关键词： 主成分分析法   过采样算法   贫困生   XGBoost模型   分类预测  

摘要： 针对高校贫困生认定工作中存在的问题,利用校园一卡通数据,综合学生消费和生活规律,结合XGBoost(Extreme Gradient Boosting)模型和主成分分析法、过采样算法,建立高校贫困生的分类预测方法。该方法在贫困生分类预测中的准确率较高。实验结果证明,采用XGBoost模型比其他模型预测准确率更高,为我国高校贫困学生的评定标准提供重要依据,保证了贫困学生认定工作的公正性。

关键词： Downward shortwave radiation   Machine learning methods   AVHRR   GBRT  

摘要： Downward shortwave radiation (DSR) is one of the major driving forces of climate system. Knowledge of the Earth's radiation budget is essential for improving our understanding of the Earth's climate. Therefore, accurate estimation of DSR has great significance. Satellite remote sensing is a practical way to derive DSR with high spatial resolution and coverage. In this study, four machine learning methods, including gradient boosting regression tree (GBRT), random forest (RF), multivariate adaptive regression spline (MARS), and artificial neural network (ANN), were applied to estimate DSR at a spatial resolution of 5 km and a temporal resolution of 1 day using Advanced Very High Resolution Radiometer (AVHRR) data. The DSR estimates based on four machine learning methods were evaluated using ground measurements at 96 sites over China. The measurements were collected from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) from 2001 to 2003. The evaluation results showed that the GBRT method performed best at both daily and monthly time scales under both clear and cloudy sky conditions. The validation results at the daily time scale showed an overall root mean square error (RMSE) of 30.34 W m(-2) and an R value of 0.90 under clear sky conditions, whereas these values were 42.03 W m(-2) and 0.86, respectively, under cloudy sky conditions. The DSR estimates had an overall RMSE value of 16.93 W m(-2) and an R value of 0.97 at the monthly time scale. The Clouds and Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data sets were also used for comparison with the DSR estimates based on the GBRT method. The DSR estimates based on the GBRT method exhibited similar spatial distributions with those of the CERES-EBAF DSR product. Moreover, the DSR estimates based on the GBRT method did not show a clear overestimation tendency, as the CERES-EBAF DSR product did, at the CDC/CMA sites.

关键词： direct horizontal irradiance   physically-based model   machine learning method   estimation   China  

摘要： Accurate estimation of direct horizontal irradiance (DHI) is a prerequisite for the design and location of concentrated solar power thermal systems. Previous studies have shown that DHI observation stations are too sparsely distributed to meet requirements, as a result of the high construction and maintenance costs of observation platforms. Satellite retrieval and reanalysis have been widely used for estimating DHI, but their accuracy needs to be further improved. In addition, numerous modelling techniques have been used for this purpose worldwide. In this study, we apply five machine learning methods: back propagation neural networks (BP), general regression neural networks (GRNN), genetic algorithm (Genetic), M5 model tree (M5Tree), multivariate adaptive regression splines (MARS);and a physically based model, Yang's hybrid model (YHM). Daily meteorological variables, including air temperature (T), relative humidity (RH), surface pressure (SP), and sunshine duration (SD) were obtained from 839 China Meteorological Administration (CMA) stations in different climatic zones across China and were used as data inputs for the six models. DHI observations at 16 CMA radiation stations were used to validate their accuracy. The results indicate that the capability of M5Tree was superior to BP, GRNN, Genetic, MARS and YHM, with the lowest values of daily root mean square (RMSE) of 1.989 MJ m(-2)day(-1), and the highest correlation coefficient (R = 0.956), respectively. Then, monthly and annual mean DHI during 1960-2016 were calculated to reveal the spatiotemporal variation of DHI across China, using daily meteorological data based on the M5tree model. The results indicated a significantly decreasing trend with a rate of -0.019 MJ m(-2)during 1960-2016, and the monthly and annual DHI values of the Tibetan Plateau are the highest, while whereas the lowest values occur in the southeastern part of the Yunnan-Guizhou Plateau, the Sichuan Basin and most of the southern Yangtze Rive

