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This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984–2009) and testing (2010–2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions. [ABSTRACT FROM AUTHOR]

Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features. Additionally, our approach effectively utilizes a combination of EEG signals and image features to streamline the feature set required for the Random Forest Classifier (RFC) while maintaining high accuracy. The findings of this research hold substantial potential for developing advanced fault alert systems, particularly in critical decision-making environments such as the medical and defence sectors. [ABSTRACT FROM AUTHOR]

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields. [ABSTRACT FROM AUTHOR]

Society's support upon chemicals over the last few decades has led to their increased production, application and discharge into the environment. Wastewater treatment plants (WWTPs) contain a multitude of these chemicals such us; pharmaceutical compounds (PCs). Often, their biodegradability by activated sludge microorganisms is significant for their elimination during wastewater treatment. In this paper the focus is laid on two PCs carbamazepine (CBZ) and diclofenac (DCF) and their main transformation products (TPs). Laboratory degradation tests with these two pharmaceuticals using activated sludge as inoculum under aerobic conditions were performed and microbial metabolites were analyzed by liquid chromatography-mass spectrometry (LC/MS-MS). In two different Mixed liquid Suspended Solids (MLSS) concentrations the biodegradability by activated sludge of CBZ and DCF were evaluated. Also, this article proposes a decision support system to optimize the prediction process of this type of pharmacological compounds. A study and analysis of the techniques of Support Vector Machine, Random Forest, Decision Trees and Multilayer Perceptron Network is carried out to select the most reliable and accurate predictor for the decision system. There are not significant differences in the removal of DCF with 30 mg MLSS/L and 60 mg MLSS/L. DCF was better removed than CBZ in all experiments studied. The TP detected in the samples were mainly 4-OH-DCF for DCF and 10, 11 EPOXICBZ for CBZ. The results show that the best models are obtained with Random Forest and Multilayer Perceptron Network techniques, with a model fit of more than 95% for both carbamazepine and diclofenac metabolites. Obtaining a root means square errors of 0.80 µg/L for the metabolite 4-OH-DCF for DCF with the technique Random Forest and a root means square errors of 1.13 µg/L for the metabolite 10, 11 EPOXICBZ for CBZ with the Multilayer Perceptron Network technique. [ABSTRACT FROM AUTHOR]

In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient's DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656–0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237–0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) ranks as the second most prevalent neurodegenerative disorder globally, and its incidence is rapidly rising. The diagnosis of PD relies on clinical characteristics. Although current treatments aim to alleviate symptoms, they do not effectively halt the disease's progression. Early detection and intervention hold immense importance. This study aimed to establish a new PD diagnostic model. Methods: Data from a public database were adopted for the construction and validation of a PD diagnostic model with random forest and artificial neural network models. The CIBERSORT platform was applied for the evaluation of immune cell infiltration in PD. Quantitative real-time PCR was performed to verify the accuracy and reliability of the bioinformatics analysis results. Results: Leveraging existing gene expression data from the Gene Expression Omnibus (GEO) database, we sifted through differentially expressed genes (DEGs) in PD and identified 30 crucial genes through a random forest classifier. Furthermore, we successfully designed a novel PD diagnostic model using an artificial neural network and verified its diagnostic efficacy using publicly available datasets. Our research also suggests that mast cells may play a significant role in the onset and progression of PD. Conclusion: This work developed a new PD diagnostic model with machine learning techniques and suggested the immune cells as a potential target for PD therapy. [ABSTRACT FROM AUTHOR]

This study examines Free Space Optical (FSO) communication's performance in different fog conditions, focusing on Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) setups. In low fog, SISO handles signal degradation well. MIMO improves link robustness in moderate fog. High fog challenges traditional FSO, leading to ML integration to optimize communication parameters. For weather channel classification, a preprocessing scheme reduces features from 12 to 2 given that the 12 features are Bit Error Rate (BER), Quality Factor (Q-factor) and Received Optical Power (ROP) for different 4 users. A Gaussian Process Classifier (GPC) achieves an accuracy greater than 99%, surpassing SVM, Decision Tree, and Random Forest. GPC's fit and predict functions execute in 0.15 s, outperforming NuSVM (0.2 s). This study highlights FSO, SISO, MIMO, and Machine Learning (ML) practicality in enhancing communication resilience in adverse weather, especially in fog-prone areas. [ABSTRACT FROM AUTHOR]

This study investigates monthly streamflow modeling at Kale and Durucasu stations in the Black Sea Region of Turkey using remote sensing data. The analysis incorporates key meteorological variables, including air temperature, relative humidity, soil wetness, wind speed, and precipitation. The study also investigates the accuracy of multivariate adaptive regression (MARS) with Kmeans clustering (MARS-Kmeans) by comparing it with single MARS, M5 model tree (M5Tree), random forest regression (RF), multilayer perceptron neural network (MLP). In the first modeling stage, principal component regression is applied to diverse input combinations, both with and without lagged streamflow (Q), resulting in twenty-three and twenty input combinations, respectively. Results demonstrate the critical role of including lagged Q for improved model accuracy, as models without lagged Q exhibit significant performance degradation. The second stage involves a comparative analysis of the MARS-Kmeans model with other machine-learning models, utilizing the best-input combination. MARS-Kmeans, incorporating three clusters, consistently outperforms other models, showcasing superior accuracy in predicting monthly streamflow. [ABSTRACT FROM AUTHOR]

