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Precision Nutrition makes use of personal information about individuals to produce nutritional recommendations that have more utility than general population level recommendations. In many cases, being able to predict current status is a necessary first step in offering tailored nutritional advice. The objective of this study is to predict plasma vitamin C using machine learning. The NHANES dataset was used to predict plasma vitamin C in a cohort of 2952 American adults using regression algorithms and clustering in a way that a hypothetical health application might. Variables were selected based on a known or hypothesized relationship with plasma vitamin C, and variables that are expensive or difficult to obtain were excluded in order to more closely replicate the situation of a real health application. The best performance was seen with the XGBoost regressor, with random forest performing almost identically. Clustering was also investigated as a means of improving regression accuracy by splitting the data up into smaller yet more homogeneous groups, however, this was not successful. The low R-squared scores obtained by the models are likely to be due to the low resolution of the NHANES data, particularly the dietary data. This emphasizes the need for high-quality data sets in Precision Nutrition research. [ABSTRACT FROM AUTHOR]

Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States' National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting. [ABSTRACT FROM AUTHOR]

The demand for fast and accurate tire-pavement contact modelling is becoming increasingly prevalent with the advancement of pavement design and finite-element modelling. This paper presents a tool for fast and accurate prediction of non-uniform tire-pavement contact stresses utilising deep learning. Two truck tires, under various wheel loading, inflation pressure, and slip ratio conditions, were considered. The developed deep learning model, ContactNet, is a deconvolutional neural network consisting of two fully connected layers, one reshape layer, and five deconvolution layers with millions of neurons. Two validated finite-element truck tire models were used to generate a contact stresses database with 1800 simulated results. The database was then used for training and testing of the ContactNet. The ContactNet resulted in average errors of 0.80%, 0.77%, 0.90%, and 0.57% in predicting maximum vertical stress, effective contact area, maximum longitudinal stress, and maximum transverse stress. The mean absolute error of the ContactNet prediction is 0.91 kPa. This significantly outperformed four conventional machine-learning regression methods investigated in this study, including polynomial regression, k-nearest neighbours, multi-layer perceptron, and random forests. [ABSTRACT FROM AUTHOR]

Sunflower constitutes an important source of protein, mineral, vitamin, fatty acid, and offer a balanced source of amino acids. Machine learning is mostly performed for the prediction of descriptive attributes in the quality evaluation of foods. In this study physical attributes of two different sunflower varieties (Metinbey and İnegöl Alası) were determined and algorithms were applied for size and shape prediction of these varieties. In addition, five different machine learning predictors were used as Multilayer Perceptron (MLP), Gaussian Processes (GP), Random Forest (RF), k-Nearest Neighbors (kNN), and Support Vector Regression (SVR). The prediction of surface area, volume, geometric mean diameter, aspect ratio, elongation, and shape index were based on the main physical attributes. İnegöl Alası variety had the greatest physical attributes. The seed length, width and thickness were obtained from İnegöl Alası variety as 23.89, 8.80 and 4.15 mm and from Metinbey as 17.88, 6.20 and 4.01 mm. All varieties were determined as significant in terms of the selected attributes as reported by Pillai Trace and Wilks' Lambda (p<0.01). In the Wilks' Lambda statistics, unexplained of the similarities or differences among the groups was 12.30%. Present findings revealed that MLP and SVR algorithms had the greatest correlation coefficients for all predicted attributes. In the study, the best predicted attributes were geometric mean diameter with an R value of 0.9989 (SVR), followed by volume and elongation with an R value of 0.9988 (MLP). Present findings revealed that MLP and SVR algorithms could potentially be used for size and shape prediction of sunflower varieties. [ABSTRACT FROM AUTHOR]

Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields. [ABSTRACT FROM AUTHOR]

We introduce a pair of Shiny web applications that allow users to visualize random forest prediction intervals alongside those produced by linear regression models. The apps are designed to help undergraduate students deepen their understanding of the role that assumptions play in statistical modeling by comparing and contrasting intervals produced by regression models with those produced by more flexible algorithmic techniques. We describe the mechanics of each approach, illustrate the features of the apps, provide examples highlighting the insights students can gain through their use, and discuss our experience implementing them in an undergraduate class. We argue that, contrary to their reputation as a black box, random forests can be used as a spotlight, for educational purposes, illuminating the role of assumptions in regression models and their impact on the shape, width, and coverage rates of prediction intervals. [ABSTRACT FROM AUTHOR]

Prediction of flow rate in rivers is essential for the planning and management of water resources. This study shows that, based on a Machine Learning approach, accurate models for streamflow prediction can be developed. The Des Moines watershed, which includes both Des Moines River and Raccoon River, was chosen as a case study. Only the daily river discharge was considered for the modeling of 10 stations located on different tributaries of the two rivers. Four machine learning algorithms were applied for the streamflow prediction: Random Subspace, M5P, Random Forest and Bagging. The performance of applied algorithms was assessed using statistical performance indicators and graphical representations. Three stations were selected for the training and testing of the different Machine Learning models. Then, the best model was validated on the other seven stations. Prediction accuracy was also assessed as the forecast horizon increased. Overall, M5P algorithms led to the best predictions, with R2 equal to 0.970 and 0.960 for the stations of East Fork Des Moines River at Dakota City and Des Moines River near Tracy, respectively. Accurate predictions were obtained also on the Raccoon River, with R2 equal to 0.938 and 0.887 for the stations of North Raccoon River near Jefferson and Raccoon River at Van Meter, respectively. [ABSTRACT FROM AUTHOR]

