关键词： 有效波高   风速   支持向量回归   粒子群优化  

摘要： 基于支持向量回归(SVR)方法,建立了渤海海域近岸海浪有效波高短期预测模型,并设计了多组风浪信息组合输入方案,开展了有效波高预测敏感性试验。研究发现:综合考虑当前风浪信息作为模型的输入,对3 h和6 h有效波高预测具有较高的预报技巧,但随着预测时效的延长其预测准确性迅速降低;若此时引入未来预测风速信息作为模型输入,则可极大提高对12 h和24 h有效波高的预测能力;此外,若输入信息与预测对象之间不存在显著相关,多个信息的输入对有效波高预测效果提高无显著作用。建立的机器学习模型对小样本数据集具有良好的适应能力,能够有效解决海浪预报中的非线性问题,可为近岸海浪有效波高短期预测提供合理的技术参考。

关键词： 人工智能   深度学习   机器学习方法   科技人才   计算机领域   科技浪潮   机器实现  

摘要： "人工智能"一词自1956年确定下来,开始飞速发展。它的出现是计算机领域的一个大突破,同时也在很多其它领域起着作用。而当前,机器学习方法最受人瞩目的就是深度学习,它推动人工智能不断前进。近年来,人工智能逐渐掀起了科技浪潮,无数科技人才开始关注、研究这个领域。人工智能即用机器实现人工。

关键词： 决策树   K近邻   朴素贝叶斯   随机森林   支持向量机   前列腺癌   预测诊断   PI-RADS v2.1  

摘要： 目的建立联合多参数MRI前列腺影像报告与数据系统(PI-RADS)v2.1评分及临床数据的决策树、K近邻、朴素贝叶斯、随机森林、支持向量机5种机器学习模型,评价上述模型对前列腺癌的诊断价值。方法回顾性分析在本院接受MR检查并获得的病理结果的242例患者。将PI-RADS v2.1评分、年龄、总前列腺特异抗原、游离前列腺特异抗原、游离前列腺特异抗原比值、体积、前列腺特异抗原密度录入5种机器学习模型进行诊断。通过F1值及ROC曲线评价机器学习模型的诊断价值并且计算特征变量所占比重大小。结果随机森林模型诊断前列腺癌ROC的AUC最大(0.93),决策树及朴素贝叶斯模型AUC也较高(0.86、0.87),支持向量机最差(0.55);F1值最高的为随机森林模型,其次依序为朴素贝叶斯、决策树、K近邻,支持向量机模型最小。通过随机森林和决策树模型计算各特征变量重要性,PI-RADS评分均占比例最大,其次为前列腺特异抗原密度、前列腺体积,年龄对模型分类贡献最低。结论随机森林、朴素贝叶斯、决策树分类模型用于预测诊断前列腺癌具有更好的效果。随机森林方法在5种机器学习模型中最优,且PI-RADS v2.1及前列腺密度变量的特征重要性表现更明显。

关键词： 影像组学   头颈癌   机器学习   风险预测  

摘要： 影像组学是一种挖掘医学影像特征的学科,它可从标准的医学影像中提取高通量定量数据特征来描述肿瘤信息,并为临床实践提供决策支持。为了预测肿瘤转移风险,分析了来自三个机构的197例头颈癌患者接受治疗前的FDGPET和CT信息,使用影像组学相关方法对每例患者提取了100多个放射学特征,并以此为特征来预测其局部复发(LR)和远处转移风险(DM)。在预测模型构建时,使用机器学习算法,以其中两个机构提供的数据作为训练集,剩余机构的数据作为独立测试集。在独立测试集上的测试结果显示:预测局部复发时使用支持向量机(SVM),其精确度达到了79%;预测远处转移时使用随机森林(RF),其精确度达到了78%。使用该方法可以对头颈癌进行辅助诊断并对患者进行更好的个性化治疗。

关键词： 流量异常检测   数据预处理   数据约减   层次聚类   机器学习方法  

摘要： 随着大数据时代的到来,网络安全中攻击总流量大幅上升,通过异常流量检测发现网络中的恶意流量成为当前亟需解决的问题.目前工业中使用的异常流量检测设备主要采用统计分析方法或简单的机器学习方法,存在网络流量数据量巨大、冗余的正常数据量较多、精准率较低、误报率较高等问题.针对此类问题,提出了一种作用于数据处理阶段的基于层次聚类的流量异常检测方法.该方法先使用层次聚类算法达到数据约减的目的,然后基于7种不同的机器学习算法构建了基于层次聚类的异常流量模型.实验结果表明,该方法在DARPA数据集上对异常行为的检测精准率可达到99%,召回率可达到99%.同时,经过数据约减后,仍能保持90%以上的精准率,极大地提升了检测效率.

