关键词： Micritic limestone   Carbonate rock   Machine learning   XGBoost  

摘要： It is important in geophysical applications to relate the compressional and shear ultrasonic wave velocities of micritic limestone to its porosity, volume fraction and density of micrite grains as well as the effective confining pressure. In this paper, this difficulty task is successfully realized by using the most relevant machine learning methods: The artificial neural network method, the support vector machine method and the extreme gradient boosting method (XGB). A relevant dataset available in literature is considered to train and test the models. It is observed that the XGB method significantly outperform the other methods in term of accuracy and training time. It allow obtaining a very high R-squared value of 0.96 and a very small relative root mean squared error of 3% while predicting the sonic velocities from other petrophysical properties. The robustness of the models is also confirmed by studying the sensitivity of the random splittings between the training and the testing sets (C) 2020 Elsevier B.V. All rights reserved.

关键词： knee arthroplasty   prediction   decision support   shared decision making   patient-reported outcomes  

摘要： Background: Approximately 15%-20% of total knee arthroplasty (TKA) patients do not experience clinically meaningful improvements. We sought to compare the accuracy and parsimony of several machine learning strategies for developing predictive models of failing to experience minimal clinically important differences in patient-reported outcome measures (PROMs) 1 year after TKA. Methods: Patients (N = 587) in 3 large Veteran Health Administration facilities completed PROMs before and 1 year after TKA (92% follow-up). Preoperative PROMs and electronic health record data were used to develop and validate models to predict failing to experience at least a minimal clinically important difference in Knee Injury and Osteoarthritis Outcome Score (KOOS) Total, KOOS JR, and KOOS subscales (Pain, Symptoms, Activities of Daily Living, Quality of Life, and recreation). Several machine learning strategies were used for model development. Ten-fold cross-validation and bootstrapping were used to produce measures of overall accuracy (C-statistic, Brier Score). The sensitivity and specificity of various predicted probability cut-points were examined. Results: The most accurate models produced were for the Activities of Daily Living, Pain, Symptoms, and Quality of Life subscales of the KOOS (C-statistics 0.76, 0.72, 0.72, and 0.71, respectively). Strategies varied substantially in terms of the numbers of inputs required to achieve similar accuracy, with none being superior for all outcomes. Conclusion: Models produced in this project provide estimates of patient-specific improvements in major outcomes 1 year after TKA. Integrating these models into clinical decision support, informed consent and shared decision making could improve patient selection, education, and satisfaction. Published by Elsevier Inc.

关键词： Paleokarst reservoirs   Electrofacies   PCA   K-means   LDA  

摘要： The paleokarst system is one of the main carbonate reservoirs, which can form important super-large oil fields. There are many typical paleokarst reservoirs in the Tarim Basin Ordovician strata, mainly composed of caves, vugs, and fractures. Due to the deep burial depth and strong heterogeneity, qualitative identifying the different scale fracture-vuggy reservoirs from the tight limestone around the wellbore is a real challenge in the industrial community. In this paper, machine learning methods were used to classify electrofacies. Firstly, core samples and electrical imaging logging of the paleokarst reservoirs are observed in detail and a core-electrical imaging chart was established. Secondly, conventional logging data was optimized and preprocessed for data mining, using Principal Component Analysis (PCA) algorithm and K-means algorithm. High-resolution electrical imaging logging was chosen as a constraint to recognize electrofacies, and an electrofacies-lithology database was established. Thirdly, based on the electrofacies-lithology database, Linear Discriminant Analysis (LDA) algorithm was used to build an electrofacies prediction model, which can automatically identify the electrofacies in carbonate strata, with a coincidence rate of 92.2%. Finally, the model was used to quantitatively recognize paleokarst reservoirs and their distributions. The electrofacies machine learning workflow proposed in this paper could be used in Tarim Basin and other similar paleokarst reservoirs, which can improve exploration efficiency and save economic cost.

