution.     

籼米淀粉超微粉体的制备及其性质

研究了籼米淀粉超微粉体的一种新的制备方法 ,通过正交实验确定了酶处理淀粉的最佳条件 ,考察了淀粉的溶解度和膨润力。结果表明 ,酶处理淀粉的最佳工艺参数为 :水解温度４５℃ ,时间为２５ｈ ,酶用量为水解淀粉的４５%。随着粒径的减小 ,淀粉的溶解度增大 ,膨润力下降。     

超细粉碎技术现状及发展趋势

综述了国内外超细粉碎技术及设备的现状、进展,指出了其发展趋势,从工作原理、结构特点等方面详细介绍了超细粉碎典型设备,同时又指出了近年来国内外在此领域中的新进展,基于现存问题指出了今后超细粉碎研究的方向。     

Successive pattern classification based on test feature classifier and its application to defect image classification

A novel successive learning algorithm based on a Test Feature Classifier is proposed for efficient handling of sequentially provided training data. The fundamental characteristics of the successive learning are considered. In the learning, after recognition of a set of unknown data by a classifier, they are fed into the classifier in order to obtain a modified performance. An efficient algorithm is proposed for the incremental definition of prime tests which are irreducible combinations of features and capable of classifying training patterns into correct classes. Four strategies for addition of training patterns are investigated with respect to their precision and performance using real pattern data. A real-world problem of classification of defects on wafer images has been dealt with by the proposed classifier, obtaining excellent performance even through efficient addition strategies.     

韶钢第五轧钢厂自动分槽器的设计与应用

介绍了韶钢第五轧钢厂自动分槽器的设计与应用．     

超细氧化锌粉体的制备及尺寸控制

根据气相化学反应法 (CAD)制备超细粉的原理 ,从低品位氧化锌矿中采用火法冶金方法直接制备超细氧化锌粉 ,对所得氧化锌的粒度尺寸、微观结构和粒度分布进行测试 ;考察了冷却强度以及收集装置对超细氧化锌粒度及粒度分布的影响 ;讨论了氧化锌粉体团聚的原因及对策、试验条件对氧化锌尺寸的控制等     

涤纶超细仿麂皮织物染整实践

介绍了涤纶超细仿麂皮织物的染整加工工艺，重点分析了前处理、染色等关键工序，并给出了具体工艺参数。     

Class normalization in centroid-based text categorization

Centroid-based categorization is one of the most popular algorithms in text classification. In this approach, normalization is an important factor to improve performance of a centroid-based classifier when documents in text collection have quite different sizes and/or the numbers of documents in classes are unbalanced. In the past, most researchers applied document normalization, e.g., document-length normalization, while some consider a simple kind of class normalization, so-called class-length normalization, to solve the unbalancedness problem. However, there is no intensive work that clarifies how these normalizations affect classification performance and whether there are any other useful normalizations. The purpose of this paper is three folds; (1) to investigate the effectiveness of document- and class-length normalizations on several data sets, (2) to evaluate a number of commonly used normalization functions and (3) to introduce a new type of class normalization, called term-length normalization, which exploits term distribution among documents in the class. The experimental results show that a classifier with weight-merge-normalize approach (class-length normalization) performs better than one with weight-normalize-merge approach (document-length normalization) for the data sets with unbalanced numbers of documents in classes, and is quite competitive for those with balanced numbers of documents. For normalization functions, the normalization based on term weighting performs better than the others on average. For term-length normalization, it is useful for improving classification accuracy. The combination of term- and class-length normalizations outperforms pure class-length normalization and pure term-length normalization as well as unnormalization with the gaps of 4.29%, 11.50%, 30.09%, respectively.