Aims: Peanut (Arachis hypogaea L.) straw decomposition and nutrient release (N, K, and P) processes were investigated using a 3-pool model (labile, intermediate, and resistant) to understand the determinant factors. Methods: A two-year field experiment was carried out with a split-plot design: the main plot contained two irrigation regimes, while the subplot contained three peanut straw incorporation rates. A total of 216 nylon-mesh bags consisting of peanut straw were buried at a depth of 20 cm, and were removed at 6 winter wheat growth stages (overwintering, double ridge, jointing, flowering, grain filling, and maturity), and the nutrient release (N, P and K) from the peanut straw was measured. Results: The decomposition dynamics of the labile and intermediate pools were similar in both years. The straw incorporation rate, rather than the irrigation regime, controlled the decomposition process, which increased with increased straw incorporation rates. At a high incorporation rate, the released N, P, and K from the peanut straw were approximately 39%, 30%, and 87% of the required regional fertilizer input for winter wheat, respectively. Furthermore, the N released from straw decomposition was strongly related to the released K as indicated by the stoichiometry ratio. The random forest model predicted that temperature, precipitation, and initial straw nutrients were the main drivers of peanut straw decomposition. Conclusions: We determined the nutrient stoichiometry and release characteristics of peanut straw decomposition, and found that in comparison to irrigation, the straw incorporation rate exhibits a more profound effect on the peanut straw decomposition process. Highlights: Peanut straw decomposition increases with incorporation rate than irrigation. The relative N release is synergistic with relative K release. High straw incorporation rate provides 30–40% of required N and P for wheat growth. Temperature, precipitation, and initial nutrient amount are the main drivers of straw decomposition. [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]

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]

Identifying a fish's artificial or natural origin is crucial for stock enhancement assessments. In this study, we collected specimens of hatchery (including the early fry, reared, and released juveniles) and wild mangrove snapper Lutjanus argentimaculatus to explore the feasibility of using δ13C and δ15N values in scales as a non-lethal alternative to muscle tissues for discriminating between hatchery and wild origins. The results showed scales generally enriched in δ13C value and relatively consistent in δ15N value compared to muscle. Significant correlations were found between the two tissues for both δ13C and δ15N values (R2 = 0.80–0.88). Based on the isotopic composition of the scale core, individuals with different origins could be effectively discriminated, achieving a classification success rate of 77.6%. The random forest analysis revealed that a δ13C value of −15.8‰ could be a reference for determining the hatchery and wild origins of mangrove snapper. This study provides an isotopic approach for assessing the benefit of stock enrichment in mangrove snapper. It highlights the importance of regular sampling and scales analysis to expand fishery management and conservation practices. [ABSTRACT FROM AUTHOR]

In recent years, the focus has shifted towards leveraging physics-based modelling and data-driven analysis to predict drilling incidents and anomalies in real time, with the goal of reducing non-productive periods. However, much of this attention has directed at specific drilling operations like drilling and tripping, leaving other vital processes, such as wellbore conditioning, comparatively overlooked. The primary objective of this study is to employ data-driven techniques for predicting the dynamic state of the wellbore by utilising sensor data, operating parameters, and surface measurements. Accurate predictions are pivotal for automating these processes, promising significant savings in both redundant time and associated costs, ultimately elevating operational efficiency. In this research, the surface drilling parameters such as flowrate, rotation speed, block position, and drill string length are incorporated with the surface measurements such as hookload, pressure, and torque during wellbore conditioning operation to predict further surface sensor measurements. Different parameter settings are evaluated to find the best approach. Six supervised learning algorithms are used to select the best prediction method. The findings reveal that considering all surface parameters and measurements yields the most accurate predictions. Among various single and multi-target regression methods, including deep learning approaches, the Gaussian process and random forest models exhibit the lowest prediction errors. By reliably predicting and understanding wellbore behaviour, this research paves the way for more efficient and autonomous drilling operations in the future, bridging a critical gap in the industry's automation capabilities. [ABSTRACT FROM AUTHOR]

