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Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model's simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry. [ABSTRACT FROM AUTHOR]

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm−2y−1, and mean absolute error (MAE) = 180.4186 gCm−2y−1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs. [ABSTRACT FROM AUTHOR]

Land deformation is a severe environmental problem that is often caused by groundwater overexploitation. Traditional approaches, such as those based on ground leveling, are used as standard for monitoring land deformation, but they cannot collect enough information for land-deformation mapping. In this study, the time-series Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) was used as an improved method to identify land deformation in Cangzhou after the initiation of China's South-to-North Water Diversion Project (SNWDP). Machine learning (ML) models, including random forest and k-nearest neighbor, were used to determine the relationship between groundwater pressure and land deformation. The results showed that from 2018 to 2022, the deformation rate was up to –115 mm/year in Nanpi and Dongguang and varied between –57 and –26 mm/year in Qingxian and Cangxian. Land deformation after the SNWDP implementation was less than before. The ML models' results show that the accuracy of the random forest and k-nearest neighbor methods were 85 and 77%, respectively. Evaluation of the groundwater-level trend measured in six wells showed that after the SNWDP implementation, the groundwater pressure started to recover in Cangzhou, but a decline has been observed recently, particularly in 2022. The mean decrease in impurity (MDI) values demonstrates that aquifers IV and III contribute the most to land deformation in Cangzhou, with the highest MDI values of 33 and 26%, respectively. The study provides new insights into the evolution of regional land deformation, and the methods employed in this research can be adopted in other regions with similar conditions. [ABSTRACT FROM AUTHOR]

The classification of land use and land cover (LULC) from remotely sensed imagery in semi-arid Mediterranean areas is a challenging task due to the fragmentation of the landscape and the diversity of spatial patterns. Recently, the use of deep learning (DL) for image analysis has increased compared to commonly used machine learning (ML) methods. This paper compares the performance of four algorithms, Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Network (CNN), using multi-source data, applying an exhaustive optimisation process of the hyperparameters. The usual approach in the optimisation process of a LULC classification model is to keep the best model in terms of accuracy without analysing the rest of the results. In this study, we have analysed such results, discovering noteworthy patterns in a space defined by the mean and standard deviation of the validation accuracy estimated in a 10-fold cross validation (CV). The point distributions in such a space do not appear to be completely random, but show clusters of points that facilitate the discovery of hyperparameter values that tend to increase the mean accuracy and decrease its standard deviation. RF is not the most accurate model, but it is the less sensitive to changes in hyperparameters. Neural Networks, tend to increase commission and omission errors of the less represented classes because their optimisation lead the model to learn better the most frequent classes. On the other hand, RF and MLP prediction layers are the most accurate from a general qualitative point of view. [ABSTRACT FROM AUTHOR]

The removal of reactive red M-2BE dye textile from aqueous solution was performed using multi-walled carbon nanotubes (MWCN) and powdered activated carbon (PAC). Kinetic adsorption modeling has been performed using machine learning (ML) algorithms of artificial neural networks, adaptive-neuro fuzzy inference system (ANFIS), random forest, gradient boosting, and support vector machine. Although ML models are more accurate, they often fail to interpret the reasoning behind predictions. Therefore, the SHapley Additive exPlanations (SHAP) were used to understand the effect of each feature on the adsorption capacity. The ANFIS has presented the best statistical metrics with R = 0.9993 , R M S E = 0.0214 , and S A E = 7.1172 . A higher adsorption capacity was observed for MWCN compared to PAC; while the first peaked at 300 mg L−1, the second approached 230 mg L−1. Temperature was found to have the smallest contribution in describing adsorption capacity. This novel application of ML with SHAP can provide important insights for adsorption researchers. [ABSTRACT FROM AUTHOR]

At present, the majority of techniques for assessing image quality are limited to extracting features from an image in a single space. This paper proposes a new dual-space multi-feature fusion based method for full-reference image quality assessment. This method involves simultaneously extracting features from both the YIQ and L*a*b* color spaces. First, we extract the luminance, slope, chroma, and gradient features in the spatial domain of the image to describe the salient differences in the image. Second, based on contrast sensitivity characteristics, we extract spatial frequency features in the spatial domain of the image to represent frequency differences in the image. Next, merge the features extracted in the dual space to construct a quality perception feature vector. Finally, the feature vector is input into the Random Forest model for regression prediction to obtain the predicted score of the image. Many experiments have been carried out on the four public datasets, and contrasted with other methods. The experimental confirm that the proposed method predicts image quality more accurately. The MATLAB source code and dataset of this paper will be published on GitHub, and the corresponding author can be contacted if necessary. [ABSTRACT FROM AUTHOR]

Large-scale digital soil maps are essential for rational and sustainable land management as well as accurate fertilizer application. This study focuses on digital mapping of soil properties, namely soil organic carbon (SOC), pH, nitrogen (N), phosphorus (P), and potassium (K) in Chernozem topsoil and subsoil. The study was conducted on two arable fields in the Cis-Ural forest-steppe zone of the Republic of Bashkortostan (Russia). The random forest algorithm in combination with terrain attributes and Sentinel-2A satellite data was applied for spatial prediction of soil properties. The root-mean-square error (RMSE) and coefficient of determination (R2) were used to determine the model performance. According to the Pearson correlation, a significant positive relationship between SOC and N content was found at all sites and depths (R = 0.76–0.92). A cross-validation revealed that SOC (R2 = 0.22–0.62, RMSE = 0.35–0.89%) and N (R2 = 0.16–0.60, RMSE = 21.11–36.6 mg kg−1) were best predicted among other properties at all depths using remote sensing data, whereas the performance of predictive models decreased with depth. However, a relationship between the content of some soil properties and their spatial distribution at study depths was observed, which can be used as an additional explanatory variable. We suppose that digital mapping of soil properties at the arable field scale should not be limited to topographic and remote sensing variables. Based on this information, the use of auxiliary variables, such as collocated soil information in combination with relief and remote sensing data can be effective in more accurately estimating the spatial distribution of properties across arable fields at different depths. Overall, this study provides valuable insights into spatial modelling of the vertical distribution of soil properties, highlighting the significance of remote sensing data at the arable field scale. The findings can be valuable for land managers, agronomists, and policymakers seeking sustainable land management practices and efficient fertilizer application, as well as for developing further mapping procedures for arable fields. [ABSTRACT FROM AUTHOR]

