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Implementing impedance control in Cartesian task space or directly at the joint level is a popular option for achieving desired compliance behavior for robotic manipulators performing tasks. The damping ratio is an important control criterion for modulating the dynamic response; however, tuning or selecting this parameter is not easy, and can be even more complicated in cases where the system cannot be directly solved at the joint space level. Our study proposes a novel methodology for calculating the local optimal damping ratio value and supports it with results obtained from five different scenarios. We carried out 162 different experiments and obtained the values of the inertia, stiffness, and damping matrices for each experiment. Then, data preprocessing was carried out to select the most significant variables using different criteria, reducing the seventeen initial variables to only three. Finally, the damping ratio values were calculated (predicted) using automatic regression tools. In particular, five-fold cross-validation was used to obtain a more generalized model and to assess the forecasting performance. The results show a promising methodology capable of calculating and predicting control parameters for robotic manipulation tasks. [ABSTRACT FROM AUTHOR]

The techniques for forecasting meteorological variables are highly studied since prior knowledge of them allows for the efficient management of renewable energies, and also for other applications of science such as agriculture, health, engineering, energy, etc. In this research, the design, implementation, and comparison of forecasting models for meteorological variables have been performed using different Machine Learning techniques as part of Python open-source software. The techniques implemented include multiple linear regression, polynomial regression, random forest, decision tree, XGBoost, and multilayer perceptron neural network (MLP). To identify the best technique, the mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination ( R 2 ) are used as evaluation metrics. The most efficient techniques depend on the variable to be forecasting, however, it is noted that for most of them, random forest and XGBoost techniques present better performance. For temperature, the best performing technique was Random Forest with an  R 2  of 0.8631, MAE of 0.4728 °C, MAPE of 2.73%, and RMSE of 0.6621 °C; for relative humidity, was Random Forest with an  R 2  of 0.8583, MAE of 2.1380RH, MAPE of 2.50% and RMSE of 2.9003 RH; for solar radiation, was Random Forest with an  R 2  of 0.7333, MAE of 65.8105  W / m 2 , and RMSE of 105.9141  W / m 2 ; and for wind speed, was Random Forest with an  R 2  of 0.3660, MAE of 0.1097  m / s , and RMSE of 0.2136  m / s . [ABSTRACT FROM AUTHOR]

Solar power forecasting is of high interest in managing any power system based on solar energy. In the case of photovoltaic (PV) systems, and building integrated PV (BIPV) in particular, it may help to better operate the power grid and to manage the power load and storage. Power forecasting directly based on PV time series has some advantages over solar irradiance forecasting first and PV power modeling afterwards. In this paper, the power forecasting for BIPV systems in a vertical façade is studied using machine learning algorithms based on decision trees. The forecasting scheme employs the skforecast library from the Python environment, which facilitates the implementation of different schemes for both deterministic and probabilistic forecasting applications. Firstly, deterministic forecasting of hourly BIPV power was performed with XGBoost and Random Forest algorithms for different cases, showing an improvement in forecasting accuracy when some exogenous variables were used. Secondly, probabilistic forecasting was performed with XGBoost combined with the Bootstrap method. The results of this paper show the capabilities of Random Forest and gradient boosting algorithms, such as XGBoost, to work as regressors in time series forecasting of BIPV power. Mean absolute error in the deterministic forecast, using the most influencing exogenous variables, were around 40% and close below 30% for the south and east array, respectively. [ABSTRACT FROM AUTHOR]

Predicting the behaviour of wildfires can help save lives and reduce health, socioeconomic, and environmental impacts. Because wildfire behaviour is highly dependent on fuel type and distribution, their accurate estimation is paramount for accurate prediction of the fire propagation dynamics. This paper studies the effect of combining automated hyperparameter tuning with Bayesian optimisation and recursive feature elimination on the accuracy of three boosting (AdaB, XGB, CatB), two bagging (Random Forest, Extremely Randomised Trees), and three stacking ensemble models with respect to their ability to estimate the vegetation cover type from cartographic data. The models are trained on the University of California Irvine (UCI) cover type dataset using five-fold cross-validation. Feature importance scores are calculated and used in recursive feature elimination analysis to study the sensitivity of model accuracy to the different feature combinations. Our results indicate that the implemented fine-tuning procedure significantly affects the accuracy of all models investigated, with XGB achieving an overall accuracy of 97.1% slightly outperforming the others. [ABSTRACT FROM AUTHOR]

Small-incision lenticule extraction (SMILE) is a safe and effective surgical procedure for refractive correction. However, the nomogram from the VisuMax femtosecond laser system often overestimates the achieved lenticule thickness (LT), leading to inaccurate estimation of residual central corneal thickness in some patients. In order to improve the accuracy of predicting achieved LT, we used machine learning models to make predictions of LT and analyze the influencing factors of LT estimation in this study. We collected nine variables of 302 eyes and their LT results as input variables. The input variables included age, sex, mean K reading of anterior corneal surface, lenticule diameter, preoperative CCT, axial length, the eccentricity of the anterior corneal surface (E), diopter of spherical, and diopter of the cylinder. Multiple linear regression and several machine learning algorithms were employed in developing the models for predicting LT. According to the evaluation results, the Random Forest (RF) model achieved the highest performance in predicting the LT with an R2 of 0.95 and found the importance of CCT and E in predicting LT. To validate the effectiveness of the RF model, we selected additional 50 eyes for testing. Results showed that the nomogram overestimated LT by 19.59% on average, while the RF model underestimated LT by −0.15%. In conclusion, this study can provide efficient technical support for the accurate estimation of LT in SMILE. [ABSTRACT FROM AUTHOR]