Wireless network parameters such as transmitting power, antenna height, and cell radius are determined based on predicted path loss. The prediction is carried out using empirical or deterministic models. Deterministic models provide accurate predictions but are slow due to their computational complexity, and they require detailed environmental descriptions. While empirical models are less accurate, Machine Learning (ML) models provide fast predictions with accuracies comparable to that of deterministic models. Most Empirical models are versatile as they are valid for various values of frequencies, antenna heights, and sometimes environments, whereas most ML models are not. Therefore, developing a versatile ML model that will surpass empirical model accuracy entails collecting data from various scenarios with different environments and network parameters and using the data to develop the model. Combining datasets of different sizes could lead to lopsidedness in accuracy such that the model accuracy for a particular scenario is low due to data imbalance. This is because model accuracy varies at certain regions of the dataset and such variations are more intense when the dataset is generated from a fusion of datasets of different sizes. A Dynamic Regressor/Ensemble selection technique is proposed to address this problem. In the proposed method, a regressor/ensemble is selected to predict a sample point based on the sample's proximity to a cluster assigned to the regressor/ensemble. K Means Clustering was used to form the clusters and the regressors considered are K Nearest Neighbor (KNN), Extreme Learning Trees (ET), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). The ensembles are any combinations of two, three or four of the regressors. The sample points belonging to each cluster were selected from a validation set based on the regressor that made prediction with lowest absolute error per individual sample point. Implementation of the proposed technique resulted in accuracy improvements in a scenario described by a few sample points in the training data. Improvements in accuracy were also observed on datasets in other works compared to the accuracy reported in the works. The study also shows that using features extracted from satellite images to describe the environment was more appropriate than using a categorical clutter height value. [ABSTRACT FROM AUTHOR]

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India's climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates. [ABSTRACT FROM AUTHOR]

Accurate identification of drug-targets in human body has great significance for designing novel drugs. Compared with traditional experimental methods, prediction of drug-targets via machine learning algorithms has enhanced the attention of many researchers due to fast and accurate prediction. In this study, we propose a machine learning-based method, namely XGB-DrugPred for accurate prediction of druggable proteins. The features from primary protein sequences are extracted by group dipeptide composition, reduced amino acid alphabet, and novel encoder pseudo amino acid composition segmentation. To select the best feature set, eXtreme Gradient Boosting-recursive feature elimination is implemented. The best feature set is provided to eXtreme Gradient Boosting (XGB), Random Forest, and Extremely Randomized Tree classifiers for model training and prediction. The performance of these classifiers is evaluated by tenfold cross-validation. The empirical results show that XGB-based predictor achieves the best results compared with other classifiers and existing methods in the literature. [ABSTRACT FROM AUTHOR]

Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize. In this study, we use RF for short-term load forecasting (STLF), focusing on data representation and training modes. We consider seven methods of defining input patterns and three training modes: local, global and extended global. We also investigate key RF hyperparameters to learn about their optimal settings. The experimental part of the work demonstrates on four STLF problems that our model, in its optimal variant, can outperform both statistical and ML models, providing the most accurate forecasts. [ABSTRACT FROM AUTHOR]

Accurate and reliable prediction of photovoltaic (PV) power output is critical to electricity grid stability and power dispatching capabilities. However, PV power generation is highly volatile and unstable due to different reasons. The wavelet transform (WT) has been utilised in time series applications, such as PV power prediction, to model the stochastic volatility and reduce prediction errors. Yet the existing WT approach has a limitation in terms of time complexity. It requires reconstructing the decomposed components and modelling them separately and thus needs more time for reconstruction, model configuration and training. The aim of this study is to improve the efficiency of applying WT by proposing a new method that uses a single simplified model. Given a time series and its WT coefficients, it trains one model with the coefficients as features and the original time series as labels. This eliminates the need for component reconstruction and training numerous models. This work contributes to the day-ahead aggregated solar PV power time series prediction problem by proposing and comprehensively evaluating a new approach of employing WT. The proposed approach is evaluated using 17 months of aggregated solar PV power data from two real-world datasets. The evaluation includes the use of a variety of prediction models, including Linear Regression, Random Forest, Support Vector Regression, and Convolutional Neural Networks. The results indicate that using a coefficients-based strategy can give predictions that are comparable to those obtained using the components-based approach while requiring fewer models and less computational time. [ABSTRACT FROM AUTHOR]

Aim: The aim of this paper is to improve Accuracy in Disease prediction using symptoms by a novel multilayer perceptron classifier in comparison with the Random Forest algorithm. Materials and Methods: Novel multilayer perceptron classifier and random forest sample size (N=10) to predict the accuracy percentage of predicted disease. G-power is calculated for two different groups, alpha (0.05), power (80%). Results: Based on the measurement of data, statistical analysis, and independent sample T-test, there is a statistically insignificant difference between the two study groups with value p=0.488 (p> 0.05). It was observed that the novel multilayer perceptron algorithm obtains accuracy as 95%. It appears to have better accuracy than the random forest (81%). Conclusion: The results prove that the novel multilayer perceptron algorithm there is a significant improvement in techniques with varied seed values in disease prediction using symptoms. [ABSTRACT FROM AUTHOR]

This article presents direct regression models for forecasting photovoltaic (PV) system output power based on machine learning (ML) methods, in which the Cleanness Index (CI) was introduced as an indicator of the PV panel soiling level. Three different ML methods based on decision trees were used: random forests (RF), extra trees (ET) and gradient boosting (GB). The research results showed that the introduction of the CI has significant potential to improve the forecasting of tree-based ML models. The mean average error of forecasts of the best model, which contained CI, and the model created by excluding CI from the set of input variables were 0.22% and 1.24%, respectively, as related to the nominal power of the PV panel. For the observed models, it was shown that the CI is the second-most important input variable and more significant than the PV panel temperature. [ABSTRACT FROM AUTHOR]

Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007–2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities. [ABSTRACT FROM AUTHOR]