关键词： Machine learning   Text mining   Biomedical effects of nanomaterials   Natural language processing   PubMed database  

摘要： Recently, the studies on biomedical effects of nanomaterials have already achieved many progresses tightly relating to human health. But the large amounts of research achievements in nanomaterial journals bomb too massive data to clarify the research highlight prediction. Fortunately, automatic text mining methods can complete the extracting information from a large set of documents efficiently by machining learning methods. We used both Naive Bayes and K-means clustering algorithms on manually labeled research data sets. It is 88.62% by the Naive Bayes algorithm classification result of 5-folds cross validation on sampled libraries. By applying the optimized Naive Bayes classification model, we made the research highlight trend prediction based on research achievements of biomedical effects of nanomaterials in 22 cutting edge nanomaterial journals including 350,000 original literatures in period from 2000 to 2017. The data mining clarified the polymer nanomaterial is the most researched nanomaterial but with a decreasing trend. The research interests of metallic and carbon based nanomaterial follow the polymer one, and possess increasing trend. We could predict that the research highlight trend on biomedical effects of nanomaterials is focused on polymer, metallic and carbon based material systems in the near future. (C) 2018 Published by Elsevier B.V.

关键词： machine learning   nursing informatics   Omaha System   public health nursing  

摘要： Background Public health nurses (PHNs) engage in home visiting services and documentation of care services for at-risk clients. To increase efficiency and decrease documentation burden, it would be useful for PHNs to identify critical data elements most associated with patient care priorities and outcomes. Machine learning techniques can aid in retrospective identification of critical data elements. Objective We used two different machine learning feature selection techniques of minimum redundancy-maximum relevance (mRMR) and LASSO (least absolute shrinkage and selection operator) and elastic net regularized generalized linear model (glmnet in R). Methods We demonstrated application of these techniques on the Omaha System database of 205 data elements (features) with a cohort of 756 family home visiting clients who received at least one visit from PHNs in a local Midwest public health agency. A dichotomous maternal risk index served as the outcome for feature selection. Application Using mRMR as a feature selection technique, out of 206 features, 50 features were selected with scores greater than zero, and generalized linear model applied on the 50 features achieved highest accuracy of 86.2% on a held-out test set. Using glmnet as a feature selection technique and obtaining feature importance, 63 features had importance scores greater than zero, and generalized linear model applied on them achieved the highest accuracy of 95.5% on a held-out test set. Discussion Feature selection techniques show promise toward reducing public health nursing documentation burden by identifying the most critical data elements needed to predict risk status. Further studies to refine the process of feature selection can aid in informing PHNs' focus on client-specific and targeted interventions in the delivery of care.

关键词： collagen disorganization   multiphoton microscopy   second harmonic generation   autofluorescence   machine learning  

摘要： Pathogenesis of many diseases is associated with changes in the collagen spatial structure. Traditionally, the 3D structure of collagen in biological tissues is analyzed using histochemistry, immunohistochemistry, magnetic resonance imaging, and X-radiography. At present, multiphoton microscopy (MPM) is commonly used to study the structure of biological tissues. MPM has a high spatial resolution comparable to histological analysis and can be used for direct visualization of collagen spatial structure. Because of a large volume of data accumulated due to the high spatial resolution of MPM, special analytical methods should be used for identification of informative features in the images and quantitative evaluation of relationship between these features and pathological processes resulting in the destruction of collagen structure. Here, we describe current approaches and achievements in the identification of informative features in the MPM images of collagen in biological tissues, as well as the development on this basis of algorithms for computer-aided classification of collagen structures using machine learning as a type of artificial intelligence methods.