Assessment of spatiotemporal dynamics of meteorological variables and their forecast is essential in the context of climate change. Such analysis can help suggest possible solutions for flora and fauna in protected areas and adaptation strategies to make forests and communities more resilient. The present study attempts to analyze climate variability, trend and forecast of temperature and rainfall in the Valmiki Tiger Reserve, India. We utilized rainfall and temperature gridded data obtained from the Indian Meteorological Department during 1981–2020. The Mann–Kendall test and Sen's slope estimator were employed to examine the time series trend and magnitude of change at the annual, monthly and seasonal levels. Random forest machine learning algorithm was used to estimate seasonal prediction and forecasting of rainfall and temperature trend for the next ten years (2021–2030). The predictive capacity of the model was evaluated by statistical performance assessors of coefficient of correlation, mean absolute error, mean absolute percentage error and root mean squared error. The findings revealed a significant decreasing trend in rainfall and an increasing trend in temperature. However, a declining trend for maximum temperature has been observed for winter and post-monsoon seasons. The results of seasonal forecasting exhibited a considerable decrease in rainfall and temperature across the Reserve during all the seasons. However, the temperature will increase during the summer season. The random forest machine learning algorithm has shown its effectiveness in forecasting the temperature and rainfall variables. The findings suggest that these approaches may be used at various spatial scales in different geographical locations. [ABSTRACT FROM AUTHOR]

In this paper, we survey the most recent advances in supervised machine learning (ML) and high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree‐based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high‐frequency financial data. [ABSTRACT FROM AUTHOR]

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reliable flood warning system to minimize the effects of extreme events, are highly important. This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy of forecasts at higher lead times through the dynamic error correction schematic. The new framework simulates an ensemble of streamflow for estimating uncertainty associated with the predictions and is applied across two snow‐fed Himalayan rivers: the Beas River in India and the Sunkoshi River in Nepal. Several statistical indices along with graphical performance indicators were used for assessing the accuracy of the model performance and associated uncertainty. The modeling framework achieved the Nash Sutcliffe Efficiency of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation for the Beas and Sunkoshi river basin respectively for a 7‐day ahead forecast. Thus, the proposed framework can be considered as a promising tool having reasonably good performance in forecasting streamflow at a higher lead time. Key Points: Hybrid hydrological model integrates process‐based model with machine learning algorithm through data assimilation techniqueDynamic error correction framework capable of improving the streamflow forecast at longer lead time is proposedOverall the developed framework improves the forecast accuracy along with quantifying the model prediction uncertainty [ABSTRACT FROM AUTHOR]

As a result of the increased number of COVID-19 cases, Ensemble Machine Learning (EML) would be an effective tool for combatting this pandemic outbreak. An ensemble of classifiers can improve the performance of single machine learning (ML) classifiers, especially stacking-based ensemble learning. Stacking utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction results. However, building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly selected. Therefore, the goal of this paper is to develop and evaluate a generic, data-independent predictive method using stacked-based ensemble learning (GA-Stacking) optimized by aGenetic Algorithm (GA) for outbreak prediction and health decision aided processes. GA-Stacking utilizes five well-known classifiers, including Decision Tree (DT), Random Forest (RF), RIGID regression, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost), at its first level. It also introduces GA to identify comparisons to forecast the number, combination, and trust of these base classifiers based on theMean Squared Error (MSE) as a fitness function. At the second level of the stacked ensemblemodel, a Linear Regression (LR) classifier is used to produce the final prediction. The performance of the model was evaluated using a publicly available dataset from the Center for Systems Science and Engineering, Johns Hopkins University, which consisted of 10,722 data samples. The experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99% for the three selected countries. Furthermore, the proposed model achieved good performance when compared with existing baggingbased approaches. The proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model to predict the epidemic trend for other countries when comparing preventive and control measures. [ABSTRACT FROM AUTHOR]

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19. [ABSTRACT FROM AUTHOR]

We examined the prediction accuracies of genomic best linear unbiased prediction (GBLUP), various weighted GBLUP according to the degrees of marker effects (WGBLUP) and machine learning (ML) methods, and compared them with traditional BLUP for age at first calving (AFC), calving difficulty (CD), and gestation length in Japanese Black cattle. For WGBLUP, firstly, BayesC and FarmCPU were used to estimate marker effects. Then, we constructed three weighted genomic relationship matrices from information of estimated marker effects in the first step: absolute value of the estimated marker‐effect WGBLUP, estimated marker‐variance WGBLUP, and genomic‐feature WGBLUP. For ML, we applied Gaussian kernel, random forest, extreme gradient boost, and support vector regression. We collected a total of 2583 animals having both phenotypic records and genotypes with 30,105 markers and 16,406 pedigree records. For AFC, prediction accuracies of WGBLUP methods using FarmCPU exceeded BLUP by 25.7%–39.5%. For CD, estimated marker‐variance WGBLUP using BayesC achieved the highest prediction accuracy. Among ML methods, extreme gradient boost, support vector regression, and Gaussian kernel increased prediction accuracies by 28.4%, 19.0%, and 36.4% for AFC, CD, and gestation length compared with BLUP, respectively. Thus, prediction performance could be improved using suitable WGBLUP and ML methods according to target reproductive traits for the population used. [ABSTRACT FROM AUTHOR]