Open access to SAR data from the Sentinel 1 missions allows analyses of long-term ground surface changes. The current data-acquisition frequency of 12 days facilitates the continuous monitoring of phenomena such as volcanic and tectonic activity or mining-related deformations. SAR data are increasingly also used as input data in forecasting phenomena on the basis of machine learning. This article presents the possibility of using selected machine learning algorithms in forecasting the influence of underground mining activity on the ground surface. The study was performed for a mining protective area with a surface of over 500 km 2 and located in western Poland. The ground surface displacements were calculated for the period from November 2014 to July 2021, with the use of the Small Baseline Subset (SBAS) method. The forecasts were performed for a total of 22 identified subsidence troughs. Each of the troughs was provided with two profiles, with a total of more than 10,000 identified points. The selected algorithms served to prepare 180-day displacement forecasts. The best results (significantly better than the baseline) were obtained with the ARIMA and Holt models. Linear models also provided better results than the baseline and their performance was very good at up to 2 months forecasting. Tree-based models including their sophisticated ensemble versions: bagging (Random Forest, Extra Trees) and boosting (XGBoost, LightGBM, CatBoost, Gradient Boosting, Hist Gradient Boosting) cannot be used for this type of predictions since Decision Trees are not able to extrapolate and thus are not a valid stand-alone tool for forecasting in this type of problems. A combination of satellite remote sensing data and machine learning facilitated both the simultaneous quasi-permanent monitoring of ground surface displacements and their forecasting in a relatively long time period. [ABSTRACT FROM AUTHOR]

Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons. [ABSTRACT FROM AUTHOR]

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach. [ABSTRACT FROM AUTHOR]

Machine learning has sparked a wide set of solar prediction experiments due to its recent success, which is one of the most common solutions for solar irradiance forecasting problems specifically. However, while using machine learning regression algorithms, additional attention must be paid to feature selection as well as effective parameter optimization. As a result, this work provides a parsimonious based genetic algorithm that incorporates feature selection integrated with random forest regression. The impact of wind speed on the solar irradiance problem has also been investigated. The performance of the proposed H-GAPRFR algorithm is tested for the location Madurai, India, with eight meteorological data variables over a year and has been validated through statistical metrics such as RMSE, MAE, and coefficient of Determination. In comparison to conventional Support Vector Regression (SVR) and Random Forest (RF) regression techniques, the suggested H-GAPRFR model reduced RMSE by 64.18% and 7.43%, respectively. Second, the suggested H-GAPRFR algorithm was used to investigate the effect of wind speed. From the analysis, it is clear that the proposed model considering wind speed improves the prediction accuracy by further reducing RMSE and MAE by 10.2% and 6.5%, respectively. As a result, it is indicated that in the problem of solar irradiance estimation, the parsimonious model with feature selection can produce improved prediction accuracy. [ABSTRACT FROM AUTHOR]

The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms: the decision tree (DT), random forest (RF), and adaptive boosting (AB) algorithms. On this basis, we explored the applicability of ML algorithms for ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy. Then, MME predictions based on optimal hyperparameters were constructed for different regions of China. The results showed that all three ML algorithms had an optimal maximum depth less than 2, which means that, based on the current amount of data, the three algorithms could only predict positive or negative precipitation anomalies, and extreme precipitation was hard to predict. The importance of each model in the ML-based MME was quantitatively evaluated. The results showed that NCEP CFSv2 and JMA CPS2 had a higher importance in MME for the eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provided a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME had a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019–2021, exhibiting a significant improvement over the other methods. This shows that ML methods have great potential for improving short-term climate prediction. [ABSTRACT FROM AUTHOR]

The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System (BSS) applying machine learning at two levels: network and station. Investigating BSSs at the station-level is the full problem that would provide policymakers, planners, and operators with the needed level of details to make important choices and conclusions. We used Random Forest and Least-Squares Boosting as univariate regression algorithms to model the number of available bikes at the station-level. For the multivariate regression, we applied Partial Least-Squares Regression (PLSR) to reduce the needed prediction models and reproduce the spatiotemporal interactions in different stations in the system at the network-level. Although prediction errors were slightly lower in the case of univariate models, we found that the multivariate model results were promising for the network-level prediction, especially in systems where there are a relatively large number of stations that are spatially correlated. Moreover, results of the station-level analysis suggested that demographic information and other environmental variables were significant factors to model bikes in BSSs. We also demonstrated that the available bikes modeled at the station-level at time t had a notable influence on the bike count models. Station neighbors and prediction horizon times were found to be significant predictors, with 15 minutes being the most effective prediction horizon time. [ABSTRACT FROM AUTHOR]

Space weather describes varying conditions between the Sun and Earth that can degrade Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be precisely and timely corrected for accurate and reliable GNSS applications. That can be modeled with the Vertical Total Electron Content (VTEC) in the Earth's ionosphere. This study investigates different learning algorithms to approximate nonlinear space weather processes and forecast VTEC for 1 h and 24 h in the future for low-, mid- and high-latitude ionospheric grid points along the same longitude. VTEC models are developed using learning algorithms of Decision Tree and ensemble learning of Random Forest, Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost). Furthermore, ensemble models are combined into a single meta-model Voting Regressor. Models were trained, optimized, and validated with the time series cross-validation technique. Moreover, the relative importance of input variables to the VTEC forecast is estimated. The results show that the developed models perform well in both quiet and storm conditions, where multi-tree ensemble learning outperforms the single Decision Tree. In particular, the meta-estimator Voting Regressor provides mostly the lowest RMSE and the highest correlation coefficients as it averages predictions from different well-performing models. Furthermore, expanding the input dataset with time derivatives, moving averages, and daily differences, as well as modifying data, such as differencing, enhances the learning of space weather features, especially over a longer forecast horizon. [ABSTRACT FROM AUTHOR]