关键词： Strength   Mixture design   Predictive modeling   Machine learning   Regression analysis  

摘要： The use of statistical and machine learning approaches to predict the compressive strength of concrete based on mixture proportions, on account of its industrial importance, has received significant attention. However, previous studies have been limited to small, laboratory-produced data sets. This study presents the first analysis of a large data set (> 10,000 observations) of measured compressive strengths from actual (job-site) mixtures and their corresponding actual mixture proportions. Predictive models are applied to examine relationships between the mixture design variables and strength, and to thereby develop an estimate of the (28-day) strength. These models are also applied to a laboratory-based data set of strength measurements published by Yeh et al. (1998) and the performance of the models across both data sets is compared. Furthermore, to illustrate the value of such models beyond simply strength prediction, they are used to design optimal concrete mixtures that minimize cost and embodied CO2 impact while satisfying imposed target strengths.

关键词： Machine learning   Image and video   Pedestrian detection   Anomaly detection  

摘要： Pedestrian detection and abnormal behavior detection is the computer for a given image and video, to determine whether there are pedestrians and their behavior is normal. Pedestrian detection is the basis and premise of pedestrian tracking, behavior analysis, gait analysis, pedestrian identity recognition and so on. A good pedestrian detection algorithm can provide strong support and guarantee for the latter. The overall goal of this project is to learn different data mining methods and try to improve the detection accuracy of video crowd machine abnormal behavior. Aiming at the shortage of user behavior anomaly detection model proposed by Lane et al., a new IDS anomaly detection model is proposed. This model improves the representation of user behavior patterns and behavior profiles and adopts a new similarity assignment method. Experiments based on Unix user shell command data show that the detection model proposed in this paper has higher detection performance.

关键词： Computational fluid dynamics   Turbulent flows   Machine learning   Turbulence theory and modelling   Artificial neural networks   Aerodynamics  

摘要： In recent years, the data-driven turbulence model has attracted widespread concern in fluid mechanics. The existing approaches modify or supplement the original turbulence model by machine learning based on the experimental/numerical data, in order to augment the capability of the present turbulence models. Different from the previous researches, this paper directly reconstructs a mapping function between the turbulent eddy viscosity and the mean flow variables by neural networks and completely replaces the original partial differential equation model. On the other hand, compared with the machine learning models for the low Reynolds (Re) number flows based on direct numerical simulation data, high Reynolds number flows around airfoils present the apparent scaling effects and strong anisotropy, which induce large challenges in accuracy and generalization capability for the machine learning algorithm. We mainly concentrate on the high Reynolds number turbulent flows around the airfoils and take the results calculated by the computational fluid dynamics solver with the Spallart-Allmaras (SA) model as training data to construct a high-dimensional data-driven network model based on machine learning. The radial basis function neural network and the auxiliary optimization methods are adopted, and the individual models are built separately for the flow fields of the near-wall region, wake region, and far-field region. The training data in this paper is extracted from only three subsonic flow fields of NACA0012 airfoil. The data-driven turbulence model can be applied to various airfoils and flow states, and the predicted eddy viscosity, lift/drag coefficients, and skin friction distributions are all in good agreement with the results of the original SA model. This research demonstrates the promising prospect of machine learning methods in future studies about turbulence modeling.

关键词： Coils heat exchanger   Nanofluid   Nusselt number, carbon nanotube   Artificial neural network   Sensitivity analysis  

摘要： Nowadays, nanofluids are broadly utilized for various engineering and industrial systems including heat exchangers, power plants, air-conditioning, etc. The helically coiled tube heat exchangers are of the most interesting and efficient kinds of heat exchangers. The current study has focused on proposing model to predict Nusselt number by considering Prandtl number, volumetric concentration, and helical number of helically coiled heat exchanger as input variables. The investigated heat exchanger utilizes water carbon nanofluid. To propose an accurate model, a multilayer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM) models are used. 72 experimental data are utilized as input data. Results indicate that LSSVM approach has the best performance and the proposed model by this approach has R-squared value equals to 1. (C) 2018 Elsevier Ltd. All rights reserved.