关键词： Slow-wave sleep   Slow waves   Automatic methods   Artificial neural network  

摘要： Background: During slow-wave sleep the electroencephalographic (EEG) and local field potential (LFP) recordings reveal the presence of large amplitude slow waves. Systematic extraction of individual slow waves is not trivial. New method: In this study, we used the neural network pattern recognition to detect individual slow waves in LFP recorded from mice as well as other commonly used methods that are based on fast frequencies modulation, amplitude, or duration. Results: The number and quality of events detected as slow waves depended on the chosen method of detection, level of thresholds, or on combination of methods. Each individual method yields some false-positive and false-negative detections. Typically, the fast frequency-method has a higher false discovery rate, but almost no missing waves;amplitude-based method has relatively high false-positive and false-negative rates;duration-based method has low false-negative rates;neural network pattern recognition approach has the lowest false-positive rate among individual methods, often rejecting waves that were falsely detected by other approaches. Combining all 4 detection methods practically eliminated false-positive errors, but a large number of slow waves remained undetected. Conclusions: The use of a particular method of slow wave detection needs to be adjusted to the objectives of a given study: to detect all slow waves, but also numerous false positives can be achieved using the fast frequency approach. Neural network pattern recognition method alone can detect slow waves with the lowest false-positive rate, that can be further minimized with the use of combination of other methods.

关键词： 设计模式   设计模式挖掘   子图同构   机器学习  

摘要： 从源码中挖掘设计模式实例具有重要意义,它能够提升软件系统的可理解性和可维护性。基于机器学习方法,提出了一个设计模式挖掘方法。其中,使用类关系图表示软件系统和设计模式,采用子图匹配的方法从软件系统中提取出候选设计模式实例,过滤了大量非设计模式实例组合,提高了设计模式挖掘效率;提出了度量指标,将软件系统和设计模式向量化,采用机器学习的方法对候选设计模式实例进行分类,提高了设计模式挖掘的准确率。实验结果证明此方法效率和有效性上得到了一定的提升。

关键词： 机器学习   原发性肝癌   logistic回归   支持向量机   预后预测  

摘要： 目的:使用机器学习方法对原发性肝癌患者生存和复发时间进行预测,并比较不同机器学习方法的预测效果。方法:使用Logistic回归、随机森林、SVM、C5.0决策树、神经网络、Bagging算法和Adaboost算法构建患者肝切除手术后3年无瘤生存时间和3年总生存时间的预后预测模型。结果:通过Logistic回归模型找到了影响患者生存和复发的影响因素,并且支持向量机和随机森林有较好的效果,测试集上的AUC为0.7122和0.7103。结论:机器学习方法相较于Logistic回归模型有较好的预测效果,但是Logistic回归模型有较好的可解释性。在分析实际问题时,可以采用二者结合的方法。

关键词： 中医治未病   贝叶斯算法   病例大数据   机器学习  

摘要： 本文采用贝叶斯网络对"未病人群"健康检查数据进行及时准确的分析,依据病例大数据,构建机器学习模型,推进健康医疗大数据的应用,从而科学的评估自身健康状况及存在风险,合理的管理健康指标,并及时进行健康干预。本研究提出的方法在一定程度上提高了量化评估结果,缩短建模时间,使临床医生针对特定人群制定相应策略;提高医生工作效率,使新型中医诊疗方法不断继承、创新、发展。

关键词： 慢性阻塞性肺疾病   k-最近相邻法   SVM   准确度  

摘要： 目的:采用基于机器学习的分类判断算法,建立慢性阻塞性肺疾病(以下简称"慢阻肺")分期模型,提高慢阻肺诊断和分期的准确度。方法:选择确诊慢阻肺住院患者2 504例,以国际GOLD分期为依据,收集与慢阻肺分期密切相关的临床特征参数指标,对参数进行筛选,参照医院的临床确诊结果,采用机器学习方法(k-最近相邻法、SVM)训练并测试慢阻肺的分期模型。结果与结论:数据为不平衡数据,虽采用分层比例抽样,但针对此类数据SVM的准确率更高为85.26%。说明机器学习提供的模型能为慢阻肺分期提供较准确的分类依据。