In this study, we developed and evaluated a hybrid framework for reservoir inflow forecast. This framework is unprecedented, which integrates new quasi-globally available observation-, satellite-, or model-based datasets using machine learing models to forecast inflow at the local scale. Under this framework, we compared random forests, gradient boosting machine, extreme learning machine, M5-cubist, elastic net, as well as their multi-model ensemble using Bayesian model averaging (BMA), and tested contributions from different input datasets, including retrospective forecast (reforecast) from florecast oriented low ocean resolution (FLOR) dynamic climate model, satellite-based hydrologic products, and climate phenomenon information. The performance was evaluated using Kling–Gupta efficiency (KGE) and correlation coefficient (R) in two headwater reservoirs, Harris reservoir in the humid Alabama–Coosa–Tallapoosa river basin and the Navajo reservoir in the arid Upper Colorado River Basin (UCRB). The results showed that for the Harris reservoir, the BMA combining five models with antecedent inflow and satellite-based hydrologic information as model inputs provided the best performance (KGE = 0.66, R = 0.76). For the Navajo reservoir, the gradient boosting machine model with all variables combined as input showed the best performance (KGE = 0.76, R = 0.83). Satellite-based soil moisture and evaporation consistently showed significant contributions to the inflow forecast. Benefits from climate indices and FLOR reforecast varied by locations, with more benefits coming from climate indices than FLOR potential evaporation reforecast at the Navajo reservoir in UCRB. Given the global coverage of the model inputs, our approach can be potentially applicable to improve reservoir inflow forecasts in different regions of the world. [ABSTRACT FROM AUTHOR]

Based on real operational data from the Robert Bosch GmbH, we investigate influencing features for waiting time estimation of operations in high product-mix / low-volume semiconductor manufacturing fabs. We define waiting time as the elapsed time between completing the previous operation and starting the next one. In addition to well-established features, we introduce novel features to capture the complexity of the manufacturing environment. To the best of our knowledge, we are the first to attempt waiting time estimation in a high product-mix / low-volume semiconductor fab. We present a framework for feature selection which is composed of three steps: First, random forest models are trained for each operation. Second, a permutation feature importance (PFI) for the full set of features for each operation is computed and the performance is statistically evaluated. The optimal subset of features is then chosen by a sequential backward search based on the PFI values. Third, the performance in terms of the coefficient of determination of each optimized model is evaluated by means of the initial performance. We apply the framework to real operational data from the production areas Lithography and Diffusion and conclude that the feature set can be reduced significantly, while the prediction performance remains equal. The novel features are found to be frequently used when estimating waiting times in the investigated use case. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values of new lines for which only genotypic information is available. Therefore, many statistical machine learning methods have been proposed for this task. Multi-trait (MT) genomic prediction models take advantage of correlated traits to improve prediction accuracy. Therefore, some multivariate statistical machine learning methods are popular for GS. In this paper, we compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least squares (PLS) and the multi-trait random forest (RF) methods. Benchmarking was performed with six real datasets. We found that the three investigated methods produce similar results, but under predictors with genotype (G) and environment (E), that is, E + G, the MT GBLUP achieved superior performance, whereas under predictors E + G + genotype × environment (GE) and G + GE, random forest achieved the best results. We also found that the best predictions were achieved under the predictors E + G and E + G + GE. Here, we also provide the R code for the implementation of these three statistical machine learning methods in the sparse kernel method (SKM) library, which offers not only options for single-trait prediction with various statistical machine learning methods but also some options for MT predictions that can help to capture improved complex patterns in datasets that are common in genomic selection. [ABSTRACT FROM AUTHOR]

Since the frequency and intensity of heatwaves are expected to increase due to global warming, it is crucial to establish a simplified approach to preventing personal heat-related illnesses as occupational hazard under extremely hot environments, by taking into account individual differences in heat strain. In light of this, this study proposed a simplified forecast model for personal heat strain under extremely hot environments by utilizing feature importance in machine learning, focused on enhancing its field applicability. Using the 1417 records gathered from the experiments under extremely hot conditions, the simplified model was developed by utilizing four types of machine learning algorithms (i.e., random forest, extreme gradient boosting, support vector regression, and multi-layer perceptron). As a result, the proposed simplified models with four principal features (i.e., dry-bulb temperature, radiant temperature, relative humidity, and percentage body fat) were found to be the most reliable, resulting in a small difference of 0.047 °C compared to the reference model. Since the principal biometric features can be easily measured by a simple and low-cost body composition test (but not with the physical measurement of personal core body temperature) before they are put into the field, the proposed approach will aid in providing more systematic and efficient management system for proactively preventing personal heat-related illnesses. Furthermore, it is expected that the field applicability of the proposed approach can be continuously enhanced by utilizing the accumulated big data gathered from the field-based living-lab projects over a lengthy period. • Traditional approaches to heat stress primarily focused on environmental factors. • Recognizing individual differences in heat strain as an occupational hazard is imperative. • Principal features affecting personal heat strain were identified based on feature importance. • A simplified forecast model for personal heat strain was proposed using principal features. • The proposed model exhibited a small difference of 0.047 °C compared to the reference model. [ABSTRACT FROM AUTHOR]

The ability to predict global horizontal irradiance (GHI) in the wake of changing weather conditions is becoming increasingly important in driving economic growth in the renewable energy industry. The choice of relevant meteorological variables for predicting GHI is crucial because it influences the prediction accuracy due to various geographical conditions. To fulfill this aim, this proposed study identifies a subset of the relevant input variables for the prediction of GHI by applying two methods Feature Combination (FC) and Feature Selection (FS). The results reveal that the most significant input variables for predicting GHI are Solar Zenith Angle, Dew Point, Diffuse Horizontal irradiance, Direct Normal Irradiance, and Wind speed obtained by the FS method. The predictive performance of the selected features is evaluated by feeding them into six different types of Machine Learning (ML) regressor algorithms such as Multiple Linear regressor (MLR), Decision Tree (DT), Random Forest (RF), Gradient Boost (GB), Light Gradient Boost Machine (LGBM) and Extra Tree (ET). The performances of models are evaluated by using statistical measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). ET regressor gives the best prediction performance among all the six models. It is observed that the percentage reduction in error for the FS method as compared to the FC method in terms of the statistical indicators MAE, RMSE, and MAPE are 8.5%, 7.72%, and 25.84% respectively. It shows that the FS method gives better predictive performance than the FC method for the estimation of GHI. [ABSTRACT FROM AUTHOR]