The increase in penetration levels of distributed generation (DG) into the grid has raised concern about undetected islanding operations. Islanding is a phenomenon in which the grid-tied inverter of a distributed generation system, and some of the local loads are disconnected from the grid. If this condition is not detected and the generation (e.g. from a photovoltaic energy source) remains operative, the isolated DG system will stay energised by the inverter. The phase mismatch between the grid and the inverter voltage makes this scenario undesirable since it could be hazardous for the maintenance operator and could harm the inverter and loads in the event of an unsynchronised reconnection of the grid. Consequently, the article presented a novel hybrid active anti-islanding approach for fast and reliably detecting unintended islanding. For the modelling and experimental setup, a multiphase grid-tied photovoltaic distributed generating system was employed, and it was regarded as a viable application. Initially, the study introduces a fault-tolerant control (FTC) technique of data-driven predictive control to limit the impact of grid faults on inverters. Furthermore, the article suggested the Sandia frequency and voltage shift (SFVS) approach for inverter-based distributed generation to identify an islanding state. The approach employs a positive feedback gain to minimise NDZ and THD; moreover, the system does not affect the power quality. To minimise system damage, this scenario necessitates the use of effective islanding detection algorithms. This study suggests using empirical mode decomposition (EMD) to enhance power quality to extract detailed coefficients, which are subsequently processed to detect common transient fluctuations during islanding. In the grid-tied inverter, random forest (RF) is also utilised to categorise the condition as islanding or non-islanding. This exhibits an acceptable trade-off between output power quality and islanding detection effectiveness. The quality of the data and the selection of hyperparameters impact the performance of ML models. Following that, hyperparameter adjustment of the random forest model is done using the memetic firefly algorithm (MFA), which has a considerable impact on overall classification performance. The hyperparameters of the suggested RF model have been successfully calculated to attain the maximum accuracy and the least loss. Results demonstrate that the suggested method produces less THD (2.44) than the conventional detection, leading to faster islanding detection, a better non-detection zone of 0.3639 Hz, and a shorter detection time of 0.89 ms, respectively. [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]

Background: Vestibular migraine (VM) and Menière's disease (MD) are two common causes of recurrent spontaneous vertigo. Using history, video-nystagmography and audiovestibular tests, we developed machine learning models to separate these two disorders. Methods: We recruited patients with VM or MD from a neurology outpatient facility. One hundred features from six "feature subsets": history, acute video-nystagmography and four laboratory tests (video head impulse test, vestibular-evoked myogenic potentials, caloric testing and audiogram) were used. We applied ten machine learning algorithms to develop classification models. Modelling was performed using three "tiers" of data availability to simulate three clinical settings. "Tier 1" used all available data to simulate the neuro-otology clinic, "Tier 2" used only history, audiogram and caloric test data, representing the general neurology clinic, and "Tier 3" used history alone as occurs in primary care. Model performance was evaluated using tenfold cross-validation. Results: Data from 160 patients with VM and 114 with MD were used for model development. All models effectively separated the two disorders for all three tiers, with accuracies of 85.77–97.81%. The best performing algorithms (AdaBoost and Random Forest) yielded accuracies of 97.81% (95% CI 95.24–99.60), 94.53% (91.09–99.52%) and 92.34% (92.28–96.76%) for tiers 1, 2 and 3. The best feature subset combination was history, acute video-nystagmography, video head impulse test and caloric testing, and the best single feature subset was history. Conclusions: Machine learning models can accurately differentiate between VM and MD and are promising tools to assist diagnosis by medical practitioners with diverse levels of expertise and resources. [ABSTRACT FROM AUTHOR]

The escalating frequency and sophistication of cyber threats pose significant challenges to traditional intrusion detection methods. Signature-based misuse detection, hybrid detection, and anomaly detection, while effective in isolation, often struggle to keep pace with the ever-evolving tactics employed by attackers. This research stems from the pressing need to develop intrusion detection models that offer improved accuracy, adaptability, and robustness in the face of rapidly changing attack vectors. Our study leverages a comprehensive set of experiments conducted on diverse datasets, including N-BaIoT, NSL-KDD, and CICIDS2017. The primary focus is on the evaluation and comparison of machine learning algorithms such as Random Forest, XGBoost, and decision trees. Notably, our research culminates in the development of a hybrid intrusion detection model that combines the strengths of these algorithms. The results of our experiments indicate that the hybrid model, particularly when combining Random Forest and XGBoost, exhibits exceptional accuracy. This approach outperforms individual algorithms, achieving an accuracy rate of 97% in certain cases. We attribute this success to the ensemble learning approach, which capitalizes on the consensus of diverse classifiers. In conclusion, our research not only demonstrates the effectiveness of ensemble learning in enhancing intrusion detection but also underscores the importance of continuous adaptation in the face of evolving threats. By leveraging network traffic data, our hybrid model offers a promising avenue for bolstering intrusion classification in Big Data environments. [ABSTRACT FROM AUTHOR]

The demand for high-performance computing (HPC) resources in computing fields such as machine learning has increased significantly in recent years. Computing power has been growing exponentially to keep up with this demand. However, these gains have not been able to translate to performance improvement in real-world applications. One of the biggest reasons for this is performance degradation in terms of input/output (I/O) due to the increased storage latency and complex parallel I/O architecture of accessing data in distributed storage systems. In this study, we analyze application-specific I/O patterns to gain a deeper understanding of I/O throughput and the interaction between applications and the I/O system. Specifically, we analyze the importance of each feature of I/O patterns through feature analysis based on the collected monitoring information. We also investigate the feasibility of identifying the application based on the analyzed key features. To this end, we present the analysis accuracy and confusion matrix of four algorithms, including random forest, which are widely used as classification algorithms in the experimental results. The experiment results confirm that we can distinguish applications with an accuracy of more than 90% by using application-specific I/O patterns. [ABSTRACT FROM AUTHOR]