关键词： 机器学习   脑电图   癫痫分类预测   特征提取  

摘要： 目前,癫痫作为世界上最为常见的神经系统疾病之一,迫切需要有效的方法来进行诊治。脑电图检查对于诊断癫痫病有着决定性的作用,最近几十年来,一些新模型新算法的引入给癫痫分类带来了较高的准确性,尤其是深度学习神经网络算法,如CNN、LSTM、RNN等。总体来说算法大致分两大类,一类是手动提取特征并用分类器进行分类,一类是让机器自行学习特征并分类,也就是神经网络算法。介绍了一些机器学习方法在癫痫分类预测上的实现和应用,以及针对脑电波的一些预处理和特征提取方法,如脑电波的特征提取和选择,最后对面临的问题和发展状况进行总结和展望。

关键词： 神经网络模型   攻击方法   演化算法   人工智能技术   机器学习方法   黑箱   自然语言处理   学习模型  

摘要： 一、相关介绍目前,人工智能技术发展火热,其中离不开神经网络的快速崛起。作为目前在计算机视觉、强化学习、自然语言处理、语音识别等领域效果最佳的机器学习模型,神经网络以其强大的特征提取能力、数据拟合能力而著称。相比传统机器学习方法,神经网络不需要手工选择特征,其自组织、自学习的特点,更能够帮助模型捕捉到各类高维数据中隐含着的潜在规律。并且,越来越多的智能应用开始逐渐落地,例如智能驾驶系统中的视觉感知、人脸识别应用、智慧城市等等。值得注意的是,最近越来越多的研究证明,神经网络模型并非是一个足够鲁棒、安全的模型。

关键词： Human mobility patterns   Mobile phones   Machine learning   Data mining  

摘要： Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis.

关键词： Radiomics   Isocitrate dehydrogenase   Diffuse glioma   Magnetic resonance imaging   Machine learning  

摘要： Purpose Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas. Methods Eight classical machine learning methods were evaluated in terms of their stability and performance for pre-operative IDH genotype prediction. A total of 126 patients were enrolled for analysis. Overall, 704 radiomic features extracted from the pre-operative MRI images were analyzed. The patients were randomly assigned to either the training set or the validation set at a ratio of 2:1. Feature selection and classification model training were done using the training set, whereas the predictive performance and stability of the model were independently assessed using the validation set. Results Random Forest (RF) showed high predictive performance (accuracy 0.885 +/- 0.041, AUC 0.931 +/- 0.036), whereas neural network (NN) (accuracy 0.829 +/- 0.064, AUC 0.878 +/- 0.052) and flexible discriminant analysis (FDA) (accuracy 0.851 +/- 0.049, AUC 0.875 +/- 0.057) displayed low predictive performance. With regard to stability, RF also showed high robustness against data perturbation (relative standard deviations, RSD 3.87%). Conclusions RF is a promising machine learning method in predicting IDH genotype. Development of an accurate and reliable model can assist in the initial diagnostic evaluation and treatment planning for diffuse glioma patients.

关键词： CyberManufacturing systems   Security   Additive manufacturing   Machine learning  

摘要： CyberManufacturing system (CMS) is a vision for future manufacturing systems. The concept delineates a vision of advanced manufacturing system integrated with technologies such as Internet of Things, Cloud Computing, Sensors Network and Machine Learning. As a result, cyber-attacks such as Stuxnet attack will increase along with growing simultaneous connectivity. Now, cyber-physical attacks are new and unique risks to CMSs and modern cyber security countermeasure is not enough. To learn this new vulnerability, the cyber-physical attacks is defined via a taxonomy under the vision of CMS. Machine learning on physical data is studied for detecting cyber-physical attacks. Two examples were developed with simulation and experiments: 3D printing malicious attack and CNC milling machine malicious attack. By implementing machine learning methods in physical data, the anomaly detection algorithm reached 96.1% accuracy in detecting cyber-physical attacks in 3D printing process;random forest algorithm reached on average 91.1% accuracy in detecting cyber-physical attacks in CNC milling process.