Producing an accurate and calibrated probabilistic forecast has high social and economic value. Systematic errors or biases in the ensemble weather forecast can be corrected by postprocessing models whose development is an urgent challenge. Traditionally, the bias correction is done by employing linear regression models that estimate the conditional probability distribution of the forecast. Although this model framework works well, it is restricted to a prespecified model form that often relies on a limited set of predictors only. Most machine learning (ML) methods can tackle these problems with a point prediction, but only a few of them can be applied effectively in a probabilistic manner. The tree-based ML techniques, namely, natural gradient boosting (NGB), quantile random forests (QRF), and distributional regression forests (DRF), are used to adjust hourly 2-m temperature ensemble prediction at lead times of 1–10 days. The ensemble model output statistics (EMOS) and its boosting version are used as benchmark models. The model forecast is based on the European Centre for Medium-Range Weather Forecasts (ECMWF) for the Czech Republic domain. Two training periods 2015–18 and 2018 only were used to learn the models, and their prediction skill was evaluated in 2019. The results show that the QRF and NGB methods provide the best performance for 1–2-day forecasts, while the EMOS method outperforms other methods for 8–10-day forecasts. Key components to improving short-term forecasting are additional atmospheric/surface state predictors and the 4-yr training sample size. Significance Statement: Machine learning methods have great potential and are beginning to be widely applied in meteorology in recent years. A new technique called natural gradient boosting (NGB) has been released and used in this paper to refine the probabilistic forecast of surface temperature. It was found that the NGB has better prediction skills than the traditional ensemble model output statistics in forecasting 1 and 2 days in advance. The NGB has similar prediction skills with lower computational demands compared to other advanced machine learning methods such as the quantile random forests. We showed a path to employ the NGB method in this task, which can be followed for refining other and more challenging meteorological variables such as wind speed or precipitation. [ABSTRACT FROM AUTHOR]

In the Caribbean basin, particulate matter lower or equal to 10 μm in diameter (PM10) has a huge impact on human mortality and morbidity due to the African dust. For the first time in this geographical area, the theoretical framework of artificial intelligence is applied to forecast PM10 concentrations. The aim of this study is to forecast PM10 concentrations using six machine learning (ML) models: support vector regression (SVR), k-nearest neighbor regression (kNN), random forest regression (RFR), gradient boosting regression (GBR), Tweedie regression (TR), and Bayesian ridge regression (BRR). Overall, with MBEmax = −2.8139, the results showed that all the models tend to slightly underestimate PM10 empirical data. GBR is the model that gives the best performance (r = 0.7831, R2 = 0.6132, MAE = 6.8479, RMSE = 10.4400, and IOA = 0.7368). By comparing our results to other PM10 ML studies in megacities, we found similar performance using only three input variables, whereas previous studies use many input variables with Artificial Neural Network (ANN) models. All these results showed the features of PM10 concentrations in the Caribbean area. [ABSTRACT FROM AUTHOR]

This study, which aims to make predictions using a previously unused deep hybrid Convolutional Neural Network-Support Vector Regression approach for hydropower generation, was carried out using a unique meteorological data set as input data. The unique dataset was collected from Kaman Meteorology Directorate and Hirfanlı HEPP in Turkey, from 2007 to 2021, to estimate the HEPP's Net Head and Hydroelectric Power Generation on a daily basis. The performances of the prediction models were benchmarked in addition to the used CNN-SVR model with Machine Learning (ML) models (Boosting Random Forest Regression (BRFR) and Weighted K-Nearest Neighbor Regression (WKNNR)) and Deep Learning (DL) models (Long-Short Term Memory (LSTM) and Deep Belief Network (DBN)). The comparison results of the used CNN-SVR model with other alternative models showed that CNN-SVR performed effectively with the highest correlation coefficient of 0.971 for NH and 0.968 for PP. [ABSTRACT FROM AUTHOR]

Metaplastic breast cancer (MpBC) is a rare and aggressive subtype of breast cancer, with data emerging on prognostic factors and survival prediction. This study aimed to develop machine learning models to predict breast cancer-specific survival (BCSS) in MpBC patients, utilizing a dataset of 160 patients with clinical, pathological, and biological variables. An in-depth variable selection process was carried out using gain ratio and correlation-based methods, resulting in 10 variables for model estimation. Five models (decision tree with bagging; logistic regression; multilayer perceptron; naïve Bayes; and, random forest algorithms) were evaluated using 10-fold cross-validation. Despite the constraints posed by the absence of therapeutic information, the random forest model exhibited the highest performance in predicting BCSS, with an ROC area of 0.808. This study emphasizes the potential of machine learning algorithms in predicting prognosis for complex and heterogeneous cancer subtypes using clinical datasets, and their potential to contribute to patient management. Further research that incorporates additional variables, such as treatment response, and more advanced machine learning techniques will likely enhance the predictive power of MpBC prognostic models. [ABSTRACT FROM AUTHOR]