关键词： Machine learning   Classification   Quick clays   Sensitive clays  

摘要： Geotechnical classification is vital for site characterization and geotechnical *** tests such as the cone penetration test with pore water pressure measurement(CPTu)are widespread because they represent a faster and cheaper alternative for sample recovery and ***,classification schemes based on CPTu measurements are fairly generic because they represent a wide variety of soil conditions and,occasionally,they may fail when used in special soil types like sensitive or quick *** and highly sensitive clay soils in Norway have unique conditions that make them difficult to be identified through general classification ***,new approaches to address this task are *** following study applies machine learning methods such as logistic regression,Naive Bayes,and hidden Markov models to classify quick and highly sensitive clays at two sites in Norway based on normalized CPTu *** showed a considerable increase in the classification accuracy despite limited training sets.

关键词： 血压   预测   光电容积脉搏波   模型  

摘要： 血压作为反映人体健康状态的指标,可准确被测量有利于临床进一步诊断,现主要使用机器学习的方法对血压预测系统进行研究,对光电容积脉搏波(PPG)信号进行去噪、特征提取、特征选择、特征归一化及离群点分析,并将昆士兰大学的开源数据库中的血压数据作为分析的数据源,用于验证研究提出的算法有效性,并建立血压预测离线模型.

关键词： Cornish-Fisher expansion   feature selection   nearest shrunken centroid   sparse PCA  

摘要： Type 2 diabetes mellitus (T2DM) affects millions of people through its life-altering complications. Worldwide, 3.4 million people die of diabetes annually. Studying the effect of genetic polymorphism on T2DM has been plagued by the available sample size. A 2016 Nature Reviews article summarized that the accuracy of predicting future type 2 diabetes from genetic polymorphism is very low at the population level. Innumerable associations between genes, environmental factors, and type 2 diabetes remain to be discovered. This research presents a method to identify subtle effects of genetic variants using whole genome sequencing data and improve prediction accuracy of T2DM at the population level. To achieve this, a new feature selection procedure and a classifier are proposed. The method involves (a) first applying sparse principal component analysis to genotype data to obtain orthogonal features;(b) building a new classifier using single nucleotide polymorphism (SNP)-specific regularization parameters to reduce the false positive rate of feature selection;(c) verifying feature relevance through penalized logistic regression. After application to a dataset containing 625 597 SNPs and 23 environmental variables from each of 3326 humans, the method identified 271 genetic variants with subtle effects on T2DM prediction. These variants led to greatly improved prediction accuracy for new patients at the population level. The proposed method also has the advantage of computational efficiency, over 15 times faster than random forest and extreme gradient boosting (XGBoost) classifiers, and thus provides a promising tool for large-scale genome-wide association studies.

关键词： Amphibole formula   Amphibole nomenclature   Machine learning   Principal components regression   Electron microprobe analysis  

摘要： Wepresent a new method for calculating amphibole formula from routine electron microprobe analysis (EMPA) data by applying a principal components regression (PCR)-based machine learning algorithm on reference amphibole data. The reference amphibole data collected from literature are grouped in two datasets, for Li-free and Li-bearing amphiboles respectively, which include Fe2+, Fe3+, OH contents and the ion site assignments determined by single crystal structure refinement. We established two PCR models, for Li-free and Li-bearing amphiboles respectively, by the 10-fold cross validation of training datasets and evaluated by independent test datasets. The results show that our models can successfully reproduce the reference data for most ions with an error less than +/- 0.01 atom per formula unit (apfu), for Fe3+ within an error less than +/- 0.2 apfu and for (OH)-O-W and O-W(2-) with errors less than +/- 0.3 apfu. The error in estimated Fe3+/Sigma Fe ratio shows a rough negative dependence on FeOT content (total iron expressed as FeO), ranging within +/- 0.3 for amphiboles with FeOT >= 5 wt% and within +/- 0.2 for amphiboles with FeOT >= 10 wt%. Our models are applicable to both (W)(OH, F, Cl)-dominant and O-W-dominant amphiboles. It is notable that this method is not suitable for calculating mineral formula of amphiboles that have been affected by deprotonation as a result of secondary oxidation, but it could offer an estimation of initial (OH)-O-W prior to the post-formation oxidation. A user-friendly Excel worksheet is provided with two independent PCR models for calculating the formula of Li-free amphibole and Li-bearing amphibole, respectively. An automatic nomenclature function is also provided according to the nomenclature criteria of the 2012 International Mineralogical Association (IMA) report. (C) 2020 Elsevier B.V. All rights reserved.