Classification methods with Machine Learning have been used in various fields, including volcanology. This method makes it possible to automatically perform data processing and classification on large scale data in a short time. Seismic time series data from volcanic earthquakes of Mt. Merapi are classified into five types of earthquakes, i.e.: Volcanic Type A (VTA), Volcanic Type B (VTB), Multiphase (MP), Low-frequency (LF) and Rockfall (RF), with a total sample data of 266 earthquakes, could be classified automatically using machine learning. The features of the signal are taken from the dominant frequency value, the spectral pattern that is formed, and the amplitude value to differentiate the earthquake dataset into each class. Classification is done using the XGBoost algorithm. Tests were carried out using various data test–train percentage to get the best result. Based on the XGBoost function test, which the best value obtained with an accuracy of testing the data is 76.67% using the 70%:30% train–test scenario. The results of this accuracy value are greater when compared to the accuracy value of previous studies with the SVM method which is 74.4% accuracy. This research also has better results in spectral analysis with the results of spectral comparison plots that appear to show more significant differences. [ABSTRACT FROM AUTHOR]

Various anomalies occurring before earthquakes are currently being studied to predict seismic events, with one of them being the radioactive element radon (222Rn). Radon concentrations in the soil, water, and atmosphere fluctuate in response to crustal movement. Recent research has statistically detected anomalies by analyzing the fluctuations in radon concentrations before earthquakes and conducting quantitative evaluations of radon. However, the method used to determine the parameters in the analysis was problematic. Therefore, in this study, we compared observed atmospheric radon concentration data with predicted values based on typical annual patterns using Random Forest analysis. We conducted a more objective analysis by employing this method and statistically determining anomalies using thresholds. This analysis was conducted using atmospheric radon concentration observation data obtained at Kobe Pharmaceutical University (KPU) before the 1995 Kobe Earthquake, and ionization currents emitted when radon decays were obtained at Fukushima Medical University (FMU) before the 2011 Tohoku-oki Earthquake. Consequently, before the major earthquakes occurred at both locations, the difference between the predicted and observed values exceeded the standard deviation by a factor of three. These results indicate the potential of Random Forest analysis to identify anomalies in atmospheric radon concentrations before earthquakes occur. [ABSTRACT FROM AUTHOR]

Electronic waste (e-waste) is considered a major issue that our world is tackling nowadays. This electronic waste causes various health issues to animals as well as human beings which further results in environmental pollution in developing countries like India. To overcome these issues, proper e-waste collection is proposed by using the dynamic sine cosine-based neural network optimization (DSCNN) approach. The major objective of this approach involves collecting waste from the individual, hence handling the widespread adoption and use of smartphones. To enhance waste planning collection, residents upload a photograph of their waste to the waste collection company's server, which mechanically recognizes and categorizes the image. A new classification and detection scheme using the DSCNN approach is proposed for efficient e-waste collection planning and correctly detects the type and quantity of waste components in images. The identification and classification accuracy of the uploaded images is very accurate; this method describes the e-waste collection process in various streets and buildings in Maharashtra, India. Experimental results describe that the proposed approach readily achieves the proper allocation of vehicle collection, vehicle routing plan, and household e-waste collection, resulting in reduced collection costs. Moreover, the proposed DSCNN method is compared to various other methods like random forest algorithm (RFA), fractional henry gas optimization (FHGO), behavior-based swarm model by the fuzzy controller (BSFC), and deep learning convolutional neural network (DL-CNN). The DSCNN approach yielded an e-waste collection detection accuracy of 97%. The accuracy rates of 94%, 95%, 93%, and 92.15% are obtained from the DL-CNN, FHGO, BSFC, and RFA. [ABSTRACT FROM AUTHOR]

Mass spectrometry imaging (MSI) allows to study cancer's intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery. [ABSTRACT FROM AUTHOR]

Mycotoxin contamination of agricultural commodities is a global public health problem that has remained elusive to various mitigation approaches, particularly in developing countries. Climate change and its impact exacerbates South Africa's vulnerability to mycotoxin contamination, and significantly threatens its's food systems, public health, and agro-economic development. Herein we analyse sixteen years (2005/2006–2020/2021) of annual national meteorological data on South Africa which reveals both systematic and erratic variability in critical climatic factors known to influence mycotoxin contamination in crops. Within the same study period, data on fumonisin (FB) monitoring show clear climate-dependent trends. The strongest positive warming trend is observed between 2018/2019 and 2019/2020 (0.51 °C/year), and a strong positive correlation is likewise established between FB contamination and temperature (r ranging from 0.6 to 0.9). Four machine learning models, viz support vector machines, eXtreme gradient boosting, random forest, and orthogonal partial least squares, are generalized on the historical data with suitable performance (RMSE as low as 0.00). All the adopted models are able to predict future FB contamination patterns with reasonable precision (R2 ranging from 0.34 to 1.00). The most important model feature for predicting average FB contamination (YA) is the historical pattern of average FB contamination in maize within the region (ΣFBs_avg). The two most significant features in modelling maximum FB contamination (YM) are minimum temperature from the CMIP6 data (Pro_tempMIN) and observed precipitation from the CRU data (O_prep). Our study provides strong evidence of the impact of climate change on FB in South Africa and reiterates the significance of machine learning modelling in predicting mycotoxin contamination in light of changing climatic conditions, which could facilitate early warnings and the adoption of relevant mitigation measures that could help in mycotoxin risk management and control. [ABSTRACT FROM AUTHOR]

