关键词： Terrorism  

摘要： In this paper, we employ machine learning methods to solve the problem of countering terrorism and extremism by using information from the Internet. This problem involves retrieving electronic messages, documents, and web resources that potentially contain information of terrorist or extremist nature, identifying the structure of user groups and online communities that disseminate this information, monitoring and modeling information flows in these communities, as well as assessing threats and predicting risks based on monitoring results. We propose some original language-independent algorithms for pattern-based information retrieval, thematic modeling, and prediction of message flow characteristics, as well as assessment and prediction of potential risk coming from members of online communities by using data on the structure of relations in these communities, which makes it possible to detect potentially dangerous users even without full access to the content they distribute, e.g., through private channels and chat rooms.

关键词： Auctions   bidding   regularized linear regression   forecasting  

摘要： It is well-known that empirical analysis suffers from multicollinearity and high dimensionality. In particular, this is much more severe in an empirical study of itemized bids in highway procurement auctions. To overcome this obstacle, this article employs the regularized linear regression for the estimation of a more precise interval for project winning bids. The approach is put to the test using empirical data of highway procurement auctions in Vermont. In our empirical analysis, we first choose a set of crucial tasks that determine a bidder's bid amounts by using the random forest variable selection method. Given the selected tasks, project bid forecasting is conducted. We compare our proposed methodology with the least square linear model based on the bias and the standard root mean square error of the bid estimates. There is evidence supporting that the suggested approach provides superior forecasts for an interval of winning bids over the competing model. As far as we know, this article is the first attempt to provide reference bids of highway construction contracts.

关键词： 数据挖掘   疾病分类   医疗大数据   决策树算法  

摘要： 随着医院信息系统(HIS)在全国各大医院的推广和应用,会有大量和病人相关的临床数据每天在医院中记录,而这些真实的数据随着规模积累的增加,存在着对病人和医生来说潜在、有价值的信息作用也会越来越大。本文会根据医院信息系统(HIS),通过决策树算法,针对疾病诊断的影响因子进行预测分析,得出一个有效帮助诊断的分析模型,通过一段时间论证分析,该模型会在临床应用中有一定的指导作用。

关键词： intensive care unit   ventilator-associated pneumonia   artificial neural network   support vector machine   ensemble neural networks   cross-validation  

摘要： One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients' data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models' performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection, with 0.9479 +/- 0.0135 and 0.8686 +/- 0.0422 accuracies, 0.9714 +/- 0.0131, 0.9250 +/- 0.0423 sensitivities, and 0.9288 +/- 0.0306, 0.8639 +/- 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with Pseudomonas aeruginosa infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 +/- 0.0058 and 0.9410 +/- 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in Pseudomonas aeruginosa infection or other diseases.

关键词： 人脸识别   机器学习   像素特征   模型训练  

摘要： 随着时代的进步,人类发展的脚步愈来愈向着先进、科技的方面前行,AI的出现很好地说明了这一特点。AI技术运用在人类生活中的方方面面,而离人们生活最近的则为人脸识别方面技术的应用。本文首先回顾人脸识别的研究进展,据不同时间段所进行的研究历程进行分析;随后介绍人脸识别工作原理以及分析人脸识别目前存在的问题并据其弊端提出一定的改进措施;最后并对人脸识别的发展进行展望。

关键词： 机器学习   竞争性自适应重加权   主成分分析   偏最小二乘   支持向量机  

摘要： 为实现对过量使用1-MCP化学保鲜剂猕猴桃快速、无损检测,提出高光谱技术结合机器学习建立识别模型的检测方法。首先对空白猕猴桃和过量化学保鲜猕猴桃在865.11～1 711.71 nm范围内进行高光谱数据采集。然后选用标准正态变量变换方法预处理原始光谱数据以去除噪声,采用波段比算法增强图像,数学形态学算法提取感兴趣区域,进而计算光谱平均值。最后采用主成份分析(PCA)、竞争性自适应加权(CARS)方法对全光谱数据(FS)进行特征提取,去除干扰项;以PCA和CARS提取的特征量和FS数据作为输入,结合偏最小二乘(PLS)和支持向量机(SVM)建立12个识别模型。试验结果表明,基于PLS和SVM建立的识别模型均能够有效检测过量化学保鲜猕猴桃,其中CARS-SVM模型性能最好,平均正确识别率达100%,运行速度最快,仅为0.015 348 s,满足工程实践中实时性高的要求,为快速、无损检测猕猴桃果品安全提供理论支撑。

关键词： Additive   manufacturing   3D   printing   Neural   network   Machine   learning   Algorithm  

摘要： Additive manufacturing(AM),also known as three-dimensional printing,is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive ***,AM processing parameters are difficult to tune,since they can exert a huge impact on the printed microstructure and on the performance of the subsequent *** is a difficult task to build a process-structure-property-performance(PSPP)relationship for AM using traditional numerical and analytical ***,the machine learning(ML)method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical *** ML algorithms,the neural network(NN)is the most widely used model due to the large dataset that is currently available,strong computational power,and sophisticated algorithm *** paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain,including model design,in situ monitoring,and quality *** challenges in applying NNs to AM and potential solutions for these problems are then ***,future trends are proposed in order to provide an overall discussion of this interdisciplinary area.

关键词： 信号处理方法   智能化处理   大数据   征稿通知   机器学习方法   信息技术   社会生活   学习能力  

摘要： 随着信息技术的高速发展,在现代社会生活中每天都有着各式各样的大量数据产生。大数据时代已然来临,而如何合理地存储、处理以及利用这些数据就成为了受到广泛关注的焦点性问题。一方面,大数据更加依赖具有学习能力、推理能力和优化能力的智能信号处理方法;另一方面,大数据为智能信号处理方法的发展提供了条件。大数据时代的智能信号处理是以信号模型为驱动,以大样本为基础,通过机器学习方法提取信号特征,进而实现对信号的智能化处理和利用。本次征文的主要目的是给大数据时代下的智能信号处理提供新的视角和思路。

关键词： 小推力   燃料最优   快速估计   机器学习  

摘要： 深空探测任务设计初段往往需要求解复杂的全局优化问题。小推力轨迹的设计与优化问题精确求解较为复杂,求解速度较慢。由于计算能力与时间要求,不可能在全局优化的过程中对每一个方案都进行精确的小推力数值求解,所以在全局优化阶段需要对小推力转移进行快速准确地估计。采用机器学习的方法,对燃料最优小推力转移的燃料消耗进行了估计,其结果明显优于目前最为常用的Lambert估计方法。根据轨道描述方法的不同以及是否带有Lambert估计特征,采用不同的特征组合进行机器学习,分析结果发现带有Lambert估计特征的春分点轨道根数的特征组合为较好的机器学习特征组合。可为未来深空探测任务轨道设计提供参考。

关键词： 智能机器人   机器学习   传感器   智能决策  

摘要： 人工智能技术的发展程度反映了中国高新科技领域的实力,同时也是未来工业革命的关键技术。智能机器人又是人工智能领域发展的重要一环,该方向全面揭示了当前人工智能领域的发展程度,并详细展示人工智能和智能机器人之间的紧密关系。本文将简略说明智能机器人的发展情况,以及智能机器人的构成部分,阐述基于机器学习方法的智能机器人的使用原理,最后将对智能机器人的应用方向和未来发展等几个部分展开分析。