• Integrated CPFEM-ML method is developed for defect induced fatigue life prediction. • Data augmentation with FIPs is used to predict fatigue life with different R ratios. • CNN achieves high accuracy in fatigue life prediction for microstructural features. Fatigue life prediction based on the traditional empirical formulation in large fatigue regimes is limited and prone to over-fitting. In this regard, we propose an integrated approach for fatigue life prediction of lamellar titanium alloys incorporating crystal plasticity simulations and a variety of machine learning methods from a microstructural perspective. Firstly, various defect shapes and distinct lamellar distributions are employed to characterize fatigue performance under different microstructural features. By utilizing fatigue indicator parameters (FIPs) to get an expanded dataset, this work employs algorithms including artificial neural networks (ANN), random forests regression (RFR), support vector regression (SVR), and particularly convolutional neural networks (CNN) to predict fatigue life. The results indicate that CNN outperforms the previously mentioned algorithms in prediction accuracy, due to its ability to directly extract microstructural features from images. Our proposed approach provides a microstructural perspective for fatigue life prediction of lamellar titanium alloys, broadening the way for life assessment of materials with diverse microstructures. [ABSTRACT FROM AUTHOR]

• The correlation between TBM data and rock mass classification was investigated. • Input parameters for a rock mass classification prediction model were identified. • The dynamic relationships between rock masses and parameters were quantified. • Rock mass classifications were predicted using the nomogram classification model. • The accuracy and practical convenience of this prediction method are demonstrated. With the widespread application of tunnel boring machines (TBM) in underground tunnel construction, the accurate and real-time identification of geological indicators has become a key factor for ensuring construction safety. In this study, we introduced the nomogram method into the field of civil engineering for the first time to predict surrounding rock grades. Against the backdrop of a major water conservancy project in China, an in-depth analysis of construction data from the ascending phase was conducted to establish the relationship between surrounding rock grade predictions and key construction parameters. Compared with traditional models, the nomogram method has shown advantages in terms of prediction accuracy. Furthermore, by providing a personalized scoring mechanism for specific construction data, the nomogram method not only achieves the real-time prediction of rock grades but also clearly reveals the dynamic relationships between input parameters and rock grades, clarifying the entire prediction process. We identified the main factors related to rock grade prediction among TBM construction parameters, including the slurry conveyor belt speed, jacking pressure, cutterhead torque, cutterhead thrust, thrust pressure, and actual rotation speed. The training process revealed that the nomogram prediction model achieved optimal performance with seven input parameters, balancing accuracy and training time. According to the prediction results, the nomogram model's area under the curve index is 0.92, providing greater accuracy than the traditional random forest model (0.89). Therefore, this study enhances the transparency and interpretability of model predictions by providing personalized assessments of construction parameters for predicting surrounding rock grades, overcoming the black-box issue of existing prediction methods and offering a novel and effective tool for rock grade prediction. [ABSTRACT FROM AUTHOR]

Improvement of data-driven techniques, specifically machine learning (ML), in material science turned it into a powerful tool for predicting materials behavior. Accordingly, this study provides a ML prediction of empirical creep lifetimes of 9Cr-1Mo ex-service heater tubes that have been used in industry for up to 47 years. Data from over 90,000 h of stress rupture tests shows that the service parameters influence creep lifetime similar to mechanical properties. Employing six different ML algorithms, viz., K Nearest Neighbors (KNN), Support Vector Regressor (SVR), Random Forest (RF), Gradient Boosting (GB), Gaussian Process (GP), and Multi-Layer Perceptron (MLP) demonstrated that the GP and MLP methods performed significantly better in predicting the creep lifetimes rather than other algorithms. Finally, a validation set involving 12 samples was conducted, and the GP algorithm showed better agreement with experimental values than other ML and Larson-Miller Parameter approaches, illustrating the capability of this model to predict creep lifetimes. [Display omitted] [ABSTRACT FROM AUTHOR]

Objectives: Ascertain and compare the performances of Automated Machine Learning (AutoML) tools on large, highly imbalanced healthcare datasets. Materials and Methods: We generated a large dataset using historical de-identified administrative claims including demographic information and flags for disease codes in four different time windows prior to 2019. We then trained three AutoML tools on this dataset to predict six different disease outcomes in 2019 and evaluated model performances on several metrics. Results: The AutoML tools showed improvement from the baseline random forest model but did not differ significantly from each other. All models recorded low area under the precision-recall curve and failed to predict true positives while keeping the true negative rate high. Model performance was not directly related to prevalence. We provide a specific use-case to illustrate how to select a threshold that gives the best balance between true and false positive rates, as this is an important consideration in medical applications. Discussion: Healthcare datasets present several challenges for AutoML tools, including large sample size, high imbalance, and limitations in the available features. Improvements in scalability, combinations of imbalance-learning resampling and ensemble approaches, and curated feature selection are possible next steps to achieve better performance. Conclusion: Among the three explored, no AutoML tool consistently outperforms the rest in terms of predictive performance. The performances of the models in this study suggest that there may be room for improvement in handling medical claims data. Finally, selection of the optimal prediction threshold should be guided by the specific practical application. [ABSTRACT FROM AUTHOR]