Early monitoring of within-field yield variability and forecasting yield potential is critical for farmers and other key stakeholders such as policymakers. Remote sensing techniques are progressively being used in yield prediction studies due to easy access and affordability. Despite the increasing use of remote sensing techniques for yield prediction in agriculture, there is still a need for medium-resolution satellite imagery when predicting canola yield using a combination of crop and soil information. In this study, we investigated the utility of remotely sensed flowering information from PlanetScope (at 4 m) satellite imagery combined with derived soil and topography parameters to predict canola yield. Our yield prediction model was trained and validated using data from 21 fields managed under variable rate seed and fertilizer application, including cleaned harvester yield maps, soil, and topography maps. To quantify the flowering intensity of canola, 9 vegetation indices (VIs) were calculated using spectral bands from PlanetScope imagery acquired for the reproductive stages of canola. We created five random forest regression models using different subsets of covariates, including VIs, soil, and topography features, to predict canola yield within the season. Using a random forest regression algorithm, we recorded accuracies ranging from poor to best performing using coefficient of determination and root mean squared error (R2: 0.47 to 0.66, RMSE: 325 to 399 kg ha−1). The optimal subset of covariates identified electrical conductivity (EC), Normalized Difference Yellowness Index, and Canola Index as the key variables explaining within-spatial variability in canola yield. Our final model exhibited a validation R2 of 0.46 (RMSE = 730 kg ha−1), demonstrating the potential of medium-resolution satellite imagery during the flowering stage to detect and quantify sub-field spatial and temporal floral phenology changes when predicting canola yield. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent model inversion attacks and to protect intellectual property. The privacy-preserving evaluation of ML models is possible through multi-key homomorphic encryption (MKHE), that allows both the client data and the model to be encrypted under different keys. In this paper, we propose an MKHE evaluation method for decision trees and we extend the proposed method for random forests. Each decision tree is evaluated as a single lookup table, and voting is performed at the level of groups of decision trees in the random forest. We provide both theoretical and experimental evaluations for the proposed method. The aim is to minimize the performance degradation introduced by the encrypted model compared to a plaintext model while also obtaining practical classification times. In our experiments with the proposed MKHE random forest evaluation method, we obtained minimal (less than 0.6%) impact on the main ML performance metrics considered for each scenario, while also achieving reasonable classification times (of the order of seconds). [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has garnered considerable attention from academic and industrial circles as a pivotal technology in recent years. The escalation of security risks is observed to be associated with the growing interest in IoT applications. Intrusion detection systems (IDS) have been devised as viable instruments for identifying and averting malicious actions in this context. Several techniques described in academic papers are thought to be very accurate, but they cannot be used in the real world because the datasets used to build and test the models do not accurately reflect and simulate the IoT network. Existing methods, on the other hand, deal with these issues, but they are not good enough for commercial use because of their lack of precision, low detection rate, receiver operating characteristic (ROC), and false acceptance rate (FAR). The effectiveness of these solutions is predominantly dependent on individual learners and is consequently influenced by the inherent limitations of each learning algorithm. This study introduces a new approach for detecting intrusion attacks in an IoT network, which involves the use of an ensemble learning technique based on gray wolf optimizer (GWO). The novelty of this study lies in the proposed voting gray wolf optimizer (GWO) ensemble model, which incorporates two crucial components: a traffic analyzer and a classification phase engine. The model employs a voting technique to combine the probability averages of the base learners. Secondly, the combination of feature selection and feature extraction techniques is to reduce dimensionality. Thirdly, the utilization of GWO is employed to optimize the parameters of ensemble models. Similarly, the approach employs the most authentic intrusion detection datasets that are accessible and amalgamates multiple learners to generate ensemble learners. The hybridization of information gain (IG) and principal component analysis (PCA) was employed to reduce dimensionality. The study utilized a novel GWO ensemble learning approach that incorporated a decision tree, random forest, K-nearest neighbor, and multilayer perceptron for classification. To evaluate the efficacy of the proposed model, two authentic datasets, namely, BoT-IoT and UNSW-NB15, were scrutinized. The GWO-optimized ensemble model demonstrates superior accuracy when compared to other machine learning-based and deep learning models. Specifically, the model achieves an accuracy rate of 99.98%, a DR of 99.97%, a precision rate of 99.94%, an ROC rate of 99.99%, and an FAR rate of 1.30 on the BoT-IoT dataset. According to the experimental results, the proposed ensemble model optimized by GWO achieved an accuracy of 100%, a DR of 99.9%, a precision of 99.59%, an ROC of 99.40%, and an FAR of 1.5 when tested on the UNSW-NB15 dataset. [ABSTRACT FROM AUTHOR]

Background: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia. Methods: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography–mass spectrometry (LC–MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model. Results: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16–18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0). Conclusions: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis. [ABSTRACT FROM AUTHOR]

We propose a random forest based estimation procedure for Quantile-Oriented Sensitivity Analysis—QOSA. In order to be efficient, a cross-validation step on the leaf size of trees is required. Our full estimation procedure is tested on both simulated data and a real dataset. Our estimators use either the bootstrap samples or the original sample in the estimation. Also, they are either based on a quantile plug-in procedure (the R-estimators) or on a direct minimization (the Q-estimators). This leads to 8 different estimators which are compared on simulations. From these simulations, it seems that the estimation method based on a direct minimization is better than the one plugging the quantile. This is a significant result because the method with direct minimization requires only one sample and could therefore be preferred. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of the feature input vector on the performance of dissolved gas analysis-based intelligent power transformer fault diagnosis methods. For this purpose, 22 feature vectors from traditional diagnostic methods were used as feature input vectors for four tree-based ensemble algorithms, namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree. To build the proposed diagnostics models, 407 samples were used for training and testing. For validation and comparison with the existing methods of literature, 89 samples were used. Based on the results obtained on the training and testing datasets, the best performance was achieved with feature vector 16, which consists of the gas ratios of Rogers' four ratios method and the three ratios technique. The test accuracies based on these vectors are 98.37, 96.75, 95.93, and 97.56% for the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree algorithms, respectively. Furthermore, the performance of the methods based on best input feature was evaluated and compared with other methods of literature such as Duval triangle, modified Rogers' four ratios method, combined technique, three ratios technique, Gouda triangle, IEC 60599, NBR 7274, the clustering method, and key gases with gas ratio methods. These methods suffer from unreliability, and this is the motivation behind the current work to develop a new technique that enhances the diagnostic accuracy of transformer faults to avoid unwanted faults and outages from the network. On validating dataset, diagnostic accuracies of 92.13, 91.01, 89.89, and 91.01% were achieved by the namely random forest, tree ensemble, gradient boosted tree, and extreme gradient tree models, respectively. These diagnostic accuracies are higher than 83.15% for the clustering method, 82.02% for the combined technique, 80.90% for the modified IEC 60599, and 79.78% for key gases with gas ratios, which are the best existing methods. Even if the performance of dissolved gas analysis-based intelligent methods depends strongly on the shape of the feature vector used, this study provides scholars with a tool for choosing the feature vector to use when implementing these methods. [ABSTRACT FROM AUTHOR]