Childhood maltreatment (CM) is known to influence brain development. To obtain a better understanding of related brain alterations, recent research has focused on the influence of the type and timing of CM. We aimed to investigate the association between type and timing of CM and local brain volume. Anatomical magnetic resonance images were collected from 93 participants (79 female/14 male) with a history of CM. CM history was assessed with the German Interview Version of the "Maltreatment and Abuse Chronology of Exposure" scale, "KERF-40 + ". Random forest regressions were performed to assess the impact of CM characteristics on the volume of amygdala, hippocampus and anterior cingulate cortex (ACC). The volume of the left ACC was predicted by neglect at age 3 and 4 and abuse at age 16 in a model including both type and timing of CM. For the right ACC, overall CM severity and duration had the greatest impact on volumetric alterations. Our data point to an influence of CM timing on left ACC volume, which was most pronounced in early childhood and in adolescence. We were not able to replicate previously reported effects of maltreatment type and timing on amygdala and hippocampal volume. [ABSTRACT FROM AUTHOR]

The urgency of interconnected social-ecological dilemmas such as rapid biodiversity loss, habitat loss and fragmentation, and the escalating climate crisis have led to increased calls for the protection of ecologically important areas of the planet. Protected areas (PA) are considered critical to address these dilemmas although growing divides in wellbeing can exacerbate conflict around PAs and undermine effectiveness. We investigate the influence of proximity to PAs on wellbeing outcomes. We develop a novel multi-dimensional index of wellbeing for households and across Africa and use Random Forest Machine Learning techniques to assess the importance score of households' proximity to protected areas on their wellbeing outcomes compared with the importance scores of an array of other social, environmental, and local and national governance factors. This study makes important contributions to the conservation literature, first by expanding the ways in which wellbeing is measured and operationalized, and second, by providing additional empirical support for recent evidence that proximity to PAs is an influential factor affecting observed wellbeing outcomes, albeit likely through different pathways than the current literature suggests. [ABSTRACT FROM AUTHOR]

Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effective care to patients. While such predictive tools have been extensively explored in resource-rich languages like English, this manuscript focuses on predicting disease categories automatically from symptoms documented in the Afaan Oromo language, employing various classification algorithms. This study encompasses machine learning techniques such as support vector machines, random forests, logistic regression, and Naïve Bayes, as well as deep learning approaches including LSTM, GRU, and Bi-LSTM. Due to the unavailability of a standard corpus, we prepared three data sets with different numbers of patient symptoms arranged into 10 categories. The two feature representations, TF-IDF and word embedding, were employed. The performance of the proposed methodology has been evaluated using accuracy, recall, precision, and F1 score. The experimental results show that, among machine learning models, the SVM model using TF-IDF had the highest accuracy and F1 score of 94.7%, while the LSTM model using word2vec embedding showed an accuracy rate of 95.7% and F1 score of 96.0% from deep learning models. To enhance the optimal performance of each model, several hyper-parameter tuning settings were used. This study shows that the LSTM model verifies to be the best of all the other models over the entire dataset. [ABSTRACT FROM AUTHOR]

To explore the potential of using the mineral alteration information extracted by remote sensing technology to indirectly estimate the heavy metal content of salinized soil, 23 sampling points were uniformly set up in the town of Gudao in the Yellow River Delta as the research area in 2022. The concentrations of seven heavy metals, Cr, Cu, Pb, Zn, As, Mn and Ni, at the sampling points were determined in laboratory tests. Spectral derivative indices, topographic factors, and mineral alteration information (iron staining, hydroxyl, and carbonate ions) were extracted and screened as modeling factors using Sentinel 2 imagery. An inverse model of heavy metal content was constructed using the random forest algorithm, and the model accuracy was evaluated using the cross-validation method. The results of the study show that: (1) Hydroxyl and carbonate ion alteration can be effectively used for the inversion of soil As and Ni content in this study area. Iron-stained alteration can be used as a modeling factor in the inversion of Cr, Cu, Pb, Zn, and Mn concentrations. (2) The inclusion of alteration information improves the accuracy of heavy metal content inversion. The Cu concentration was verified to be the best predictor, with an RMSE of 3.309, MAPE of 11.072%, and R2 of 0.904, followed by As, Ni, and Zn; the predictive value of Mn, Cr and Pb was average. (3) Based on the results of concentration inversion, the high concentration areas of As, Ni, and Mn are primarily distributed on both sides of the river and around lakes and ponds. The high-concentration areas of Zn were mainly distributed in the farmland areas on both sides of the river. Areas with high concentrations of Cu were mainly distributed in the eastern oil extraction area, both sides of the rivers, and around lakes. [ABSTRACT FROM AUTHOR]