关键词： Nitrogen   RGB image   Simple nonlinear regression   Random forest regression   Backpropagation neural network  

摘要： Crop red-green-blue (RGB) images are powerful tools in nitrogen (N) nutrition estimation. Various regression models using crop N nutrition parameters and image indices have been suggested, but their accuracy and generalization performance for N estimation have not been thoroughly evaluated. In this study, a commercial digital camera was used to capture rice canopy RGB images in a 2-year field experiment, and three regression methods (simple nonlinear regression, SNR;backpropagation neural network, BPNN;and random forest regression, RF) were used for rice shoot dry matter (DM), N accumulation (NA), and leaf area index (LAI) estimation. A repeated random subsampling validation method was performed 1000 times on all three regression methods for the evaluation of model performance and stability. The RF regression models had the highest accuracy for the validation dataset, with average testing prediction accuracy (ATPA) of 80.17%, 79.44%, and 81.82% for DM, LAI, and NA estimation, respectively, followed by BPNN and SNR models. According to the distribution of ATPA in 1000-time calculations, the highest standard deviation (SD) and interval range (5%- 95%) of ATPA was observed in BPNN models, which indicated that the BPNN model was most susceptible to dataset splitting. The lower SD and interval range of ATPA were followed by RF and SNR models, which indicated that the RF and SNR models were less affected by dataset splitting and were able to produce robust regression models consistently. In conclusion, the ensemble algorithm of the RF model effectively prevents overfitting when dealing with different dataset segmentations;thus, the RF model has strong generalization performance. A combination of digital imagery and appropriate machine learning methods facilitates convenient and reliable estimation of crop N nutrition.

关键词： Machine learning   Multiclass classifier   Distance-based classifier  

摘要： Today, machine learning algorithms are an important research area capable of analyzing and modeling data in any field. Information obtained through machine learning methods helps researchers and planners to understand and review systematic problems of their current strategies. Thus, it is very important to work fully in every field that facilitates human life, such as early and correct diagnosis, correct choice, fully functioning autonomous systems. In this paper, a novel machine learning algorithm for multiclass classification is presented. The proposed method is designed based on the Minimum Distance Classifier (MDC) algorithm. The MDC is variance-insensitive because it classifies input vectors by calculating their distances/similarities with respect to class-centroids (average value of input vectors of a class). As it is known, real-world data contains certain proportions of noise. This situation negatively affects the performance of the MDC. To overcome this problem, we developed a variance-sensitive model, which we call Standardized Variable Distances (SVD), considering the standard deviation and z-score (standardized variable) factors. To ensure the accuracy of the SVD, we used Wisconsin Breast Cancer Original (WBCO) and LED Display Domain (led7digit) datasets, which we obtained from UCI machine learning repository, with 5-fold cross validation. It was compared and analyzed classification performance of the SVD with Decision Tree (DT), Random Forest (RF), k-Nearest Neighbor (k-NN), Multinomial Logistic Regression (MLR), Naive Bayes (NB), Support Vector Machine (SVM), and the Minimum Distance Classifier (MDC), which are well-known in the literature. It has also been compared thirteen different studies using the same datasets over the past five years. Our results in the experimental studies have shown that the SVD can classify better than traditional and state-of-the-art methods, compared in this study. The proposed method reached over 97% classification accurac

关键词： habitat characteristics   potential habitat modeling   artificial neural networks   support vector machines   decision tree CHAID algorithm  

摘要： Ferula ovina (Boiss.) is a valuable but vulnerable monocarpic perennial forb species from Apiaceae plant family whose habitat has been degraded over the years;hence identifying the factors controlling its distribution is important to assist range managers for the reclamation activities. The specific objective of this study was to investigate the quantitative relationships between soil properties, topographical features, climate factors and F. ovina distribution in a semiarid part of central Iran and to assess the relative importance of these factors in controlling its spatial variability. To discern these complex relationships, artificial neural networks (ANNs), support vector machines (SVMs), and decision tree CHAID algorithm were employed. Results from the ANN, SVM, and CHAID models indicated that the climate and topographic conditions should be considered more in explaining the variability in F. ovina occurrence and distribution. Factors such as slope, precipitation of warmest month, and minimum temperature of coldest month were identified by the ANN, SVM, and CHAID models as the determinant factors influencing the spatial distribution of F. ovina in central Iran, respectively. Furthermore, CHAID approach showed greater potential in predicting the F. ovina occurrence in the study area. This study provides a strong basis for identifying the most determinant habitat characteristics of F. ovina and other vulnerable or endangered plant species in semiarid rangelands of Iran;however, its general analytical framework could be applied to other parts of the world with similar challenges.

