关键词： POLLUTION   AROMATASE   ESTROGEN   ANDROGENS   ENDOCRINE disruptors  

摘要： Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

关键词： Lexicographic orders   Preference learning   Artificial intelligence   Decision analysis   Accounting  

摘要： Lexicographic preferences on a set of attributes provide a cognitively plausible structure for modeling the behavior of human decision makers. Therefore, the induction of corresponding models from revealed preferences or observed decisions constitutes an interesting problem from a machine learning point of view. In this paper, we introduce a learning algorithm for inducing generalized lexicographic preference models from a given set of training data, which consists of pairwise comparisons between objects. Our approach generalizes simple lexicographic orders in the sense of allowing the model to consider several attributes simultaneously (instead of looking at them one by one), thereby significantly increasing the expressiveness of the model class. In order to evaluate our method, we present a case study of a highly complex real-world problem, namely the choice of the recognition method for actuarial gains and losses from occupational pension schemes. Using a unique sample of European companies, this problem is well suited for demonstrating the effectiveness of our lexicographic ranker. Furthermore, we conduct a series of experiments on benchmark data from the machine learning domain. (C) 2016 Elsevier B.V. All rights reserved.

关键词： 数据挖掘   海关风险管理   预测模型  

摘要： 海关业务每日产生的海量记录中蕴藏着数据"金矿"有待进一步挖掘,为加强海关风险识别的准确性,让大数据的价值进一步得到显现,本文采用数据挖掘分类分析的方法,对历史报关单数据进行分析,根据其查获情况,将有查获与否作为分类标号,建立分类模型对历史报关单进行分类,提取相关的规则,揭示数据中隐藏的规律并运用其规律进行预测,为报关单的风险评估预测提供参考。

关键词： Alcohol use disorder (AUD)   Alcohol abuse (AA)   Alcohol dependence (AD)   Electroencephalography (EEG)   Resting-state EEG (REEG)   Inter-hemispheric coherence   Spectral powers of EEG data  

摘要： Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency (Accuracy = 80.8, sensitivity = 82.5, and specificity = 80, F-Measure = 0.78). In addition, the power computed in different EEG bands were found significant and provided an overall classification efficiency as (Accuracy = 86.6, sensitivity = 95, specificity = 82.5, and F-Measure = 0.88). Further, the integration of these EEG feature resulted into even higher results (Accuracy = 89.3 %, sensitivity = 88.5 %, specificity = 91 %, and F-Measure = 0.90). Based on the results, it is concluded

关键词： Machine learning (ML)   Predictive model   Medicine and health care  

摘要： Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

关键词： Affective design   Role-playing games   Brain-computer interface  

摘要： Game science has become a research field, which attracts industry attention due to a worldwide rich sell-market. To understand the player experience, concepts like flow or boredom mental states require formalization and empirical investigation, taking advantage of the objective data that psychophysiological methods like electroencephalography (EEG) can provide. This work studies the affective ludology and shows two different game levels for Neverwinter Nights 2 developed with the aim to manipulate emotions;two sets of affective design guidelines are presented, with a rigorous formalization that considers the characteristics of role-playing genre and its specific gameplay. An empirical investigation with a brain-computer interface headset has been conducted: by extracting numerical data features, machine learning techniques classify the different activities of the gaming sessions (task and events) to verify if their design differentiation coincides with the affective one. The observed results, also supported by subjective questionnaires data, confirm the goodness of the proposed guidelines, suggesting that this evaluation methodology could be extended to other evaluation tasks.

关键词： 数据挖掘   股票分类   量化投资   分类器  

摘要： 运用K-近邻、朴素贝叶斯、决策树、支持向量机这4种数据挖掘算法,基于2015-04-01—2016-03-31A股市场所有股票日交易数据,计算10个具有代表性的传统技术分析指标,抽取合适样本,结合实际投资需求,构建4个股票强涨跌分类器。对样本数据进行测试,结果表明K-近邻具有较高的分类正确率,支持向量机具有较高的击中率。综合来看,K-近邻和支持向量机更适合于实际投资。

摘要： A new approach to automatic solar disk state detection by all-sky images using machine learning methods is developed and implemented. The efficiency of the most widely used machine learning algorithms is analyzed. The effect of reducing the dimensionality of the feature space on the classification accuracy is estimated. The multilayer artificial neural network model has shown the best accuracy in terms of the true score. The operation result demonstrates the effectiveness of machine learning methods applied to solar disk state detection by all-sky images.

关键词： Air quality   Forecast   Machine learning   Extreme learning machine   Artificial neural network   Ozone   PM2.5   NO2  

摘要： Air quality data (observational and numerical) were used to produce hourly spot concentration forecasts of ozone (O-3), particulate matter 2.5 mu m (PM2.5), and nitrogen dioxide (NO2), up to 48 h for six stations across Canada-Vancouver, Edmonton, Winnipeg, Toronto, Montreal, and Halifax. Using numerical data from an air quality model (GEM-MACH15) as predictors, forecast models for pollutant concentrations were built using multiple linear regression (MLR) and multi-layer perceptron neural networks (MLPNN). A relatively new method, the extreme learning machine (ELM), was also used to overcome the limitation of linear methods as well as the large computational demand of MLPNN. In operational forecasting, the continual arrival of new data necessitates frequent model updating. This type of learning (online sequential learning) is straightforward for MLR and ELM but not for MLPNN. Forecast performance of the online sequential MLR (OSMLR) and online sequential ELM (OSELM), together with stepwise MLR, all updated daily, were compared with MLPNN updated seasonally and the benchmark climatology model. OSELM, combining relatively inexpensive frequent model updating with nonlinear modeling capability, tended to outperform the other models in mean absolute error and correlation. Compared to the linear models, the nonlinear models (OSELM and MLPNN) often had worse bias (mean error) and more severe underprediction of extreme events.

关键词： Enhanced oil recovery   Polymer gels   Conformance improvement treatments   Machine learning techniques   Advanced screening models  

摘要： Conformance improvement by polymer gels continues to gain momentum in the field of water management in mature oilfields. A key component for a successful treatment is the identification of the most appropriate gel technology for a targeted reservoir. Advanced approaches provide efficient screening and ranking tools;however, to the best of our knowledge, no such approaches have been developed for polymer gels so far. In this study, we utilized a machine-learning technique to develop an advanced selection methodology for the application of polymer gels in injection wells. Historical data of four in-situ gel systems including bulk gels, high temperature bulk gels, colloidal dispersion gels, and weak gels were used to train logistic regression models. Data sets of 19 property or parameter were tested for potential outliers, missing values were imputed, and some variables were categorized in order to treat data gaps. To identify the most discriminating variables, the univariate entropy R-2, stepwise regression, and area under ROC curve (AUC) heuristic technique were employed. The candidate variables were then modified according to some considerations like the match of univariate logistic probability to conformance engineering considerations. To consider the regional tendencies in application of polymer gels, we developed three probabilistic models that include different number of treating technologies. Furthermore, to meet the new developments in the application of some gel Systems, we constructed a variant model without the treatment timing indicator (water cut) for each main logistic classifier. Results show that logistic classification models and their variants correctly predict the proper gel technology in more than 85% of projects in the training and validation samples with a minimum AUC of 0.9375. We also used a prediction profiler to visually monitor performances of the classifiers and certain tendencies were identified by the investigation of the mispredicted pro