Luster is one of the vital indexes in pearl grading. To find a fast, nondestructive, and low-cost grading method, optical coherence tomography (OCT) is introduced to predict the luster grade through the texture features. After background removal, flattening, and segmentation, the speckle pattern of the region of interest is described by seven kinds of feature textures, including center-symmetric auto-correlation (CSAC), fractal dimension (FD), Gabor, gray level co-occurrence matrix (GLCM), histogram of oriented gradients (HOG), laws texture energy (LAWS), and local binary patterns (LBP). To find the relations between speckle-derived texture features and luster grades, four Four groups of pearl samples were used in the experiment to detect texture differences based on support vector machines (SVMs) and random forest classifier (RFC)) for investigating the relations between speckle-derived texture features and luster grades. The precision, recall, F1-score, and accuracy are more significant than 0.9 in several simulations, even after dimension reduction. This demonstrates that the texture feature from OCT images can be applied to class the pearl luster based on speckle changes. [ABSTRACT FROM AUTHOR]

This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in the medical and aesthetic fields. We explore the potential of cavitation to alter abdominal fat composition and delve into the optimization of fat prediction models using advanced hyperparameter optimization techniques, Hyperopt and Optuna. Our objective is to enhance the predictive accuracy of abdominal fat dynamics post-cavitation treatment. Employing a robust dataset with abdominal fat measurements and cavitation treatment parameters, we evaluate the efficacy of our approach through regression analysis. The performance of Hyperopt and Optuna regression models is assessed using metrics such as mean squared error, mean absolute error, and R-squared score. Our results reveal that both models exhibit strong predictive capabilities, with R-squared scores reaching 94.12% and 94.11% for post-treatment visceral fat, and 71.15% and 70.48% for post-treatment subcutaneous fat predictions, respectively. Additionally, we investigate feature selection techniques to pinpoint critical predictors within the fat prediction models. Techniques including F-value selection, mutual information, recursive feature elimination with logistic regression and random forests, variance thresholding, and feature importance evaluation are utilized. The analysis identifies key features such as BMI, waist circumference, and pretreatment fat levels as significant predictors of post-treatment fat outcomes. Our findings underscore the effectiveness of hyperparameter optimization in refining fat prediction models and offer valuable insights for the advancement of non-invasive fat reduction methods. This research holds important implications for both the scientific community and clinical practitioners, paving the way for improved treatment strategies in the realm of body contouring. [ABSTRACT FROM AUTHOR]

The creation of protected areas (PAs) is not always based on science; consequently, some aquatic species may not receive the same level of protection as terrestrial ones. The objective of this study was to identify priority areas for the conservation of chelonians in the Brazilian Amazon basin and assess the contribution of PAs, distinguishing between Full Protection Areas, Sustainable Use Areas, and Indigenous Lands for group protection. The entire species modeling procedure was carried out using Species Distribution Models. Location records were obtained from platforms such as SpeciesLink, GBIF, the Hydroatlas database, and WorldClim for bioclimatic variables adjusted with algorithms like Maximum Entropy, Random Forest, Support Vector Machine, and Gaussian-Bayesian. Indigenous lands cover more than 50% of the distribution areas of chelonian species in the Brazilian Amazon. Protected areas with higher conservation importance (Full Protection Areas and Sustainable Use Areas) hold less than 15% of the combined species distribution. Researchers face significant challenges when making decisions with models, especially in conservation efforts involving diverse taxa that differ significantly from one another within a group of individuals. [ABSTRACT FROM AUTHOR]

Comparing the near-surface wind speeds obtained from the most recent global circulation model (GCM) simulations to well-known benchmark datasets like the European Centre for Medium-Range Weather Forecasts reanalysis Version 5 (ERA5) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), is necessary to make a critical assessment. Using 28 Coupled Model Intercomparison Project Phase 6 (CMIP6)-based monthly surface wind predictions, the multi-model ensemble (MME) approach in this study generates these predictions using random forest (RF) and multiple linear regression (MLR) methods over seven geographical regions in Türkiye with varying topographic complexity between 1980 and 2014, along with an offshore region. Benchmark datasets, station observations, and individual GCM predictions are used to compare the performances of MME predictions. The analysis showed that individual and the simple mean of GCM simulations are highly biased in spatial and temporal wind means. On the other hand, the MMEs formed by using groups of GCMs have significant skill for representing temporal variability in wind speed as well as for producing annual climatology and anomaly range for topographically complex regions. In MME predictions, the correlation improvements are 38–45% for RF and 22–34% for MLR. Moreover, the effect of the model group with dynamic vegetation growth on improvement remains only marginal. [ABSTRACT FROM AUTHOR]