The development of the smart grid (SG) offers a way to improve the generation of electrical energy as well as the corresponding transmission and distribution. Due to the versatile nature, the installing of SG consumes less area and time when compared to traditional grids. The major aim of SG is to provide controllability of assets and grid observability and enhance the security and performance of power system. An integration of internet of things (IoT) in smart grid ensures intelligent features with reduced human intervention, cost-effectiveness, and reliability. In order to communicate the useful information with the internet and other web applications, IoT-enabled sensors are widely utilized in the power grid system, which enables better grid management. A photovoltaic (PV) system and a high-gain integrated Luo converter are included in the IoT-based power monitoring system for the smart grid that is recommended as a result of these variables. The maximum solar power is tracked with a help of grey wolf optimized artificial neural network (GWO-ANN) which aids in enhanced operation of converter. The obtained converter output is applied to 3 ϕ grid through a 3 ϕ voltage source inverter (VSI). The parameters like temperature, intensity, converter voltage and current, and grid voltage and current are monitored by sensors and further stored in IoT webpage. The random forest (RF) classifier is used for the prediction of shortest path through which the monitored data are transmitted. The whole set-up contributes an improved monitoring of smart grid parameters which is proved by the generated results. [ABSTRACT FROM AUTHOR]

Hyperspectral data has a significant count of spectral channels with an enhanced spectral resolution, which provides detailed information at each pixel. This data can be used in numerous remote sensing (RS) applications, along with mineral mapping. Mineral mapping is an important component of geological mapping, which helps in investigating the mineralization potential of an area. This work can be completed effectively by applying machine learning (ML) techniques to RS data. This paper proposes a stacked ensemble-based framework for mineral mapping using the dataset obtained by the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG). The study area is situated in Jahazpur, Rajasthan, India. The purpose of this stacked ensemble-based model is to enhance the performance of ML-based mineral mapping. The proposed stacked ensemble model consists of two major elements: a base learner (Naïve Bayes, KNN, artificial neural network, decision tree, and support vector machine) and a stacked learner (random forest). The results of the experiments show that the stacked ensemble-based model has a lot of potential for accurately mapping the minerals talc, montmorillonite, kaolionite, and kaosmec. The proposed model has obtained an overall accuracy of 98.96%, an average accuracy of 98.21%, and a Kappa coefficient of 0.9628. Research highlights: A stacked ensemble-based model for mineral mapping is proposed. The well-known five conventional machine learning models (called base models) are investigated for mineral mapping. The performance of the proposed model is evaluated on the AVIRIG–NG dataset. The study area is situated in Jahazpur, Rajasthan, India. The proposed method outperformed all base models. [ABSTRACT FROM AUTHOR]

In order to estimate the likelihood of 1, 3, 6 and 12 month mortality in patients with hip fractures, we applied a variety of machine learning methods using readily available, preoperative data. We used prospectively collected data from a single university hospital in Copenhagen, Denmark for consecutive patients with hip fractures, aged 60 years and older, treated between September 2008 to September 2010 (n = 1186). Preoperative biochemical and anamnestic data were used as predictors and outcome was survival at 1, 3, 6 and 12 months after the fracture. After feature selection for each timepoint a stratified split was done (70/30) before training and validating Random Forest models, extreme gradient boosting (XGB) and Generalized Linear Models. We evaluated and compared each model using receiver operator characteristic (ROC), calibration slope and intercept, Spiegelhalter's z- test and Decision Curve Analysis. Using combinations of between 10 and 13 anamnestic and biochemical parameters we were able to successfully estimate the likelihood of mortality with an area under the curve on ROC curves of 0.79, 0.80, 0.79 and 0.81 for 1, 3, 6 and 12 month, respectively. The XGB was the overall best calibrated and most promising model. The XGB model most successfully estimated the likelihood of mortality postoperatively. An easy-to-use model could be helpful in perioperative decisions concerning level of care, focused research and information to patients. External validation is necessary before widespread use and is currently underway, an online tool has been developed for educational/experimental purposes (https://hipfx.shinyapps.io/hipfx/). [ABSTRACT FROM AUTHOR]

With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924; PREA = 0.0.9999976; RECA = 0.999775; F1A = 0.999876; Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency. [ABSTRACT FROM AUTHOR]

In this work, we used the convolutional neural network (CNN) method to invert sea surface height (SSH) from the Global Navigation Satellite System (GNSS) delayed Doppler map (DDM) data during 2009–2017 and compared the CNN inversion data with those obtained from traditional simple random forest (RF) method. SSH observations from the OSTM/Jason-2 satellite were used to judge the merits of the two methods. The results show that both methods yield good SSH inversion results, but when the training set is 9000, the root mean square errors of the SSH inversion results based on the CNN and the RF method are 16.78 and 15.96 respectively; as the training set increases above 9000, the accuracy of the CNN method is significantly better than that of the RF method. This suggests that SSH inversion based on the CNN method will become more advantageous as more data become available. [ABSTRACT FROM AUTHOR]

Traditional rainfall-runoff modeling techniques require large datasets and often an exhaustive calibration process, which is challenging, especially in poorly-gauged basins and resource-limited settings. Therefore, it is necessary to examine new ways of constructing predictive models for runoff that can achieve satisfactory results, while also minimizing the data requirement and model construction time. In this study, the effectiveness of integrating the Random Forest (RF) as an important feature identifier with novel gradient boosted trees to achieve satisfactory results was examined for two adjacent catchments in Vietnam. Antecedent daily runoff in combination with daily and one-day antecedent rainfall was found to significantly influence the runoff at the outlet of the catchments. Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) were effective in predicting day-ahead runoff. For instance, CatBoost with NSE, d, r, and R2 values of 0.92, 0.98, 0.96, and 0.92, respectively, and XGBoost with NSE, d, r, and R2 values of 0.91, 0.98, 0.96, and 0.92, respectively, are well suited for predicting runoff. A comparative analysis of their results with previous studies revealed that the models were very effective since they were able to better reduce generalization errors at different calibration and validation phases. This study presents the integration of RF and gradient boosted trees as a simplified alternative to computationally expensive and data-intensive physically-based rainfall-runoff models. The practitioners can build upon the experimentation presented in this study to minimize the computational time requirement, construction process complexity, and data requirement, which are often serious constraints in physically-based rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

This study estimates both hourly and daily Downward Surface Solar Radiation (SSR) in Istanbul while determining the importance of variables on SSR using tree-based machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosted Regression Tree (GBRT). The hourly and daily data of climatic factors for the period between January 2016 and December 2020 are gathered from the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. In addition to the meteorology data, hourly data of selected aerosols are obtained from the Ministry of Environment, Urbanization and Climate Change. Temperature, cloud coverage, ozone level, precipitation, pressure, and two components of wind speeds, PM10, PM2.5, and SO2 are utilized to train and test the established models. The model performances are determined with the out-of-bag errors by calculating R-squared, MSE, RMSE, and MBE. The GBRT model is found to be the most accurate model with the lowest error rates. Furthermore, this study provides the variable importance in determining the SSR. Although all models provide different values for the variable importance; temperature, ozone level, cloud coverage, and precipitation are found to be the most important variables in estimating daily SSR. For the hourly estimation, the time of day (hour) becomes the most important factor in addition to temperature, ozone level, and cloud coverage. Finally, this study shows that the tree-based machine learning methods used with these variables to estimate hourly and daily SSR results are very accurate when it is not possible to measure the SSR values directly. [ABSTRACT FROM AUTHOR]