The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management. [ABSTRACT FROM AUTHOR]

Global numerical weather prediction (NWP) models, including ensemble prediction systems (EPS), are routinely used by the forecasters to predict tropical cyclone (TC) tracks and intensity. However, due to computational restrictions, the EPSs are usually of coarse resolution, which results in poor prediction of TC intensity. The bias in the model predicting maximum sustained winds (MSW) and central pressure (CP) is large when TCs are intense. This article describes the suitability of machine learning (ML) techniques to reduce errors in TC intensity forecasts obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Ensemble Prediction System (NEPS) over the North Indian Ocean (NIO). Four different ML techniques, namely, Multivariate Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boost (XGB), have been used for the bias correction (BC) of mean MSW and CP, while the spread of ensemble members has been retained. The study is based on 20 TC cases formed over the NIO during 2018–21. The best track (BT) information from the India Meteorological Department (IMD) has been used for training the ML models and verifying them. The results show that XGB (for CP) and RF (for MSW) ML techniques are superior to MLR and SVR methods. The statistically significant reduction (at 99% CI) in mean absolute error (MAE) (root mean square error (RMSE) of mean MSW and CP while using the best algorithm are 30 (33)% and 63 (55)%, respectively. The correlation coefficient for CP increases from 0.58 to 0.93 and that for MSW from 0.60 to 0.75. The probabilistic verification of bias-corrected ensemble members also suggests that the RF- and XGB-based models have better reliability and ROC compared to the raw and other models for MSW and CP, respectively. [ABSTRACT FROM AUTHOR]

This study aimed to introduce the random forest (RF) method as a valuable tool for short-term crash frequency prediction. Besides, the study compares the forecast efficiency of the RF model with the classical seasonal autoregressive integrated moving average (SARIMA) model in the multivariate time-series analysis of crash counts. To this end, fatal accidents reported by the police and intercity traffic flow extracted from the loop detectors were aggregated in intercity highways at the country's level monthly from Farvardin 1395 to Mordad 1400. The first 55 data points were used as the training sample, and the remaining ten months were considered the test sample. The Box-Jenkins and random forest machine learning methods were employed for short-term crash frequency prediction. The mean absolute percentage error (MAPE) criterion was utilized to compare the forecast accuracy of the developed models. The performance of the random forest model (MAPE = 2.6) with the exogenous variables of traffic flow, crash year, and month outperformed the best SARIMA (1 , 0 , 0) (1 , 0 , 0) 12 model (MAPE = 5.7) with traffic flow as the regressor. This study suggests that the random forest as an ensemble learning algorithm is a better crash prediction tool compared to the classical Box-Jenkins method, accounting for the non-linear dependencies in crash count time-series. Besides, the results illustrate that the multivariate SARIMA (SARIMAX) model significantly outperforms its univariate counterpart, accounting for the simultaneous impacts of exogenous variables. [ABSTRACT FROM AUTHOR]

This thesis takes the historical weather time series of Chongqing as experimental samples. Firstly, this thesis uses wavelet transform to organize the data, and then divides the sample data into training and test sets to verify the accuracy of the evaluation of the Naive Bayes Model. Secondly, the Naive Bayes Model is compared with currently used machine learning models such as SVM, XGBoost, bagging, and random forest. Finally, the results show that the Naive Bayes Model has high stability and accuracy for the air quality assessment of Chongqing, and it can be applied to the evaluation of urban ambient air quality. [ABSTRACT FROM AUTHOR]

Using a machine-learning technique known as random forests, we analyze the role of investor confidence in forecasting monthly aggregate realized stock-market volatility of the United States (US), over and above a wide-array of macroeconomic and financial variables. We estimate random forests on data for a period from 2001 to 2020, and study horizons up to one year by computing forecasts for recursive and a rolling estimation window. We find that investor confidence, and especially investor confidence uncertainty has out-of-sample predictive value for overall realized volatility, as well as its "good" and "bad" variants. Our results have important implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Accurate short-term ozone forecasts may allow improved policy actions to reduce the risk to human health. However, forecasting surface ozone is a difficult problem as its concentrations are controlled by a number of physical and chemical processes that act on varying timescales. We implement a state-of-the-art transformer-based model, the temporal fusion transformer, trained on observational data from three European countries. In four-day forecasts of daily maximum 8-hour ozone (DMA8), our novel approach is highly skillful (MAE = 4.9 ppb, coefficient of determination R² = 0:81) and generalizes well to data from 13 other European countries unseen during training (MAE = 5.0 ppb, R² = 0:78). The model outperforms other machine learning models on our data (ridge regression, random forests, and long short-term memory networks) and compares favorably to the performance of other published deep learning architectures tested on different data. Furthermore, we illustrate that the model pays attention to physical variables known to control ozone concentrations and that the attention mechanism allows the model to use the most relevant days of past ozone concentrations to make accurate forecasts on test data. The skillful performance of the model, particularly in generalizing to unseen European countries, suggests that machine learning methods may provide a computationally cheap approach for accurate air quality forecasting across Europ. [ABSTRACT FROM AUTHOR]