Urban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R2 = 0.99) and 0.9 ℃ (R2 = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R2 = 0.6, daytime) and 2.2 ℃ (R2 = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R2 = 0.34, daytime) and 1.2 ℃ (R2 = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers. [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]

This study aims at utilizing machine learning (ML) in predicting the fire resistance and spalling degree of reinforced concrete (RC) columns with improved accuracy and reliability. A database with 119 test specimens was created for the development of ML-based regression models, and a database with 101 test specimens was created for the development of ML-based classification models. Six ML algorithms—support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and light gradient boosting machine (LightGBM). The hyperparameters of the ML-based models were optimized through Bayes optimization search (BayesSearchCV) with ten-fold cross-validation. The results indicated that the AdaBoost not only accurately predicted the spalling degree of RC columns with an accuracy of 87%, but also performed best in predicting the fire resistance of RC columns with R2 = 0.96 and RMSE = 16.58. The AdaBoost model achieved high accuracy without significant bias, surpassing existing design equations. SHAP method was utilized to produce global explanations for the predictions. The results revealed that concrete compressive strength, loading ratio, slenderness ratio, and column width were the most critical features for spalling degree identification. Meanwhile, those were slenderness ratio, concrete cover, loading ratio, part of the fired column, and longitudinal reinforcement for fire resistance prediction. The parametric study demonstrated that the fire resistance of RC columns is positively affected by only concrete cover. [ABSTRACT FROM AUTHOR]

A brain tumor is one of the most lethal diseases that can affect human health and cause death. Invasive biopsy techniques are one of the most common methods of identifying brain tumor disease. As a result of this procedure, bleeding may occur during the procedure, which could harm some brain functions. Consequently, this invasive biopsy process may be extremely dangerous. To overcome such a dangerous process, medical imaging techniques, which can be used by experts in the field, can be used to conduct a thorough examination and obtain detailed information about the type and stage of the disease. Within the scope of the study, the dataset was examined, and this dataset consisted of brain images with tumors and brain images of normal patients. Numerous studies on medical images were conducted and obtained with high accuracy within the hybrid model algorithms. The dataset's images were enhanced using three distinct local binary patterns (LBP) algorithms in the developed model within the scope of the study: the LBP, step-LBP (nLBP), and angle-LBP (αLBP) algorithms. In the second stage, classification algorithms were used to evaluate the results from the LBP, nLBP and αLBP algorithms. Among the 11 classification algorithms used, four different classification algorithms were chosen as a consequence of the experimental process since they produced the best results. The classification algorithms with the best outcomes are random forest (RF), optimized forest (OF), rotation forest (RF), and instance-based learner (IBk) algorithms, respectively. With the developed model, an extremely high success rate of 99.12% was achieved within the IBk algorithm. Consequently, the clinical service can use the developed method to diagnose tumor-based medical images. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of six prominent machine learning models—random forest, decision trees, K-nearest neighbor, logistic regression, support vector machines, and Naïve Bayes—for intrusion detection systems in industrial Internet of Things environments. The evaluation encompasses the effects of data preprocessing techniques, including feature engineering, data normalization, recoding, and missing data mitigation. Furthermore, the research delves into dataset balancing, examining the effects of six different techniques on model performance. The investigations are conducted using the domain-specific WUSTL-IIOT-2021 dataset, which captures the unique characteristics of IIoT data. The study also investigates multi-class attack identification utilizing an innovative SMOTE-based multi-class balancing approach to tackle dataset imbalances. The results indicate that data preprocessing and intelligent dataset balancing produce consistent enhancements in the classification performance of the selected models across binary and multi-classification tasks. Random forest emerges as the standout algorithm, delivering consistently high performance with computational efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a synchrophasor measurement-assisted integrated scheme to enhance power grid stability via transient stability assessment and emergency control of power systems in real-time. The synchrophasor measurements are time-stamped network information available from Phasor Measurement Units (PMUs). The Transient Stability Assessment (TSA) is based on measured generator bus angles obtained directly by PMUs at the control center. These synchronized angle measurements are utilized to construct features, and a Random Forest classifier is implemented to infer the TSA in real-time. The TSA results are determined within the first three cycles following the fault clearance. For operating scenarios that are unstable, an effective emergency control scheme is developed to avoid system degradation. The control strategy utilizes wide-area measurements to formulate a proportional sharing principle-based methodology in real-time. The composite scheme estimates the location and magnitude of the emergency measures to enhance grid stability. The performance of the proposed composite scheme is tested on an IEEE 39-bus test system. The results highlight that the scheme is computationally efficient and robust to topological changes. [ABSTRACT FROM AUTHOR]

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3–6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities. [ABSTRACT FROM AUTHOR]