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes. [ABSTRACT FROM AUTHOR]

Human activity recognition technology has received much academic attention in recent years and plays a vital role in a wide range of applications such as smart healthcare and security surveillance. A fall detection scheme based on Channel State Information (CSI) is designed to overcome the limitations of traditional fall detection systems such as low privacy, high cost and poor cross-domain capability. Based on the data collected by commercial WiFi devices, the scheme analyzes the changes of indoor CSI caused by human activities and realizes the non-contact fall detection. Firstly, Discrete Wavelet Transform (DWT), phase difference analysis and Moving Average Filter (MAF) are combined to reduce the impact of environmental noise on the detection performance. Secondly, the variance of CSI amplitude and phase difference is calculated as an indicator for selecting subcarriers and the moving variance is used to segment the active interval of the selected subcarriers, which can reduce the data dimensionality and extract the time-frequency features. Finally, the Genetic Algorithm (GA) is used to optimize the parameter selection process of the Random Forest (RF) to improve the performance of the classifier model. Through comparison experiments on the DARMS dataset, it is verified that the proposed scheme can effectively improve the accuracy of fall activity recognition and maintain an accuracy of over 95.25 % when the training environment changes. [ABSTRACT FROM AUTHOR]

Rapid and accurate estimation of rock strength is of great practical significance for the stability of rock engineering. This article is based on the results of triaxial compression tests on crack rocks and comprehensively considers the interrelationships between crack geometric characteristics, confining pressure, and physical and mechanical parameters. A database of rock strength characteristic parameters from an experimental perspective is constructed, and a quantitative prediction model for the strength of crack rock masses under the influence of multiple characteristic parameters is established using the random forest algorithm. The results show that the accuracy of predicting rock strength in categories 2 of 21–30 MPa, 3 of 31–40 MPa, 4 of 41–50 MPa, and 5 of 51–60 MPa is 100%, and the accuracy of predicting rock strength in categories 1 of 11–20 MPa is 82% and in categories 0 of 0–10 MPa is 74%; through the study of parameter correlation and quantitative analysis with strength, the selected parameters are reliable and have a certain degree of correlation, and the influence mechanism of characteristic parameters on rock strength has been explored; the calculation results of parameter classification show that confining pressure and crack length are the key factors affecting rock strength. The importance of other parameters is ranked in the order of longitudinal wave velocity > Poisson’s ratio > saturated water content > porosity > saturated quality > dry quality > saturated density > crack dip angle > crack number > crack penetration. This provides an effective approach to consider the randomness and correlation of rock strength parameters, providing a new idea for rock mechanics research. [ABSTRACT FROM AUTHOR]

Applications for machine learning (ML), deep learning, and other artificial intelligence models have shown great promise in nuclear physics, including not only in classification systems but also in the analysis of numerical data. This study used various ML algorithms to estimate the concentrations of six rare earth elements (Sm, La, Ce, Sc, Eu, and Tb) in both archaeological and marine sediment samples. An interesting aspect of this analysis is that 80% of the 235 data points were used for training data, which included two parameters: specific activity ( A sp ) and concentration (ρ ) by the k0-method for the purpose of model development. The remaining 20% of the dataset was held out for testing the model's accuracy. The fundamental principle of this approach is the use of regression analysis between A sp and ρ to construct a machine learning regression model. This machine learning model was subsequently applied to estimate element concentrations based on A sp values obtained from gamma spectra. The mean absolute error (MAE), root mean square error (RMSE) and the statistical measure R-squared (R2) were employed for evaluating the accuracy of the predicted models. The random forest (RF) algorithm produces smaller MAE and RMSE values and achieves better R2 values compared to other algorithms. In addition, RF shows the lowest relative bias of the concentration values of elements in reference material (NIST 2711a) compared to other prediction models. The work focuses on demonstrating that machine learning models can effectively predict the concentrations of rare earth elements, even though this is a fundamental issue in NAA and one previous study has addressed this issue for one single element. The extension of the current work and potential directions for further development will be presented in the results and discussion section. [ABSTRACT FROM AUTHOR]

The estimation of disc cutter wear (CW) remains a complex problem in mechanized tunneling using tunnel boring machines (TBM), despite the development of numerous TBM performance models. This research aimed to estimate the cutter life index (CLI) as an index to predict the CW by developing predictive models based on two machine learning algorithms, namely gradient boosting (GB) and random forest (RF), optimized by three optimization techniques: particle swarm optimization (PSO), differential evolution (DE), and simulated annealing (SA). To gain the aim, a dataset consisting of four rock parameters—density (ρ), uniaxial compressive strength, Brazilian tensile strength (BTS), and brittleness index—with 80 mechanized tunnel cases for each parameter has been utilized by obtaining the sample and then relevant tests on them were conducted in the laboratory. First, various parameter selection methods, such as mutual information, have been employed to reduce the dimensionality of the problem, and it has been revealed that ρ and BTS have been the most influential parameters to estimate the CLI. Then, by developing six optimized models, including GB-PSO, GB-DE, GB-SA, RF-PSO, RF-DE, and RF-SA, using the two mentioned parameters, their performance has been assessed via three performance evaluation indices of coefficient of determination (r2), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results showed that among six predictive models, the two models of GB-SA (with r2, RMSE, and MAPE of 0.8274, 10.8329, and 0.3957, respectively) and RF-PSO (with r2, RMSE, and MAPE of 0.8213, 11.0249, and 0.4113, respectively) outperformed the other models, with 82.74% accuracy of GB-SA and with 82.13% accuracy of the RF-PSO, and the two can be utilized to estimate disc cutter via CLI for different type of rock in the range of established dataset. [ABSTRACT FROM AUTHOR]

Tropical forests are the world's most structurally complex ecosystems, providing key functions like biomass accumulation, which is linked to this complexity. Tropical forests are also exposed to chronic, non-severe winds, yet their effect on forest structural complexity is understudied. Here we examine drivers of forest structural complexity in Puerto Rico with a particular focus on chronic wind exposure. We used airborne light detection and ranging data collected in 2016 to quantify canopy height and rugosity (variation in height) in ~ 20,000, 0.28 ha forested sites stratified by forest age. We used random forest models to analyze variation in canopy height and rugosity as a function of chronic wind exposure, forest age, mean annual precipitation, elevation, slope (in degrees), soil type, soil available water storage, and exposure to a previous hurricane. Canopy height was driven by precipitation, forest age, and chronic wind exposure, decreasing by 2.12 m (16%) on average in wind-exposed forests across all forest ages. Canopy height increased by 4.0 m (41%) on average in forests aged 25–66 years, and by 4.0 m between sites with 1000 and 2000 mm y−1 precipitation. Canopy rugosity was driven by canopy height, precipitation, forest age, and elevation, increasing log-linearly with canopy height and precipitation, decreasing with elevation, and was highest in younger forests. Chronic wind exposure did not drive variation in canopy rugosity. Our results suggest that chronic wind exposure plays an integral role in limiting canopy height, potentially reducing aboveground carbon accumulation in older tropical forests. [ABSTRACT FROM AUTHOR]