Investment in the right location ensures sustainable competition. In the telecommunication sector, the number of base stations (BSs) is one of the most significant investment parameters. When a potential BS is subject to be selected, practitioners will first consider investing in a BS where the return on investment (ROI) is highest. Therefore, the quantifiable objectives are distinctly defined, as it makes sense to choose maximizing features that raise per unit investment. This study provides a solution to evaluate the best BS installation alternative with machine‐learning approaches as well as to estimate ROI value by changing the properties that affect the ROI value. For this purpose, the estimation performance of logistic regression, random forest, and XGBoost methods are compared and further strengthened by random forest hyperparameter optimization to provide the best performance. The model, with a success rate of 98.7% according to the F‐score, showed that it was a robust algorithm. The three most essential features for the ROI value are determined to be voice traffic, data traffic, and frequency cost. These parameters enable a review of the prediction results of telecommunications managers and planning specialists responsible for BS investment. [ABSTRACT FROM AUTHOR]

We investigate the different roles played by nodes' network and non-network attributes in explaining the formation of European university collaborations from 2011 to 2016, in three European Research Council (ERC) domains: Social Sciences and Humanities (SSH), Physical and Engineering Sciences (PE), Life Sciences (LS), as well as multidisciplinary collaborations. On link formation in collaboration networks, existing research has not yet compared and simultaneously examined both network and non-network attributes. Using four machine learning predictive algorithms (LASSO, Neural Network, Gradient Boosting, and Random Forest) our results show that, over various model specifications: (i) best model link formation accuracy is larger than 80%, (ii) among the non-network attributes, public funding plays an important role in PE and LS, (iii) network attributes count more than non-network attributes for the formation, sensibly increasing accuracy, (iv) feature-importance scores show a different ordering in the four domains, thus signalling different modes of knowledge production and transmission taking place within these different scientific communities. [ABSTRACT FROM AUTHOR]

For effective water resource management, water budgeting, and optimal release discharge from a reservoir, the accurate prediction of daily inflow is critical. An attempt has been made using artificial intelligence (AI) techniques to enhance water management efficiency in the Haditha-dam reservoir. This case study occasionally suffers from severe drought events and thus causes significant water shortages as well as stopping hydroelectric power stations for several months. Four different approaches were employed for inflow forecasting, namely multiple linear regression (MLR), random forest (RF), extreme learning machine (ELM), and regularized extreme learning machine (RELM). Autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to select the best-lagged variables. The obtained results revealed the superiority of the RELM model compared to other forecast models. The proposed model (RELM) yielded higher prediction accuracy, and its prediction records were similar to the actual values. Moreover, the adopted model achieved a higher correlation of coefficient value (R = 0.955). The regularization approach effectively enhanced the prediction capacity and the generalization ability of the proposed model. On the other hand, the RF model's performance capacity was poor compared to other comparable models due to the overfitting issue. Moreover, the results showed that the PACF (partial autocorrelation function) gave more accurate and realistic predictors than ACF (autocorrelation function) because of its ability to cope with a sudden temporal variation of inflow time series. Overall, the RELM approach provided higher adequacy and tighter confidence in forecasting daily inflow even in noisy data and severe climatic conditions. [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]

• A spatiotemporal risk prediction of wildlife-vehicle collisions (WVCs) is presented. • A Bavarian dataset of around 731,000 accidents over a period of 10 years (2010–2019) was used. • Heterogenous datasets of impact factors are pre-processed using GIS. • Different machine learning (ML) approaches are applied - random forest, feedforward neural networks, and support vector machine classifier to predict the risk of WVCs. • ML algorithms provide high risk prediction quality of over 86% (double-weighted sensitivity and single-weighted specificity rate). • Spatial transferability and temporal forecast of WVC prediction was verified. Introduction: The technology in the automotive industry is becoming increasingly safer in the age of automated driving, but the number of accidents is still high, especially in wildlife-vehicle collisions (WVCs). To better avoid these accidents, patterns involved in these accidents must be detected. Method : This paper presents a spatiotemporal risk prediction of WVCs, including various road and environmental characteristics. A process of data preparation using GIS automated by Python scripts was developed to enable a spatiotemporal link of diverse features for the subsequent predictive data analysis. Different machine learning (ML) approaches were applied- random forest (RF), feedforward neural networks (FNN), and support vector machine classifier (SVM) - including automated ML to predict the risk of WVCs. Therefore, a dataset of approximately 731,000 accidents reported to the police in Bavaria over a period of 10 years (2010–2019) was used. In addition, non-accidents were randomly generated in Python over time and space for the supervised ML processes. As the actual risk probability for WVCs and non-WVCs is not entirely known, the impact of different training ratios between accidents and non-accidents was tested on the risk prediction quality (RPQ) (25%, 50%, 75%, 90% WVCs) of the double-weighted sensitivity and single-weighted specificity rate. Results: The test yielded high mean values of RPQ as an indicator for a suitable WVC prediction. Both RF (86.6%) and FNN (86.7%) were identified as suitable choices for WVC risk prediction in terms of RPQ. The SVM yielded a lower prediction quality, even though acceptable results could be achieved within a shorter runtime. Practical Applications: Spatial transferability was verified since the algorithm was trained on the dataset of Bavaria (excluding Upper Bavaria) and successfully tested in Upper Bavaria. WVC forecasts were also proven through training with datasets from 2010-2017 and in prediction for 2018 and 2019. [ABSTRACT FROM AUTHOR]