关键词： Auto loan   Dominance-based Rough Set Balanced Rule   Ensemble   Decision rules   Random Forest   SVM  

摘要： Financial fraud is escalating as financial services and operations grow. Despite preventive actions and security measures deployed to mitigate financial fraud, fraudsters are learning and finding new ways to get around fraud prevention systems, thereby, challenging quantitative techniques and predictive models. Thus, new techniques must be explored and tested so the insights obtained from the analysis may be used to support more accurate fraud prediction and the development of fraud prevention systems which have additional checks to mitigate suspicious events. Auto loan is a significant financial product not yet explored in the literature, unlike the misuse of credit cards, for instance. Given the recent increase in fraudulent transactions concerning auto loan applications, this paper tests a new data set for auto loan applications using a technique not yet explored for financial fraud prediction, namely the Dominance-based Rough Set Balanced Rule Ensemble (DRSA-BRE), and after comparing it with other techniques traditionally used for predicting financial fraud, finds that the proposed approach has several advantages over the traditional ones. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Machine learning   Organic solid waste   Modeling   Prediction  

摘要： Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.

关键词： leaf physiognomy   machine learning   paleobotany   paleoclimate   precipitation   temperature  

摘要： Digital leaf physiognomy (DLP) is considered as one of the most promising methods for estimating past climate. However, current models built using the DLP data set still lack precision, especially for mean annual precipitation (MAP). To improve predictive power, we developed five machine learning (ML) models for mean annual temperature (MAT) and MAP respectively, and then tested the precision of these models and some of their averaging compared with that obtained from other models. The precision of all models was assessed using a repeated stratified 10-fold cross-validation. For MAT, three combinations of models (R-2 = .77) presented moderate improvements in precision over the multiple linear regression (MLR) model (R-2 = .68). For log(e)(MAP), the averaging of the support vector machine (SVM) and boosting models improved the R-2 from .19 to .63 compared with that of the MLR model. For MAP, the R-2 of this model combination was 0.49, which was much better than that of the artificial neural network (ANN) model (R-2 = .21). Even the bagging model, which had the lowest R-2 (.37) for log(e)(MAP), demonstrated better precision (R-2 = .27) for MAP. Our palaeoclimate estimates for nine fossil floras were also more accurate, because they were in better agreement with independent paleoclimate evidence. Our study confirms that our ML models and their averaging can improve paleoclimatic reconstructions, providing a better understanding of the relationship between climate and leaf physiognomy.

关键词： Early mature rice   Machine learning (ML) methods   Multiple linear regression (MLR)   Rice yield prediction   Phenology  

摘要： Rice (Oryza sativa L.) is a staple cereal crop and its demand is substantially increasing with the growth of the global population. Precisely predicting rice yields are of vital importance to ensure the food security in countries like China, where rice accounts for one-fifth of the total agricultural production. Previous studies found that the rice yields had been significantly impacted by climate change. In addition, phenological variables were found to be important factors concerning rice yields due to its fundamental role in carbon allocation between plant organs, but its impacts on rice yields were seldom evaluated. In this study, eleven combinations of phenology, climate and geography data were tested to predict the site-based rice yields using a traditional regression-based method (MLR, multiple linear regression), and more advanced three machine learning (ML) methods: back-propagation neural network (BP), support vector machine (SVM) and random forest (RF). The results showed that ML methods were more precise than MLR method. The combination using the integrated phenology, climate during growing season and geographical information was better for yields predictions than other combinations across the ML methods, e.g. the difference RMSE (R-2) between prediction and observed rice yields were 800 (0.24), 737 (0.33), and 744 (0.31) kg/ha for BP, SVM and RF, respectively. The SVM had achieved the highest precisions in yield predictions and the phenological variables substantially improved the accuracy of yield predictions, and the relative importance of phenological variables were even similar as climatic variables. We highlight the phenology and climate need to be accurately represented in the crop models to improve the accuracy in rice yield prediction under climate change conditions using integrated ML methods.