The Random Forest (RF) and K nearest neighbors (KNN) machine learning (ML) algorithms were evaluated for their ability to predict ophiolite occurrences, in the East Vardar Zone (EVZ) of central North Macedonia. A predictive map of the investigated area was created using three data sources: geophysical data (digital elevation model, gravity and geomagnetic), multispectral optical satellite images (Landsat 7 ETM + and their derivatives), and geological data (distance to fault map and ophiolite outcrops map). The research included a comparison and discussion on the statistical and geological findings derived from different training dataset class ratios in relation to a testing dataset characterized by significant class imbalance. The results suggest that the precise selection of a suitable class balance for the training dataset is a critical factor in achieving accurate ophiolite prediction with RF and KNN algorithms. The analysis of feature importance revealed that the Bouguer gravity anomaly map, total intensity of the Earth's magnetic field reduced to the pole map, distance to fault map, band ratio BR3 map obtained from multispectral satellite images, and digital elevation model are the most significant features for predicting ophiolites within the EVZ. KNN showed poorer results compared to RF in terms of both the evaluation metrics and visual analysis of prediction maps. The methods applied in this research can be applied for predictive mapping of complex geo-tectonic units covered by dense vegetation, and may indicate the presence of these units even if they were not previously mapped, particularly when geophysical data are used as features. [ABSTRACT FROM AUTHOR]

This study explores the application of machine learning algorithms for the prediction of pan evaporation (Ep), which is a critical factor in water resource management for the assessment of water demand and usage. Specifically, this research evaluates the effectiveness of two base models: Random Forest (RF) and Multi-Layer Perceptron (MLP) and their optimized counterparts using a Genetic Algorithm (GA), designated as GA-RF and GA-MLP, for modeling Ep at a target station using data from adjacent stations. The datasets were split into a training set (70%) and a testing set (30%). The models' performances were judged using three statistical measures: Correlation Coefficient (CC), Scattered Index (SI), and Willmott's Index of agreement (WI). The enhanced models, particularly GA-MLP-5, showed superior performance with a CC of 0.8704, SI of 0.2539, and WI of 0.9212, indicating the potent ability of GA to refine RF and MLP models for predictive accuracy. Additionally, sensitivity analysis via the GA-RF indicates the varying influence of Ep from neighboring stations on the target station, shedding light on key predictors for effective water management. Conclusively, this study demonstrates that the hybrid models have significant potential in accurate Ep estimation and can be expanded to predict other meteorological variables, offering valuable tools for water resource management strategies. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) is a major measure for disaster prevention and control. This paper proposes an incremental learning random forest model that uses random forests for incremental training of landslides. The 1652 landslides in Ganzhou, China from year 2015 to 2020 were divided into three equal parts (T1, T2 and T3) considering time series. And the data of T1 part is used to construct incremental learning random forest model ILRF_1, T2 and T3 part as the new part of incremental training. Landslide susceptibility maps were plotted using ILRF, batch learning random forests (BLRF), batch learning support vector machines (BLSVM), respectively. And the accuracy is verified using the receiver operating curve (ROC). The results showed that the ILRF was the most promising model(AUC = 0.937) for the LSM, followed by BLRF(AUC = 0.905), BLSVM(AUC = 0.865). ILRF has better predictive ability in the susceptibility evaluation of this region. It is very high-prone area accounts for the smallest proportion, mainly concentrated in the mountain front area in the northeast of Ganzhou City. The multi-year average rainfall, population density, and normalized difference vegetation index are the first three important factors that control the spatial possibility of landslides. [ABSTRACT FROM AUTHOR]

Genomic prediction in Coffee breeding has shown good potential in predictive ability (PA), genetic gains and reduction of the selection cycle time. It is known that the cost of genotyping was prohibitive for many species, and their value is associated with the density markers panel used. The use of optimize marker density panel may reduce the genotyping cost and improve the PA. We aimed to evaluate the trade-off between density marker panels size and the PA for eight agronomic traits in Coffea canephora using machine learning algorithms. These approaches were compared with BLASSO method. The used data consisted of 165 genotypes of C. canephora genotyped with 14,387 SNP markers. The plants were phenotyped for vegetative vigor (Vig), rust (Rus) and cercosporiose incidence (Cer), fruit maturation time (Mat), fruit size (FS), plant height (PH), diameter of the canopy projection (DC) and yield (Y). Twelve different density marker panels were used. The common trend observed in the analysis shows an increase of the PA as the number of markers decreases, having a peak when used between 500 and 1,000 markers. Comparing the best and the worse results (full SNP panel density) for each trait, some had an improvement around of 100% (PH: 0.35–0.77; Cer: 0.40–0.84; DC: 0.39–0.82; Rus: 0.39–0.83, Vig: 0.40–0.77), the other showed an improvement more than 340% (Mat: 0.12–0.60; Y: 0.14–0.61; FS: 0.07–0.60). The results of the current study indicate that the reduction of the number of markers can improve the selection of individuals at a lower cost. [ABSTRACT FROM AUTHOR]

Existing research on non-suicidal self-injury (NSSI) among adolescents has primarily concentrated on general risk factors, leaving a significant gap in understanding the specific NSSI characteristics that predict diverse psychopathological outcomes. This study aims to address this gap by using Random Forests to discern the significant predictors of different clinical outcomes. The study tracked 348 adolescents (64.7% girls; mean age = 13.31, SD = 0.91) over 6 months. Initially, 46 characteristics of NSSI were evaluated for their potential to predict the repetition of NSSI, as well as depression, anxiety, and suicidal risks at a follow-up (T2). The findings revealed distinct predictors for each psychopathology. Specifically, psychological pain was identified as a significant predictor for depression, anxiety, and suicidal risks, while the perceived effectiveness of NSSI was crucial in forecasting its repetition. These findings imply that it is feasible to identify high-risk individuals by assessing key NSSI characteristics, and also highlight the importance of considering diverse NSSI characteristics when working with self-injurers. [ABSTRACT FROM AUTHOR]

Multi-material stacks comprising composite and metallic layers are widely used in aerospace components. For the assembly of structural part, high-quality holes are required to ensure the performance of the mechanical fastening (rivet/bolt). On the final assembly lines of aircraft structures, these machining operations are often performed using an electric automated drilling unit (eADU). When drilling hybrid stacks, the difference in machinability of the materials (CFRP, titanium alloy, and aluminum alloy) makes it difficult to avoid delamination, fibre pullout, matrix degradation, burrs, roughness, and size defects. Therefore, each material must be drilled with suitable machining conditions to meet the demanding quality requirements. To this end, automated material detection would allow for the adjustment of appropriate cutting parameters for each material. Due to the numerous stack configurations (material, thickness) and the variability of process parameters (tool geometry, cutting conditions, lubrication, etc.), automated material detection is not an easy task. To address this issue in eADU applications, this paper presents a novel approach to identify during the process the drilled material being manufactured using a random forest (RF) machine learning model and multi-sensor data fusion. Cutting forces, vibration, micro-lubrication conditions (flow rate and pressure), and eADU spindle and feed motor currents are monitored on a dedicated drilling test rig. Numerous tests were performed on Al7175/CFRP stacks with different cutting conditions to validate the proposed methodology. Advanced signal processing and analysis in the time and frequency domains are used for feature extraction to identify Al7175 from the CFRP. The input features of the RF model were selected using the feature importance measure embedded in the RF model learning process. The knowledge process of drilling multiple material stacks is also considered. The results showed that the features extracted from the frequency domain are more effective in identifying the CFRP Al7175 than those extracted from the time domain. This optimal subset was then used to build the RF model. The proposed methodology resulted in a highly accurate classification allowing the implementation of an adaptive machining process on the eADU for hybrid stack drilling. [ABSTRACT FROM AUTHOR]