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]

Phylogenetic analyzes under the Maximum-Likelihood (ML) model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating ML-based tree inferences. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyzes, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets. [ABSTRACT FROM AUTHOR]

Movements in the India VIX are an important gauge of how the market's risk perception shifts from day to day. This research attempts to forecast movements one day ahead of the India VIX using logistic regression and 11 ensemble learning classifiers. The period of study is from April 2009 to March 2021. To achieve the stated task, classifiers were trained and validated with 90% of the given sample, considering two-fold time-series cross-validation for hyper-tuning. Optimised models were then predicted on an unseen test dataset, representing 10% of the given sample. The results showed that optimal models performed well, and their accuracy scores were similar, with minor variations ranging from 63.33% to 67.67%. The stacking classifier achieved the highest accuracy. Furthermore, CatBoost, Light Gradient Boosted Machine (LightGBM), Extreme Gradient Boosting (XGBoost), voting, stacking, bagging and Random Forest classifiers are the best models with statistically similar performances. Among them, CatBoost, LightGBM, XGBoost and Random Forest classifiers can be recommended for forecasting day-to-day movements of the India VIX because of their inherently optimised structure. This finding is very useful for anticipating risk in the Indian stock market. [ABSTRACT FROM AUTHOR]

Purpose: Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches. Design/methodology/approach: Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy. Findings: The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967. Originality/value: The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions. [ABSTRACT FROM AUTHOR]

With the continuous development of machine learning and the increasing complexity of financial data analysis, it is more popular to use models in the field of machine learning to solve the hot and difficult problems in the financial industry. To improve the effectiveness of stock trend prediction and solve the problems in time series data processing, this paper combines the fuzzy affiliation function with stock‐related technical indicators to obtain nominal data that can widely reflect the constituent stocks in the case of time series changes by analysing the S&P 500 index. Meanwhile, in order to optimise the current machine learning algorithm in which the setting and adjustment of hyperparameters rely too much on empirical knowledge, this paper combines the deep forest model to train the stock data separately. The experimental results show that (1) the accuracy of the extreme random forest and the accuracy of the multi‐grain cascade forest are both higher than that of the gated recurrent unit (GRU) model when the un‐fuzzy index‐adjusted dataset is used as features for input, (2) the accuracy of the extreme random forest and the accuracy of the multigranular cascade forest are improved by using the fuzzy index‐adjusted dataset as features for input, (3) the accuracy of the fuzzy index‐adjusted dataset as features for inputting the extreme random forest is improved by 18.89% compared to that of the un‐fuzzy index‐adjusted dataset as features for inputting the extreme random forest and (4) the average accuracy of the fuzzy index‐adjusted dataset as features for inputting multi‐grain cascade forest increased by 5.67%. [ABSTRACT FROM AUTHOR]

Public health is now in danger because of the current monkeypox outbreak, which has spread rapidly to more than 40 countries outside of Africa. The growing monkeypox epidemic has been classified as a "public health emergency of international concern" (PHEIC) by the World Health Organization (WHO). Infection outcomes, risk factors, clinical presentation, and transmission are all poorly understood. Computer- and machine-learning-assisted prediction and forecasting will be useful for controlling its spread. The objective of this research is to use the historical data of all reported human monkey pox cases to predict the transmission rate of the disease. This paper proposed stacking ensemble learning and machine learning techniques to forecast the rate of transmission of monkeypox. In this work, adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE) were applied for time series forecasting of monkeypox transmission. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Additionally, the monkey pox dataset was used as test data for this investigation. Experimental results revealed that SEL outperformed other machine learning approaches considered in this work with an RMSE of 33.1075; a MSE of 1096.1068; and a MAE of 22.4214. This is an indication that SEL is a better predictor than all the other models used in this study. It is hoped that this research will help government officials understand the threat of monkey pox and take the necessary mitigation actions. [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]

