关键词： Gunshot residues   Hyperspectral imaging   Anomaly detection   Classification   Forensic scene documentation   Forensic investigation  

摘要： Advanced image processing algorithms can support the forensic analyst to make tasks like detection, pattern comparison or identification more objective. In the case of the gunshot residue (GSR) analysis, the automatic detection of potential GSR samples can support the task of evidence collection or analysis of residue needed e.g. for a muzzle-to-target firing distance estimation. In this paper we investigate the application of a hyperspectral camera and two well-known Machine Learning algorithms to automatically indicate the potential presence of GSR samples in a scene containing cloth fabrics. For this study we have created and annotated a hyperspectral image dataset consisting of GSR samples present on multiple fabric types. The GSR samples were obtained using two types of ammunition, discharged from two shooting distances. We have investigated two detection scenarios: an unsupervised anomaly detection (with the RX detector) and a supervised pixel classification (with the SVM classifier). Our results show that an accurate detection is possible in both cases. We also note that in this setting the anomaly detection approach usually requires an image normalisation, while the classifier does not require a fabric-specific information. As an addition, we show that the hyperspectral imaging generally outperforms the RGB imaging in terms of GSR detection accuracy. While the actual verification on presence of GSR on the scene requires an analyst and secondary identification methods, the hyperspectral camera with image processing algorithms can be a valuable tool supporting the evidence collection and analysis. (C) 2018 Published by Elsevier B.V.

关键词： machine learning   statistical methods   remote sensing   satellite imagery   big data   agriculture   sustainable development  

摘要： Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data;the pre-processing and evaluation steps.

关键词： Unsupervised machine learning   Clustering analysis   Genomic similarity   Multiple sclerosis  

摘要： Introduction: Many chronic disorders have genomic etiology, disease progression, clinical presentation, and response to treatment that vary on a patient-to-patient basis. Such variability creates a need to identify characteristics within patient populations that have clinically relevant predictive value in order to advance personalized medicine. Unsupervised machine learning methods are suitable to address this type of problem, in which no a priori class label information is available to guide this search. However, it is challenging for existing methods to identify cluster memberships that are not just a result of natural sampling variation. Moreover, most of the current methods require researchers to provide specific input parameters a priori. Method: This work presents an unsupervised machine learning method to cluster patients based on their genomic makeup without providing input parameters a priori. The method implements internal validity metrics to algorithmically identify the number of clusters, as well as statistical analyses to test for the significance of the results. Furthermore, the method takes advantage of the high degree of linkage disequilibrium between single nucleotide polymorphisms. Finally, a gene pathway analysis is performed to identify potential relationships between the clusters in the context of known biological knowledge. Datasets and results: The method is tested with a cluster validation and a genomic dataset previously used in the literature. Benchmark results indicate that the proposed method provides the greatest performance out of the methods tested. Furthermore, the method is implemented on a sample genome-wide study dataset of 191 multiple sclerosis patients. The results indicate that the method was able to identify genetically distinct patient clusters without the need to select parameters a priori. Additionally, variants identified as significantly different between clusters are shown to be enriched for protein-protein interactions

关键词： Landslide   Rockfall   Machine learning   LiDAR   GIS   Remote sensing  

摘要： The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.

关键词： Random forest   CART   Olfactory perception   Volatile organic compounds   Wine  

摘要： The purpose of this paper is to discuss the application of the Random Forest methodology to sensory analysis. A methodological point of view is mainly adopted to describe as simply as possible the construction of binary decision trees and, more precisely, Classification and Regression Trees (CART), as well as the generation of an ensemble of trees or, in other words, a Random Forest. The interest of the permutation accuracy criterion, as a measure of variable importance, is specifically emphasized as a way of identifying the most predictive variables and selecting a subset of these variables for parsimonious and efficient predictive models. A two-step procedure is proposed for choosing this subset of variables. The principle of the method is illustrated in a case study in which the aim was to better understand and predict the olfactory characteristics of red wines made of the Cabernet Franc grape variety, from their Volatile Organic Compound (VOC) content. For two main olfactory attributes, the bell pepper odor and the leather odor, it was possible to list the most important compounds and to highlight a very small number of compounds useful for estimating each of the olfactory attributes considered. For the latter, it was also observed that Random Forest models had a better predictive ability than Partial Least Squares (PLS) Regression models.