Ecological restoration of rare earth element (REE) mining areas has become a key component in the sustainable development of the ion-adsorption-type REE industry. Current studies on ecological restoration assessments are mainly based on a comparison of REE mining ranges conducted over two or more periods, therefore, the dynamic evolution of spatial patterns for REE mining and vegetation recovery remains to be elucidated. Rapid advances in remote sensing techniques, along with the cloud-based Google Earth Engine (GEE), can provide an effective and convenient observation strategy for monitoring the environment in mining areas. In this study, we propose a hybrid method consisting of a random forest (RF) classifier and Landsat-based detection of trends in disturbance and recovery (LandTrendr) to identify and monitor the disturbance of REE mining and vegetation recovery in REE mining areas in the southern Jiangxi Province of China from 1988 to 2021 based on the GEE platform. The results reveal that REE mining activities were widespread in the study area, and the spatial distribution of REE mining areas was highly dispersed. The total area of REE mining in the study region was 93.14 km2, and the greatest REE mining activity was observed in the year 2006. As of 2021, the vegetation coverage area in the REE mining zone reached 47.44 km2, with a recovery rate of 50.93%. Among the seven counties in the study area, Longnan county had the lowest level of vegetation recovery in REE mining areas. A total of 95% of the vegetation recovery events in REE mining areas lasted between 1 and 18 years. These observations suggest that LandTrendr efficiently captured REE mining-induced disturbances and vegetation recovery and offers important reference data for the formulation of mining area restoration strategies. The results can provide a referable basis for the ecological restoration of southern Jiangxi Province, China. [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]

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography–mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts. [ABSTRACT FROM AUTHOR]

In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, and this article uses information encoded by social networks to solve the problem of employer turnover. However, understanding the factors behind black-box prediction models can be challenging. Our question was about the predictability of employee turnover, given information from the multilayer network that describes collaborations and perceptions that assess the performance of organizations that indicate the success of cooperation. Our goal was to develop an accurate prediction procedure, preserve the interpretability of the classification, and capture the wide variety of specific reasons that explain positive cases. After a feature engineering, we identified variables with the best predictive power using decision trees and ranked them based on their added value considering their frequent co-occurrence. We applied the Random Forest using the SMOTE balancing technique for prediction. We calculated the SHAP values to identify the variables that contribute the most to individual predictions. As a last step, we clustered the sample based on SHAP values to fine-tune the explanations for quitting due to different background factors. [ABSTRACT FROM AUTHOR]

Estimating penetration rates of Jumbo drills is crucial for optimizing underground mining drilling processes, aiming to reduce costs and time. This study investigates various regression and machine learning methods, including Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forests (RF), to predict the penetration rates (ROP) using multivariate inputs such as operation parameters and rock mass characteristics. The Rock Mass Drillability Index (RDi), incorporating both intact rock properties and structural parameters, was utilized to characterize the rock mass. The dataset was split into 80% for training and 20% for testing. Performance metrics including correlation coefficient (R2), variance accounted for (VAF), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. SVR exhibited the best prediction performance for ROP, achieving the highest R2, lowest RMSE, MAE, and MAPE, as well as the largest VAF values of 0.94, 0.15, 0.11, 4.84, and 94.13 during training, and 0.91, 0.19, 0.13, 6.02, and 91.11 during testing, respectively. With this high accuracy, we conclude that the proposed machine learning algorithms are valuable and efficient predictors for estimating jumbo drill penetration rates in underground mining operations. [ABSTRACT FROM AUTHOR]

Ischemic stroke (IS) is a common cerebrovascular disease whose pathogenesis involves a variety of immune molecules, immune channels and immune processes. 6-methyladenosine (m6A) modification regulates a variety of immune metabolic and immunopathological processes, but the role of m6A in IS is not yet understood. We downloaded the data set GSE58294 from the GEO database and screened for m6A-regulated differential expression genes. The RF algorithm was selected to screen the m6A key regulatory genes. Clinical prediction models were constructed and validated based on m6A key regulatory genes. IS patients were grouped according to the expression of m6A key regulatory genes, and immune markers of IS were identified based on immune infiltration characteristics and correlation. Finally, we performed functional enrichment, protein interaction network analysis and molecular prediction of the immune biomarkers. We identified a total of 7 differentially expressed genes in the dataset, namely METTL3, WTAP, YWHAG, TRA2A, YTHDF3, LRPPRC and HNRNPA2B1. The random forest algorithm indicated that all 7 genes were m6A key regulatory genes of IS, and the credibility of the above key regulatory genes was verified by constructing a clinical prediction model. Based on the expression of key regulatory genes, we divided IS patients into 2 groups. Based on the expression of the gene LRPPRC and the correlation of immune infiltration under different subgroups, LRPPRC was identified as an immune biomarker for IS. GO enrichment analyses indicate that LRPPRC is associated with a variety of cellular functions. Protein interaction network analysis and molecular prediction indicated that LRPPRC correlates with a variety of immune proteins, and LRPPRC may serve as a target for IS drug therapy. Our findings suggest that LRPPRC is an immune marker for IS. Further analysis based on LRPPRC could elucidate its role in the immune microenvironment of IS. [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]