The prediction of potential protein–protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein–protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein–protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein–protein interactions prediction. MultiPPIs is free available at . [ABSTRACT FROM AUTHOR]

Criminal activities generate an estimated $2 trillion in laundered money per year, highlighting the need for financial institutions to detect and report suspicious activity to protect their reputation. However, rule-based models commonly used for this purpose generate a high number of false positives, draining compliance team time, and increasing investigation costs. However, the application of machine learning in conjunction with rule-based models presents noteworthy implications, encompassing the potential reduction in false positives and the concomitant risk of machine learning inadvertently suppressing true positive alerts. This paper proposes a framework called automatic suppression based on XGBoost for anti-money laundering (ASXAML) to enhance detection by reducing false positives. ASXAML leverages recursive feature elimination with cross-validation for optimal feature selection. Subsequently, Optuna is employed to fine-tune hyperparameters for the XGBoost model. Results indicate that ASXAML achieves an optimal balance between reducing false positives and avoiding missed money laundering events, with an 86% F-beta score and only 11% money laundering customers were incorrectly closed out of 1926 in the test data. [ABSTRACT FROM AUTHOR]

Procambarus clarkii is a notorious invasive species that has led to ecological concerns owing to its high viability and rapid reproduction. South Korea, a country exposed to a high risk of introduction of invasive species due to active international trade, has suffered from recent massive invasions by invasive species, necessitating the evaluation of potential areas requiring intensive monitoring. In this study, we developed two different types of species distribution models, CLIMEX and random forest, for P. clarkii using occurrence records from the United States. The potential distribution in the United States was predicted along coastal lines and inland regions located below 40°N latitude The model was then applied to evaluate the potential distribution in South Korea, and an ensemble map was constructed to identify the most vulnerable domestic regions. According to both models, the domestic potential distribution was highest in most areas located at low altitudes. In the ensemble model, most of the low-altitude western regions, the eastern coast, and some southern inland regions were predicted to be suitable for the distribution of P. clarkii, and a similar distribution pattern was predicted when the model was projected into the future climate. Through this study, it is possible to secure basic data that can be used for the early monitoring of the introduction and subsequent distribution of P. clarkii. [ABSTRACT FROM AUTHOR]

In recent years, coal mine has applied shield tunneling machines to roadway excavation to improve production efficiency. Geological condition is an important factor that determines the efficiency of shield machines. The shield machine is most favorable for medium to hard surrounding rocks such as limestone and sandstone. Therefore, the lithology prediction of the location of a planned excavation roadway becomes the core issue in improving the efficiency of the shield machine. At present, seismic inversion is an essential method for lithology prediction. However, in Yangquan Xinjing area, missing P-wave logging curves affects the impedance inversion. Therefore, using existing logging curves to generate missing P-wave logging curves and using sandstone exposure data to continuously update lithology distribution prediction results are of great interest. In this study, logging curves were first pretreated by standardization to ensure the inversion effect. Because of the missing acoustic logging curves, the random forest regression algorithm was introduced using density, natural gamma, apparent resistivity, and spontaneous potential curves as characteristic variables to establish a curve regression prediction model. Then, P-wave logging curves were acquired. After a full analysis of the principles of acoustic and gamma curves, a quasi-acoustic curve is constructed, and a quasi-acoustic inversion was performed. The top and bottom interfaces of the K7 sandstone were interpreted on the inversion data body. The interpreted horizon information was converted from the time domain to the depth domain. The predicted results agreed well with the exposed data. At the same time, combined with the lithology exposure data from the shield tunneling machine, the distribution prediction of the K7 sand body in the target roadway section was updated and iterated many times, which provided effective guidance for the optimization of the tunneling route of the shield tunneling machine. [ABSTRACT FROM AUTHOR]

A current challenge of genetic breeding programs is to increase grain yield and protein content and at least maintain oil content. However, evaluations of industrial traits are time and cost-consuming. Thus, achieving accurate models for classifying genotypes with better industrial technological performance based on easier and faster to measure traits, such as agronomic ones, is of paramount importance for soybean breeding programs. The objective was to classify groups of soybean genotypes to industrial technological variables based on agronomic traits measured in the field using machine learning (ML) techniques. Field experiments were carried out in two sites in a randomized block design with two replications and 206 F2 soybean populations. Agronomic traits evaluated were: days to maturation (DM), first pod height (FPH), plant height (PH), number of branches (NB), main stem diameter (SD), mass of one hundred grains (MHG), and grain yield (GY). Industrial technological variables evaluated were oil yield, crude protein, crude fiber, and ash contents, determined by high-optical accuracy near-infrared spectroscopy (NIRS). The models tested were: support vector machine (SVM), artificial neural network (ANN), decision tree models J48 and REPTree, random forest (RF), and logistic regression (LR, used as control). A genotype clustering was performed using PCA and k-means algorithm, and then the clusters formed were used as output variables of the ML models, while the agronomic traits were used as input variables. ML techniques provided accurate models to classify soybean genotypes for more complex variables (industrial technological) based on agronomic traits. RF outperformed the other models and can be used to contribute to soybean breeding programs by classifying genotypes for industrial technological traits. [ABSTRACT FROM AUTHOR]

This paper addresses the interpretability problem of non-parametric option pricing models by using the explainable artificial intelligence (XAI) approach. We study call options written on the S&P 500 stock market index across three market regimes: pre-COVID-19, COVID-19 market crash, and post-COVID-19 recovery. Our comparative option pricing exercise demonstrates the superiority of the random forest and extreme gradient boosting models for each market regime. We also show that the model's pricing accuracy has worsened from the pre-COVID-19 to the recovery period. Moneyness was the most important price determinants across the market regimes, while the implied volatility and time-to-maturity inputs contributed intermittently to a lesser extent. During the COVID-19 crash, open interest gained more economic importance due to the increased behavioral tendencies of traders consistent with market distress. [ABSTRACT FROM AUTHOR]