关键词： Aquatic species   Machine learning   Survival analysis   Ecological features   Planned intervention  

摘要： In the work, we evaluate the performance of machine learning approaches for predicting successful eradication of aquatic invasive species (AIS) and assess the extent to which eradication of an invasive species depends on the certain specified ecological features of the target ecosystem and/or features that characterize the planned intervention. We studied the outcomes of 143 planned attempts for eradicating AIS, where each attempt was described by ecological and eradication-strategy-related features of the target ecosystem. We considered several machine learning approaches to determine whether one could produce a classifier that accurately predicts weather an invasive species will be eradicated. To assess each learner's performance, we examined its tenfold cross-validated prediction accuracy as well as the false positive rate, the F-measure, and the Area Under the ROC Curve. We also used Kaplan-Meier survival analysis to determine which features are relevant to predicting the time required for each eradication program. Across the five typical machine learning approaches, our analysis suggests that learners trained by the decision tree work well, and have the best performance. In particular, by examining the trained decision tree model, we found that if an occupied area was not large and/or containments of AIS dispersal were employed, the eradication of AIS was likely to be successful. We also trained decision tree models over only the ecological features and found that their performances were comparable with that of models trained using all features. As our trained decision tree models are accurate, decision makers can use them to estimate the result of the proposed actions before they commit to which specific strategy should be applied.

关键词： 学业风险   相关性分析   关联分析   课程分类   成绩预测  

摘要： 针对大学生存在学业风险、高校管理难度增大的问题,提出了基于一卡通数据与课程分类预测学生是否存在及格风险的方法。首先对计算机学院学生的一卡通数据与课程成绩进行预处理与特征提取,利用皮尔逊相关系数与Apriori算法分析不同学期课程成绩间、早餐次数与成绩间的相关性和关联性。然后结合早餐次数与同类型课程的成绩,运用多种分类器预测学生未来成绩是否及格。结果表明,该方法可预测学生某门课程是否存在不及格风险,便于教辅人员及时干预学业困难学生。

关键词： Thermal sensation   Thermal comfort   Bagging   Machine learning   Ensemble machine learning  

摘要： In this paper, an intelligent ensemble machine learning (EML) method - Bagging was developed for thermal perception prediction. Field data was collected in naturally ventilated (NV) and split air-conditioning (SAC) dormitory buildings in hot summer and cold winter (HSCW) area of China during the summer of 2016. The indoor physical measurement and subjective survey were conducted simultaneously during the field study. To determine the merit of the proposed Bagging approach, the performances of Bagging approach were compared against the artificial neural network (ANN) and support vector machine (SVM) regarding conventional statistical indicators, i.e., mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R-2) and Pearson correlation coefficient (r). Several thermal indices, i.e., thermal sensation (TS), effective temperature (ET*), standard effective temperature (SET*) and predicted mean vote (PMV), were adopted to access the occupants' thermal comfort and evaluate the model prediction. In our case study, the Bagging model's R 2 for TS, PMV, ET degrees and SET* were 0.4986, 0.9892, 0.9920 and 0.9900, respectively. It shows higher accuracy than SVM and ANN models in thermal perception prediction and outperforms the classical PMV index in TS prediction. Results indicate the proposed Bagging model's prediction performance is reliable and is highly accurate to predict the thermal perception. (C) 2018 Elsevier B.V. All rights reserved.

关键词： 机器学习   出行方式选择   多项Logit模型   中小城市   预测精度  

摘要： 在交通出行预测中,多项Logit模型（MNL）的应用十分广泛,但是同时也存在很大的缺陷。模型假定各选择枝不可观测的随机效用部分相互独立且服从二重指数分布,但是这在很多情况下都与实际不符。本文采用机器学习方法中的决策树（Decision Tree）和支持向量机（SVM）模型与多项Logit模型进行对比分析,找出对于居民出行方式选择预测最准确的模型。本文采用的数据来源于2016年12月安顺市的居民出行调查数据,通过三个模型对比可得,SVM模型的预测精度最高,其次是决策树模型,MNL模型的预测精度相对较低。