Link prediction is a widely studied topic in graph data analytics and finds numerous applications like friend recommendations in social networks and product recommendations in e-commerce. It refers to predicting new connections or edges that may arise in the near future amongst the nodes of the network. There exist various methods of link prediction generally based on either local, semi-local, or global features of networks and usually suffers from the problems of consistency in their performances over different and large size networks. In this paper, we intend to propose a generic and improved method of link prediction named as NSMLLP by integrating Node centralities, Similarity measures, and Machine Learning classifiers. We calculate popularity measures for every node and evaluate similarity measures for every pair of nodes in the network. The combined popularity and similarity measures form the features for every node pair in the network. The combined features of the nodes at the end of the edges, along with the positive or negative edge label, form a well-defined dataset for the task of link prediction. This dataset is then fed into machine learning classifiers like Random Forest classifier, AdaBoost classifier, and an ANN based classifier. The results obtained from these classifiers are then combined to make the final link prediction. We provide an information gain study aiming to quantify the improvement brought on by our proposed method. A feature importance study is also provided to comprehend better the relative importance of the various popularity and similarity measures used by us. The experimental results obtained on multiple real-life networks demonstrate that the proposed technique outperforms many existing popular methods of link predication based on several evaluation criteria. [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]

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]

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]

Retweet prediction is an important task in the context of various problems, such as information spreading analysis, automatic fake news detection, social media monitoring, etc. In this study, we explore retweet prediction based on heterogeneous data sources. In order to classify a tweet according to the number of retweets, we combine features extracted from the multilayer network and text. More specifically, we introduce a multilayer framework for the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships, as well as other key properties of communication on Twitter. Next, we select a set of local network measures from each layer and construct a set of multilayer network features. We also adopt a BERT-based language model, namely Cro-CoV-cseBERT, to capture the high-level semantics and structure of tweets as a set of text features. We then trained six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category-embedding model, neural oblivious decision ensembles, and an attentive interpretable tabular learning model for the retweet-prediction task. We compared the performance of all six algorithms in three different setups: with text features only, with multilayer network features only, and with both feature sets. We evaluated all the setups in terms of standard evaluation measures. For this task, we first prepared an empirical dataset of 199,431 tweets in Croatian posted between 1 January 2020 and 31 May 2021. Our results indicate that the prediction model performs better by integrating multilayer network features with text features than by using only one set of features. [ABSTRACT FROM AUTHOR]

Various methods are developed to study drug-target interaction problems. One way to deal with the drug-target interaction problem is to use a wet laboratory, which is considered a time-consuming process. Another possible way is through computational techniques. These techniques are significant in terms of time and cost. However, computational techniques use traditional classifiers to predict drug-target interactions. Drug-target interaction datasets suffer from a class imbalance problem where non-interaction classes are large in number and interaction classes are less proportional. Traditional classifiers are affected by their biased nature towards the majority class. This article provides a proposed model which uses AM-PseAAC and MSF as feature representations for target and drug, respectively, and also uses SMOTE-ENN as a resampling technique to handle class imbalance. Finally, it uses a soft voting ensemble proposed by combining random forest and Xgboost ensemble learners. A comparison of the proposed model with the existing state of the art using standard datasets (enzyme, GPCR, Ion Channel, Nuclear Receptor) confirms that the proposed model outperforms. Experiments were performed at different stages of the proposed design, considering AUC, G-Mean, sensitivity, and specificity as performance metrics, which in turn validate our proposed framework. [ABSTRACT FROM AUTHOR]

The agricultural sector’s day-to-day operations, such as irrigation and sowing, are impacted by the weather. Therefore, weather constitutes a key role in all regular human activities. Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property fromdisasters.Rainfall, wind speed, humidity, wind direction, cloud, temperature, and other weather forecasting variables are used in this work for weather prediction. Many research works have been conducted on weather forecasting. The drawbacks of existing approaches are that they are less effective, inaccurate, and time-consuming. To overcome these issues, this paper proposes an enhanced and reliable weather forecasting technique. As well as developing weather forecasting in remote areas. Weather data analysis and machine learning techniques, such as Gradient Boosting Decision Tree, Random Forest, Naive Bayes Bernoulli, and KNN Algorithm are deployed to anticipate weather conditions. A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather forecasting. The aim of this study is to demonstrate its ability to predict weather forecasts as soon as possible. Experimental evaluation shows our ensemble technique achieves 95% prediction accuracy.Also, for 1000 nodes it is less than 10 s for prediction, and for 5000 nodes it takes less than 40 s for prediction. [ABSTRACT FROM AUTHOR]

In this paper, we use the Eurozone yield curve in an effort to forecast the deviations of the euro-area output (IPI) from its long-run trend. We use various short- and long-term interest rates spanning the period from 2004:9 to 2020:6 in monthly frequency. The interest rates are fed to three machine learning methodologies: Decision Trees, Random Forests, and Support Vector Machines (SVM). These Machine Learning methodologies are then compared to an Elastic-Net Logistic Regression (Logit) model from the area of Econometrics. According to the results, the optimal SVM model coupled with the RBF kernel outperforms the competition reaching an in-sample accuracy of 85.29% and an out-of-sample accuracy of 94.74%. [ABSTRACT FROM AUTHOR]