Glass-forming ability (GFA) of bulk metallic glasses (BMGs) is a determinant parameter which has been significantly studied. GFA improvements could be achieved through trial-and-error experiments, as a tedious work, or by using developed predicting tools. Machine-Learning (ML) has been used as a promising method to predict the properties of BMGs by removing the barriers in the way of its alloy design. This article aims to develop a ML-based method for predicting the maximum critical diameter (Dmax) of BMGs as a factor of their glass-forming ability. The main result is that the random forest method can be used as a sustainable model (R2 = 92%) for predicting glass-forming ability. Also, adding characteristic temperatures to the model will increase the accuracy and efficiency of the developed model. Comparing the measured and predicted values of Dmax for a set of newly developed BMGs indicated that the model is reliable and can be truly used for predicting the GFA of BMGs. [ABSTRACT FROM AUTHOR]

The prediction model with the sinter drum strength as the evaluation index was established based on the index data and historical sintering data generated during the sintering process. The regression prediction model in the algorithm of machine learning was applied to the prediction of the strength of the sinter drum. After verifying the feasibility of drum strength prediction, different data preprocessing methods were used to preprocess the data. Ten regression prediction algorithms such as linear regression, ridge regression, regression tree, support vector regression, and nearest neighbor regression were used for predicting the sinter drum strength to obtain preliminary prediction results. By comparing the prediction results, the most suitable combinations of data preprocessing algorithms and prediction algorithms for sinter drum strength prediction is obtained. The prediction results show that, for the drum strength of the sinter, using the function data standardization algorithm for data preprocessing has the best effect. Then, using gradient boosting regression, random forest regression, and extra tree regression prediction algorithms resulted in higher prediction accuracy. On this basis, the regression prediction model algorithm parameters are optimized and improved. The parameters of the regression prediction algorithm that are most suitable for the prediction of sinter drum strength are obtained. [ABSTRACT FROM AUTHOR]

Accurate, timely air quality index (AQI) forecasting helps industries in selecting the most suitable air pollution control measures and the public in reducing harmful exposure to pollution. This article proposes a comprehensive method to forecast AQIs. Initially, the work focused on predicting hourly ambient concentrations of PM2.5 and PM10 using artificial neural networks. Once the method was developed, the work was extended to the prediction of other criteria pollutants, i.e., O3, SO2, NO2, and CO, which fed into the process of estimating AQI. The prediction of the AQI not only requires the selection of a robust forecasting model, it also heavily relies on a sequence of pre-processing steps to select predictors and handle different issues in data, including gaps. The presented method dealt with this by imputing missing entries using missForest, a machine learning-based imputation technique which employed the random forest (RF) algorithm. Unlike the usual practice of using RF at the final forecasting stage, we utilized RF at the data pre-processing stage, i.e., missing data imputation and feature selection, and we obtained promising results. The effectiveness of this imputation method was examined against a linear imputation method for the six criteria pollutants and the AQI. The proposed approach was validated against ambient air quality observations for Al-Jahra, a major city in Kuwait. Results obtained showed that models trained using missForest-imputed data could generalize AQI forecasting and with a prediction accuracy of 92.41% when tested on new unseen data, which is better than earlier findings. [ABSTRACT FROM AUTHOR]

Background and purpose: We developed a machine learning model to allow early functional outcome prediction for patients presenting with posterior circulation (pc)-stroke based on CT-imaging and clinical data at admission. The proposed algorithm utilizes quantitative information from automated multidimensional assessments of posterior circulation Acute Stroke Prognosis Early CT-Score (pc-ASPECTS) regions. Discriminatory power was compared to predictions based on conventional pc-ASPECTS ratings. Methods: We retrospectively analyzed non-contrast CTs and clinical data of 172 pc-stroke patients. 90 days outcome was dichotomized into good and poor using modified Rankin Scale (mRS) cut-offs. Predictive performance was assessed for outcome differentiation at mRS 2, 3, 4 and survival prediction (mRS ≤ 5) using random forest algorithms. Results were compared to conventional pc-ASPECTS and clinical parameters. Models were evaluated in a nested fivefold cross-validation approach. Results: Receiver operating characteristic areas under the curves (ROC-AUCs) of the test sets using conventionally rated pc-ASPECTS reached 0.63 for mRS ≤ 4 to 0.68 for mRS ≤ 5 and 0.73 for mRS ≤ 5 to 0.85 for mRS ≤ 2 if clinical data were considered. Pure imaging-based machine learning classifier ROC-AUCs were lowest for mRS ≤ 4 (0.81) and highest for mRS ≤ 5 (0.87). The combined clinical data and machine learning-based model had the highest predictive performance with ROC-AUCs reaching 0.90 for mRS ≤ 2. Conclusion: Machine learning-based evaluation of pc-ASPECTS regions predicts functional outcome of pc-stroke patients with higher accuracy than conventional assessments. This could optimize triage for additional diagnostics and allocation of best possible medical care and might allow required arrangements of the social environment at an early point of time. [ABSTRACT FROM AUTHOR]

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface settlement caused by large-diameter shield tunneling is crucial for the safety of the tunnel environment. However, due to the complexity and uncertainty of the rock-machine interaction and groundwater variation, it is difficult to predict the settlement by developing traditional theoretical methods. Recently, a big number of data obtained from the Chunfeng shield tunnel in China provides the possibility to predict the settlement using machine-learning methods. In this study, the equipment parameters, the geological parameters, and the monitored settlements are used to establish the models. Three machine-learning algorithms (i.e., long-short-term memory (LSTM), random forest (RF), and gated recurrent unit (GRU)) are used to predict the surface settlement. Three indicators, mean absolute error (MAE), accuracy (ACC), and coefficient of determination ( R 2 ), are selected to evaluate the prediction performance. Results demonstrated that the filtering and selection of model parameters is vitally important to the accuracy of model prediction. Among the three machine-learning algorithms, the LSTM algorithm gives the best accuracy in predicting the maximum surface settlement and can effectively predict the settlement development in different strata. [ABSTRACT FROM AUTHOR]