Schistosomiasis, a prevalent water-borne disease second only to malaria, significantly impacts impoverished rural communities, primarily in Sub-Saharan Africa where over 90% of the severely affected population resides. The disease, majorly caused by Schistosoma mansoni and S. haematobium parasites, relies on freshwater snails, specifically Biomphalaria and Bulinus species, as crucial intermediate host (IH) snails. Targeted snail control is advisable, however, there is still limited knowledge about the community structure of the two genera especially in East Africa. Utilizing a machine learning approach, we employed random forest to identify key features influencing the distribution of both IH snails in this region. Our results reveal geography and climate as primary factors for Biomphalaria, while Bulinus occurrence is additionally influenced by soil clay content and nitrogen concentration. Favorable climate conditions indicate a high prevalence of IHs in East Africa, while the intricate connection with geography might signify either dispersal limitations or environmental filtering. Predicted probabilities demonstrate non-linear patterns, with Bulinus being more likely to occur than Biomphalaria in the region. This study provides foundational framework insights for targeted schistosomiasis prevention and control strategies in the region, assisting health workers and policymakers in their efforts. [ABSTRACT FROM AUTHOR]

The objective of this study is to promptly and accurately allocate resources, scientifically guide grain distribution, and enhance the precision of crop yield prediction (CYP), particularly for corn, along with ensuring application stability. The digital camera is selected to capture the digital image of a 60 m × 10 m experimental cornfield. Subsequently, the obtained data on corn yield and statistical growth serve as inputs for the multi-source information fusion (MSIF). The study proposes an MSIF-based CYP Random Forest model by amalgamating the fluctuating corn yield dataset. In relation to the spatial variability of the experimental cornfield, the fitting degree and prediction ability of the proposed MSIF-based CYP Random Forest are analyzed, with statistics collected from 1-hectare, 10-hectare, 20-hectare, 30-hectare, and 50-hectare experimental cornfields. Results indicate that the proposed MSIF-based CYP Random Forest model outperforms control models such as support vector machine (SVM) and Long Short-Term Memory (LSTM), achieving the highest prediction accuracy of 89.30%, surpassing SVM and LSTM by approximately 13.44%. Meanwhile, as the experimental field size increases, the proposed model demonstrates higher prediction accuracy, reaching a maximum of 98.71%. This study is anticipated to offer early warnings of potential factors affecting crop yields and to further advocate for the adoption of MSIF-based CYP. These findings hold significant research implications for personnel involved in Agricultural and Forestry Economic Management within the context of developing agricultural economy. [ABSTRACT FROM AUTHOR]

Protein–protein interactions are crucial for the entry of viruses into the cell. Understanding the mechanism of interactions is essential in studying human-virus association, developing new biologics and drug candidates, as well as viral infections and antiviral responses. Experimental methods to analyze human-virus protein–protein interactions based on protein sequence data are time-consuming and labor-intensive, so machine learning models are being developed to predict interactions and determine large-scale interactomes between species. The present work highlights the importance of sequence features in classifying interacting and non-interacting proteins from the protein sequence data. Higher dimensional amino acid sequence features such as Amino Acid Composition (AAC), Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Pseudo-Amino Acid Composition (PAAC) etc., are extracted. Following feature extraction, three datasets were created: Dataset 1 contains all of the extracted features. While Datasets 2 and 3 contain the most relevant features obtained through dimensionality reduction. To analyze the importance of high-dimensional features and their participation in protein–protein interactions, a random forest classifier is trained on three datasets. With dimensionality reduction, the model exhibited exceptional accuracy, indicating that dimensionality reduction fails to capture the complexity of interactions and the underlying relationships between human and viral proteins. As a result of retaining high-dimensional features, it is possible to capture all the characteristics of protein–protein interactions that resemble host–pathogen associations, leading to the development of biologically meaningful models. Our proposed approach is a more realistic and comprehensive classification model, leading to deeper insights and better applications in virology and drug development. [ABSTRACT FROM AUTHOR]

The advent of modern technologies, acquisition of large amounts of crop management and weather data, and advances in computing are reshaping modern agriculture. These advancements have unlocked the power of data by providing valuable insights and more accurate yield predictions. This study utilizes a historic US sweet corn dataset to: (a) evaluate machine learning model performances on sweet corn yield prediction and (b) identify the most influential variables for crop yield predictions. The sweet corn data comprised field-level data for over a quarter-century period (1992–2018) from two primary commercial sweet corn production regions for processing, namely the Upper Midwest and the Pacific Northwest. Several machine learning models were trained to predict field-level sweet corn yield from 67 variables of crop genetics, management, weather, and soil factors. The random forest model outperformed all trained models with the lowest RMSE (3.29 Mt/ha) and the highest Pearson's correlation coefficient (0.77) between predicted and observed yields. Variable importance plots revealed the top three most influential predictor variables as year (time), location (space), and seed source (genetics). Season long total precipitation and average minimum temperature during anthesis were the two most important weather variables in yield prediction. This is the first report of using fine-scale (time and space) crop data and advanced data analytics to leverage insights into commercial sweet corn production. [ABSTRACT FROM AUTHOR]

Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using wavelets for feature extraction and random forest for classification, an automatic sleep-stage classification method was sought and assessed. The age of the subjects, as well as the moment of sleep (early-night and late-night), were confronted to the performance of the classifier. From this study, we observed that these variables do affect the automatic model performance, improving the classification of some sleep stages and worsening others. [ABSTRACT FROM AUTHOR]

Ensemble methods are supervised learning algorithms that provide highly accurate solutions by training many models. Random forest is probably the most widely used in regression and classification problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression. However, such an algorithm suffers from a lack of explainability and thus does not allow users to understand how particular decisions are made. To improve on that, we propose a new way of interpreting an ensemble tree structure. Starting from a random forest model, our approach is able to explain graphically the relationship structure between the response variable and predictors. The proposed method appears to be useful in all real-world cases where model interpretation for predictive purposes is crucial. The proposal is evaluated by means of real data sets. [ABSTRACT FROM AUTHOR]

This study is focused on evaluating the potential of deep neural networks for assessing soil properties based on VIS–NIR spectroscopy with spectral wavelength ranges of 350–2500 nm and 10 nm resolution on a global scale. The dataset was provided by the ICRAF-ISRIC soil spectral library and consists of 4438 samples from 58 countries. In This research we used the powerful one-dimensional (1D) convolutional neural network (CNN) models to predict soil organic carbon (OC), pH, calcium carbonate (CaCO3), cation exchange capacity (CEC), effective CEC (ECEC), sum of cations (SC), base saturation (BS), exchangeable acidity (ACe), exchangeable cations (aluminum (Al), calcium (Ca), magnesium (Mg), sodium (Na) and‌ potassium (K)), silt, sand and clay content. Also, traditional regression approaches of spectral data including partial least squares regression (PLSR), multilayer perceptron (MLP) and random forest (RF) with optimum preprocessing of spectral data were also tested and the models' performances were evaluated and compared. The optimum structure of models was determined by testing different components for PLSR, selecting the best number of neurons in the hidden layer, activation functions, and solvers for MLP, and finding the proper number of trees and maximum depth in RF. The CNN-based model estimated the OC, pH, CaCO3, CEC, ECEC, SC, BS, ACe, Al, Ca, Mg, Na, K, silt, sand and clay content with the ratio of percent deviation (RPD) of 2.98, 1.9, 2.48, 2.01, 2.05, 2.39, 1.97, 1.01, 1.59, 2.06, 1.72, 1.69, 1.29, 1.38, 1.8 and 1.97, respectively. We evaluated the performance of CNN-based methods with an effective architecture for soil spectral data analysis. The CNN model outperforms other regression algorithms in terms of accuracy and low predicting errors using non-preprocessed data. However, the use of diverse soil properties for modeling spectral data can provide sufficient information on the advantages and disadvantages of VIS–NIR data in predictions. [ABSTRACT FROM AUTHOR]