Predicting vessel trajectories is crucial for enhancing situational awareness and preventing collisions at sea. However, achieving accurate and efficient predictions is challenging due to the heterogeneity in vessel movement patterns and changes in vessel mobility modes during voyages. To address this, we propose a new approach that uses historical AIS data to cluster route patterns for each vessel type, thereby improving prediction accuracy. By training machine learning algorithms to focus only on similar vessel types, this approach can better predict individual vessel mobility patterns. This approach offers computational advantages by using a relatively small set of trajectories from the nearest cluster of a selected vessel. Both spatial and course attributes are considered to determine the nearest cluster, while engineered features capture changes in vessel mobility modes. Using an AIS dataset from UTM Zone 10N (US West Coast), we achieved distance errors of 370 m , 742 m , and 1. 2 k m for horizons 10, 20, and 30 min, respectively, using the Random Forest algorithm for short-term trajectory prediction (≤ 30 min) with the last 1-hour trajectory of selected vessels as input. • Proposed framework for predicting the short-term future trajectory of vessels. • Enhanced prediction accuracy by addressing heterogeneity in mobility patterns. • Engineered features are incorporated to consider changes in mobility modes. • Spatial and course attributes are used for trajectory classification. • Evaluation shows high prediction accuracy with low computational cost. [ABSTRACT FROM AUTHOR]

• This study provides inspiration to use breeding values for predicting variety yield. • Three machine learning algorithm approaches were used to develop prediction models. • The proposed model based on random forest achieved the highest prediction accuracy. • The model can provide support for selecting good varieties effectively in a district or a county. This study proposed a model that utilized machine learning algorithms to predict the yield of maize (Zea mays L.) cultivars. This will enable the selection of good cultivars with high yields that are suitable for planting in specific areas, such as a district or county. The breeding value of the cultivars and 11 types of time-series meteorological variables were selected as the input parameters of the model. The yield was set as the output parameter of the model. Random forest (RF), Levenberg–Marquardt neural network, and multilayer perceptron neural network algorithms were used to construct the model. The results showed that the RF outperformed the other algorithms in predicting the yield of maize cultivars by achieving the maximum coefficient of determination (R2) of 0.77 and minimal root-mean-square error of 320.25 kg/acre, mean absolute error of 229.84 kg/acre, and mean absolute percentage error of 7.1%. The constructed model can be used to effectively predict the yield of specific varieties to enable the selection of good varieties in specific areas, such as a district or county. A prediction of the yield of a specific maize cultivar in a particular planting environment can have considerable value. It can facilitate the objective identification of better adapting cultivars among farmers and support the precise promotion and planting of cultivars. [ABSTRACT FROM AUTHOR]

Forecasting concentrations of PM 2.5 is important due to its known impacts on public health and environment. However, PM 2.5 concentrations can vary significantly over short distances and time, which can be influenced by local emissions and short-term weather patterns. This spatiotemporal variability makes accurate PM 2.5 forecasting an inherently complex and challenging task. This study presented novel methodologies for short-term PM 2.5 concentration forecast by combining the atmospheric chemistry transport model Community Multiscale Air Quality Modeling System (CMAQ) with data-driven machine learning methods, namely long short-term memory (LSTM) and random forest (RF) models. The combined model system forecast PM 2.5 with 1 h, 1km × 1 km spatiotemporal resolution. The LSTM system forecast time-dependent PM 2.5 concentrations at observation sites with a maximum root mean square error (RMSE) of 3.66 μg/m3 for 1-hr forecast and 23.75 μg/m3 for 72-hr forecast, leveraging results obtained from the atmospheric transport model with RMSE of 45.81 μg/m3. Wavelet transform in the LSTM system allowed learning and prediction of PM 2.5 concentrations at different frequencies, capturing temporal variability of PM 2.5 at various time scales. The RF model predicted distributions of PM 2.5 concentrations by learning LSTM results and integrating crucial features such as CMAQ results, meteorological and topographical information. The feature significance of CMAQ results was the highest among the input features in RF models. Overall, the hybrid model could help with managing and mitigating the adverse effects of air pollution by enabling informed decision-making at the individual, community and policy levels. [Display omitted] • Long Short-Term Memory networks accurately forecast PM 2.5 concentrations at observation sites. • Spatial resolution of predicted PM 2.5 concentrations was enhanced using a random forest approach. • Forecasting decomposed sub-sequences of observed PM 2. 5 concentrations improved prediction accuracy. [ABSTRACT FROM AUTHOR]

Global Sea Surface Temperature (SST) trends have garnered significant attention in several ocean-related domains, including global warming, marine biodiversity, and environmental protection. This involves having an accurate and efficient forecast of future SST to ensure early detection and response in time to these events. Deep learning algorithms have become popular in SST prediction recently, although directly obtaining optimal prediction results from historical observation data is not simple. In this paper, we propose STA-SST, a new deep learning approach for forecasting SST, by combining the temporal dependencies of SST using the Bidirectional Long Short-Term Memory (BiLSTM) model, spatial features extracted from the convolution layer, and relevant information from the attention mechanism. To assess how well the Attention-BiLSTM with convolution layer predicts SST, we conducted a case study in the Moroccan Sea, concentrating on five different areas. The proposed model was compared against alternative forecasting models, including LSTM, XGBoost, Support Vector Regression (SVR), and Random Forest (RF). The experimental results show that STA-STT produces noticeably the best prediction results and is a solid choice for field implementation. • STA-SST combines BiLSTM, Conv1D, and attention mechanism for accurate SST forecasting. • Spatial features enhance the prediction of time series data. • STA-SST outperforms traditional models in SST forecasting. [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]