Recent research has shown that random forests (RFs) can create skillful probabilistic severe weather hazard forecasts from numerical weather prediction (NWP) ensemble data. However, it remains unclear how RFs use NWP data and how predictors should be generated from NWP ensembles. This paper compares two methods for creating RFs for next-day severe weather prediction using simulated forecast data from the convection-allowing High-Resolution Ensemble Forecast System, version 2.1 (HREFv2.1). The first method uses predictors from individual ensemble members (IM) at the point of prediction, while the second uses ensemble mean (EM) predictors at multiple spatial points. IM and EM RFs are trained with all predictors as well as predictor subsets, and the Python module tree interpreter (TI) is used to assess RF variable importance and the relationships learned by the RFs. Results show that EM RFs have better objective skill compared to similarly configured IM RFs for all hazards, presumably because EM predictors contain less noise. In both IM and EM RFs, storm variables are found to be most important, followed by index and environment variables. Interestingly, RFs created from storm and index variables tend to produce forecasts with greater or equal skill than those from the all-predictor RFs. TI analysis shows that the RFs emphasize different predictors for different hazards in a way that makes physical sense. Further, TI shows that RFs create calibrated hazard probabilities based on complex, multivariate relationships that go well beyond thresholding 2–5-km updraft helicity. [ABSTRACT FROM AUTHOR]

Online social media have aroused much research interest in recent years. In contrast to previous work that focused on the detection of emerging topics, this article undertakes the prediction of active users in online social events, which is so far rarely explored. This prediction task is formulated as a binary classification problem that built on real-world tweet streams, taking Ferguson event and New York Chockhold event as examples. Then, a comprehensive user feature system is designed to characterize the events’ online participants, which includes not only basic statistical characteristics and image-pixel-level features, but also some emotional features and personality features. Next, the Weighted Random Forest (Weighted-RF) classifier is adopted to solve the classification problem. Based on the user feature system and the classifier, the experience of a previous event can be archived and applied to the prediction of later similar events. Experimental results show that the Weighted-RF trained by samples of Ferguson event can effectively predict active users in NYC event, with an AUC value around 0.8392. Besides, the image-content based personality model provides a new tool for depicting user portraits, which further contributes to the quantitative analysis of online social events. [ABSTRACT FROM AUTHOR]

Globally, extreme rainfall has intense impacts on ecosystems and human livelihoods. However, no effort has yet been made to forecast the extreme rainfall indices through machine learning techniques. In this paper, a new extreme rainfall indices forecasting model is proposed using a random forest (RF) model to provide effective forecasts of monthly extreme rainfall indices. In addition, RF feature importance is proposed in this study to identify the most and least important features for the proposed model. This study forecasts only statistically significant extreme rainfall indices over Bangladesh including consecutive dry days (CDD), the number of heavy rain days (R10mm; rainfall ≥ 10 mm), and the number of heavy rain days (R20mm; rainfall ≥ 20 mm) within 1–3 months of lead time. The proposed model uses monthly antecedent CDD, R10mm, and R20mm including atmospheric parameters and ocean–atmospheric teleconnections, namely, convective available potential energy (CAPE), relative humidity (RH), air temperature (TEM), El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and North Atlantic Oscillation (NAO), as the inputs to the model. Results show that the proposed model yields the best performance to forecast CDD, R10mm, and R20mm with only the antecedent of these indices as input. Ocean–atmospheric teleconnections (IOD, ENSO, and NAO) are useful for CDD forecasting, and local atmospheric parameters (CAPE, RH, and TEM) are useful for R10mm and R20mm forecasting. The results suggest that adding atmospheric parameters and ocean–atmospheric teleconnections is useful to forecast extreme rainfall indices. [ABSTRACT FROM AUTHOR]

Day-ahead prediction of electricity market price is a key for market participants to make a bidding strategy. While numerous methods for day-ahead market price (DMP) forecasting have been developed and applied, those lack interpretable models for DMP forecasting. This study provides a new hybrid model for DMP forecasting, centered on eXtreme Gradient Boosting (XGBoost). The suggested proposal uses neural networks (NNs) with entity embedding (EE) to preprocess categorical feature data and applying Shapley Additive exPlanations (SHAP) to select optimal features. In addition, the weights of NNs and SHAP are interpretability methods. The embedding weights from NNs can reveal the distribution of categorical features in the multi-dimensional space by t-distributed Stochastic Neighbor Embedding (t-SNE). SHAP can explain the impact of input features on the forecast result by assessing the importance of global and local features. The proposed model has been proven to be high accuracy, interpretability and outperforms other models on the test set accuracy, including support vector machine, artificial neural works, classification and regression trees, and random forest). Furthermore, the study found that those load/capacity ratios, the historical clearing prices, penetration of renewable energy, month, hour, weekdays, and weekends have a substantial impact on the prediction of the DMP. [ABSTRACT FROM AUTHOR]

Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio. [ABSTRACT FROM AUTHOR]

The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service providers for recommending adjustments in the service subscription level, both to promote rational resource consumption and to avoid CSP customer loss. A training dataset was built from real data about the customer, the subscribed service and its usage history, and it was used in a supervised machine-learning approach for prediction. Classification models were built and evaluated based on multilayer neural networks, AdaBoost and random forest algorithms. From the experiments with our dataset, the best results for a churn prediction were obtained with a random forest-based model, with 64 estimators, having 0.988 accuracy and 0.997 AUC value. [ABSTRACT FROM AUTHOR]