Since there has been concern about food security, accurate prediction of wheat yield prior to harvest is a key component. Random Forest (RF) has been used in many classification and regression applications, such as yield estimation, and the performance of RF has improved by tuning its hyperparameters. In this paper, different changes are made to traditional RF for yield estimation, and the performance of RF is evaluated. Accordingly, RFs constructed using various weak learners, as well as a combined RF consisting of different weak learners are assessed by growing weak Gaussian Process Regression (GPR), Decision Tree (DT), Neural Network (NN), and Stepwise Regression (SW) models in the forest. The input data to DTs are also partitioned into leaves in N (e.g., two) dimensional feature space by using clustering in each parent node. In addition, a subset of the training set is randomly sampled with replacement for training a learner in the forest, instead of randomly sampling the whole training set in traditional RF. Using clustering in DTs added flexibility while utilizing NN as a weak learner yielded the most favorable outcomes in our research. The number of input training samples (Itree) to each tree was also identified as a new hyperparameter to the forest, and the prediction results were more influenced by the Itree compared to the known hyperparameters, such as the number of trees in the forest (ntree) and the number of features for each tree (mtry). [ABSTRACT FROM AUTHOR]

Morphometric analysis of a river basin, encompassing the assessment of shape, drainage, and terrain characteristics, is crucial for erosional, hydrological, and hydro-geological studies However morphometric analysis of large river basins conventionally poses computational challenges. In this study, Google Earth Engine (GEE), a cloud-based platform was utilized to conduct morphometric modeling and evaluation of geomorphology of river basins using JavaScript Application Programming Interfaces (APIs). Consecutively, soil erosion susceptibility mapping based on morphometric parameters using an ensemble of statistical and machine learning algorithms specifically Principal Component Analysis (PCA), Random Forest (RF), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was performed. These methods were applied to India's largest (Ganga: 834,933.65 Km2) and second-largest (Godavari: 310,000 Km2) river basins. Correlation-based comparative analysis of PCA and RF-derived erosional susceptibility rankings for the Ganga (GARB) and Godavari (GRB) river basins was also performed followed by the identification of highly susceptible zones. Results indicate that 28.50% (86,800 Km2) of GRB and 9.85% (81,823.49 Km2) of GARB are highly susceptible to geological erosion. Highly susceptible GARB sub-watersheds were 2.7 times more susceptible than those in GRB, while less susceptible GARB sub-watersheds were 1.96 times more susceptible than GRB counterparts. Machine learning with RF excelled in river basins with greater morphometric variation (GARB), while PCA proved more effective in homogenous river basins (GRB). This study introduces an alternative method for morphometric modeling based erosional analysis, emphasizing the suitability and constraints of statistical and machine learning algorithms and providing valuable guidance for soil conservation efforts. [ABSTRACT FROM AUTHOR]

In practical engineering, the mechanical state of circuit breaker is different and the mechanical characteristic curve is similar, so it is easy to be misdiagnosed using single signal to diagnose the defects of high voltage circuit breakers. In view of this problem, this paper proposes a defect diagnosis method based on the combination of stroke curve current signal of high voltage circuit breakers optimized by random forest feature optimization. Firstly, the characteristic values of stroke curve and current signal are extracted according to the mechanical characteristic signals under different states. Secondly, redundant features are eliminated by random forest algorithm combined with support vector machine model to determine the optimal feature subset database. Finally, grey wolf optimization-support vector machine model is used to further improve the comprehensive performance of circuit breaker defect diagnosis. The diagnostic results show that compared with the single signal diagnosis method, the accuracy of the combined diagnosis method can be improved by up to 22.5%. In addition, the defect diagnosis accuracy of the high voltage circuit breaker combined with the line signal can be further improved by 2.5% through feature selection and support vector machine model optimization. [ABSTRACT FROM AUTHOR]

Pan evaporation modeling and forecasting are needed to provide timely, continuous, and valuable information to support water management. This study aimed to overcome the constraints identified in traditional regression techniques and less explored machine learning models—the CatBoost, to enhance the precision and comprehensiveness of pan evaporation estimation. Additionally, the CatBoost model was compared with other machine-learning approaches, viz., multiple linear regression (MLR), multiple non-linear least-squares regression (MNLSR), multivariate adaptive regression splines (MARS), random forest regression (RF), and M5 model tree (M5Tree). The algorithms were developed using data obtained from the Crop Research Center, Pantnagar- Uttarakhand, India. Stepwise regression was used as an input variable selection algorithm for selecting the best relevant input from several meteorological variables. For the model's development, data were split into two subsets: the first three years were reserved for model calibration, while the fourth-year data was used for model validation. Statistical analyses, namely root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Willmott index of agreement (WI), Pearson correlation coefficient (PCC), coefficient of determination (R2), and mean absolute error (MAE) were used to assess the effectiveness of weekly pan evaporation estimation models. Findings indicated that the CatBoost model, based on four predictors, was the most accurate at Pantnagar station, with NSE, WI, MAE, RMSE, PCC and R2 ranging from 0.9838 to 0.9877, 0.9960–0.9968, 0.1991–0.2078, 0.3643–0.3650–0.9955, 0.9925 and 0.9851–0.9911, respectively. RF model was found to be the second-most best-performing model in terms of in accuracy and effectiveness for mapping weekly pan evaporation, followed by MARS, MNLSR, MLR, and M5Tree. In conclusion, CatBoost's approach of considering combinations greedily implies a thoughtful and cautious selection of features during the modeling process. This can enhance the model's robustness and generalization capability, contributing to more accurate predictions in diverse environmental conditions in subtropical and sub-humid regions. [ABSTRACT FROM AUTHOR]

With the increase interest and number of the users in Social media, the file handling has also increased. To manage the load, cloud servers are being used by the service providers. To identify and cluster file is a difficult task that is important in the domain of computer science. Various traditional approaches for identification exists that uses design features. The problem with these methods is get that they can be easily spoofed. To resolve the issue, in this paper, a hybrid algorithm combining the features of Random Forest with AdaBoost is proposed. The algorithm Internet of Thing (IoT) data file formatting (IDFF) classifies data as (text, image, audio and video) and gives better accuracy. Our proposed research obtains better Accuracy (93%), Precision (95%), Recall (95%), F-Measure (95%), and G-Mean (96%). [ABSTRACT FROM AUTHOR]