Background: Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level. Methods: We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots. Results: The logistic regression, gradient boosting and random forest classifiers performed similarly in predicting revision ESS. Area under the receiving operating characteristic curve (AUROC) values were 0.744, 0.741 and 0.730, respectively, using data collected from the baseline visit until six months after baseline ESS. The length of time during which data were collected improved the prediction performance. For data collection times of 0, 3, 6 and 12 months after baseline ESS, AUROC values for the logistic regression were 0.682, 0.715, 0.744 and 0.784, respectively. The number of visits before or after baseline ESS, the number of days from the baseline visit to the baseline ESS, patient age, CRS with nasal polyps (CRSwNP), asthma, non-steroidal anti-inflammatory drug exacerbated respiratory disease and immunodeficiency or suspicion of it all associated with revision ESS. Patient age and number of visits before baseline ESS carried non-linear effects for predictions. Conclusions: Intelligent data analysis identified important predictors of revision ESS at the individual level, such as the frequency of clinical visits, patient age, Type 2 high diseases and immunodeficiency or a suspicion of it. [ABSTRACT FROM AUTHOR]

The PM10 prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM10 concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM10 in Kota Bharu, Kelantan. The results from these three methods were compared to find the best method to predict PM10 concentration for the next day by using the maximum daily data from January 2002 to December 2017. To this end, 80% of the data were used for training and 20% for validation of the models. The performance measure of the PM10 concentration was based on accuracy, sensitivity, specificity, and precision for RF, BRT, and DT, respectively, which indicates that these three models were developed effectively, and they are applicable in the prediction of other atmospheric environmental data. The best model to use in predicting the next day's PM10 concentration classification was the random forest classifier, with an accuracy of 98.37, sensitivity of 97.19, specificity of 99.55, and precision of 99.54, but the result of the boosted regression tree was substantially different from the RF model, with an accuracy of 98.12, sensitivity of 97.51, specificity of 98.72, and precision of 98.71. The best model can assist local governments in providing early warnings to people who are at risk of acute and chronic health consequences from air pollution. [ABSTRACT FROM AUTHOR]

Meteorological phenomena is an area in which a large amount of data is generated and where it is more difficult to make predictions about events that will occur due to the high number of variables on which they depend. In general, for this, probabilistic models are used that offer predictions with a margin of error, so that in many cases they are not very good. Due to the aforementioned conditions, the use of machine learning algorithms can serve to improve predictions. This article describes an exploratory study of the use of machine learning to make predictions about the phenomenon of rain. To do this, a set of data was taken as an example that describes the measurements gathered on rainfall in the main cities of Australia in the last 10 years, and some of the main machine learning algorithms were applied (knn, decision tree, random forest, and neural networks). The results show that the best model is based on neural networks. [ABSTRACT FROM AUTHOR]

• This is the first research of calibrateing quantile regression forests with conformal predictive systems for better performance. • Out-of-bag predictions of quantile regression forests are employed to make full use of data and improve computational efficiency. • With analysis of quantile regression forests, our proposed weighed approach performs best in the comparison experiments. Quantile regression forests (QRF) is a generalization of random forests for quantile regression, which can also output probabilistic prediction for regression problems. QRF delivers a nonlinear and nonparametric way of probabilistic prediction and is widely used in many applications. To improve the prediction quality of QRF, this paper builds conformal predictive systems (CPSs) on top of QRF to calibrate the probabilistic prediction, which is the first attempt of combining CPSs with QRF to our best knowledge. Conformal predictive systems aim to produce valid probabilistic predictions in machine learning community, whose predictive distributions are compatible with realizations. Three algorithms are proposed in this paper. One is a fast approximation algorithm to split conformal predictive system. Building on that, the other two algorithms are proposed based on the out-of-bag samples and the weighted approach of QRF. Experiments with 20 public data sets were conducted and the results demonstrated that our algorithms are empirically valid and compared favourably with the original QRF. [ABSTRACT FROM AUTHOR]

Precise and reliable runoff forecasting is crucial for water resources planning and management. The present study was conducted to test the applicability of different data-driven techniques including artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and M5P models for runoff forecasting for the lead time of 1 day and 2 days in the Koyna River basin, India. The best input variables for the development of the models were selected by applying the Gamma test (GT). Two different scenarios were considered to select the input variables for 2 days ahead runoff forecasting. In the first scenario, the output of 1 day ahead runoff (t + 1) was not used as an input while it was also used as an input along with other input variables for the development of the models in the second scenario. For 2 days ahead runoff forecasting, the models developed by adopting the second scenario performed more accurately than that of the first scenario. The RF model performed the best for 1 day ahead runoff forecasting with root mean square error (RMSE), coefficient of efficiency (CE), correlation coefficient (r) and coefficient of determination (R2) values of 168.94 m3/s, 0.67, 0.84 and 0.704, respectively, during the test period. For 2 days ahead runoff forecasting, RF and ANN models performed the best in the first and second scenario, respectively. In 2 days ahead runoff forecasting, RMSE, CE, r and R2 values were observed to be 169.72 m3/s, 0.67, 0.84, 0.7023 and 148.55 m3/s, 0.74, 0.87, 0.76 in the first and second scenarios, respectively, during the test period. Finally, the results revealed that the addition of 1 day ahead runoff forecast increased the forecast accuracy of 2 days ahead runoff forecasts. In addition, the dependability of the various models was determined using the uncertainty analysis. [ABSTRACT FROM AUTHOR]

Accurate identification of drug-targets in human body has great significance for designing novel drugs. Compared with traditional experimental methods, prediction of drug-targets via machine learning algorithms has enhanced the attention of many researchers due to fast and accurate prediction. In this study, we propose a machine learning-based method, namely XGB-DrugPred for accurate prediction of druggable proteins. The features from primary protein sequences are extracted by group dipeptide composition, reduced amino acid alphabet, and novel encoder pseudo amino acid composition segmentation. To select the best feature set, eXtreme Gradient Boosting-recursive feature elimination is implemented. The best feature set is provided to eXtreme Gradient Boosting (XGB), Random Forest, and Extremely Randomized Tree classifiers for model training and prediction. The performance of these classifiers is evaluated by tenfold cross-validation. The empirical results show that XGB-based predictor achieves the best results compared with other classifiers and existing methods in the literature. [ABSTRACT FROM AUTHOR]

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TFIDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news. [ABSTRACT FROM AUTHOR]