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Machine learning classifiers are increasingly widely used. This research note explores how a particular widely used classifier, the Random Forest, performs when faced with samples which are imbalanced and noisy data. Both are known to affect accuracy, but if their effects are independent or not has not been explored. Based on an experiment using synthetic data generated for the study we find that the effects of noise and sample balance interact with each other; classification accuracy is worse when faced with both noisy data and sample imbalance. This has implications for the use of RF in market research, but also for how methods to address either sample imbalance or noise are assessed. [ABSTRACT FROM AUTHOR]

This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15–3 (CA 15–3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learning (ML) algorithms to achieve high diagnostic accuracy and potential for noninvasive early detection. The GAgNPs-enhanced electrochemical biosensor demonstrated selective detection of CA 15–3 in serum and MUC-1 in tissue homogenates, with limits of detection (LODs) of 0.07 and 0.11 U mL−1, respectively. The nanoscale dimensions of the GAgNPs endowed them with electrochemically active surface areas, facilitating sensitive biomarker detection. Experimental studies targeted CA 15–3 and MUC-1 biomarkers in clinical samples, and the biosensor exhibited ease of use and good selectivity. Furthermore, ML algorithms were employed to analyze the electrochemical data and predict biomarker concentrations, enhancing the diagnostic accuracy. The Random Forest algorithm achieved 98% accuracy in tumor presence prediction, while an Artificial Neural Network attained 76% accuracy in CA 15–3-based tumor grade classification. The integration of ML techniques with the GAgNPs-based biosensor offers a promising approach for noninvasive, accurate, and early detection of canine mammary tumors, potentially revolutionizing veterinary diagnostics. This multilayered strategy, combining eco-friendly nanomaterials, electrochemical sensing, and ML algorithms, holds significant potential for advancing both biomedical research and clinical practice in the field of canine mammary tumor diagnostics. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) has seen rapid expansion, but this growth brings significant privacy challenges due to the large amounts of data generated by myriad IoT devices. To address these challenges, this study introduces Smart Anonymity, a method that determines the optimal data anonymization algorithm for a dataset by assessing its unique features. The solution leverages OWL ontologies grounded in description logic (DL), which facilitates inconsistency checks and the discovery of new facts for data validation. Additionally, machine learning (ML) is incorporated to improve the accuracy of these classifications. ML is also instrumental in recommending suitable anonymization algorithms, with the random forest algorithm being employed explicitly for this purpose. The findings from this research indicate that Smart Anonymity effectively improves user privacy. [ABSTRACT FROM AUTHOR]

For a long time, decision-making in auditing was limited to a risk-oriented recommendation and consisted of the rigorous analysis of a sample of data. The new trend in the audit decision process focuses on the use of decision support systems (DSSs) founded on data analytics (DA) to better concentrate on in-depth analysis. This study examines how DA can improve the audit decision-making approach in the banking sector. We show that DA techniques can improve the quality of audit decision-making within banks and highlight the advantages associated with mastering these techniques, which results in a more effective and efficient audit of digital banking transactions. We propose an artifact-based data analytics-driven decision support system (DA-DSS) for an automatic fraud detection system supported by DA. The proposed DA-DSS artifact with a random forest classifier at its core is a promising innovation in the field of electronic transaction fraud detection. The results show that the random forest classifier can accurately classify the data generated by this artifact with an accuracy varying from 88 to 93% using transaction data collected from 2021 to 2022. Other classifiers including k-nearest neighbors (KNN) are also used, with a classification rate ranging from 66 to 88% for the same transaction datasets. These results show that the proposed DA-DSS with random forest can significantly improve auditing by reducing the time required for data analysis and increasing the results' accuracy. Management can use the proposed artifact to enhance and speed up the decision-making process within their organization. [ABSTRACT FROM AUTHOR]

High costs and time consuming associated with experimental trial-and-error result in low efficiency, creating an urgent need for a more effective strategy for ultra-high temperature ceramic matrix composites (UHTCMCs) development. Inspired by the exceptional performance of machine learning (ML) algorithms across various domains, this work employs ML algorithms to construct models and conduct in-depth analysis of the key factors and their patterns influencing the ablation resistance of UHTCMCs. A set of 26 dimensional features that could potentially impact the ablation resistance of UHTCMCs were established based on domain knowledge. Eight typical ML models were used to build and predict the linear ablation rate (LAR) of UHTCMCs. Results show that the random forest model has optimal generalization performance, with mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R 2) being 2.75 μm s−1 and 7.3 (μm s−1)2, and 0.71 respectively. The Shapley additive explanations values based on the random forest model reveal that the key features affecting the LAR of UHTCMCs are ranked as average melting point of ceramics (AMPC) > thermal conductivity of material (TCM) > thermal expansion coefficient of oxides (TECO) > fabrication temperature of material (FTM), all showing a negative influence on the LAR. Symbolic regression further indicates that AMPC, TCM, and TECO have an exponential negative correlation with LAR. These data-driven conclusions have been thoroughly validated through the use of C f /(TiZrHfNbTa)C composites. The established model can accelerate the discovery of material knowledge and provide reliable guidance for UHTCMC development. [ABSTRACT FROM AUTHOR]

Metamaterial absorbers (MMAs) have tremendous potential for controlling and modulating Terahertz electromagnetic (EM) waves. It is challenging to design MMAs for optimal performance using conventional methods due to their time-consuming and computationally demanding nature. Machine learning (ML) regressor approaches have emerged as potential tools for optimizing the predictive modeling of MMAs in recent years. This article examined different regression techniques, such as Decision Tree, k-Nearest Neighbors, Random Forest, Extra Trees (ET), Extreme Gradient Boosting, Bagging, and Categorical Boosting. The primary goal is to assess the effectiveness of each regressor technique in forecasting the performance of MMAs using various performance metrics. The study focuses on a representative spectrum of MMAs, chosen for its key features that are common across a wide range of MMAs. The extra tree Regressor outperforms other models in the comparative analysis due to its capacity to integrate multiple decision trees and include randomness into the feature selection process, resulting in improved predictive skills. The ET regressor forecasts MMA performance remarkably well for a 70 % test size, with a Root Mean Squared Error of 0.0251, a Mean Absolute Error of 0.0074, a Mean Squared Error of 0.006, and an Adjusted R-squared of 0.9873. These findings can assist researchers in optimizing metamaterial absorber designs for Terahertz applications using ML regressor techniques and provide insights into how these methods can be generalized to other spectra with similar characteristics. [ABSTRACT FROM AUTHOR]

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Motivation: The association between weather conditions and stroke incidence has been a subject of interest for several years, yet the findings from various studies remain inconsistent. Additionally, predictive modelling in this context has been infrequent. This study explores the relationship of extremely high ischaemic stroke incidence and meteorological factors within the Slovak population. Furthermore, it aims to construct forecasting models of extremely high number of strokes. Methods: Over a five-year period, a total of 52,036 cases of ischemic stroke were documented. Days exhibiting a notable surge in ischemic stroke occurrences (surpassing the 90th percentile of historical records) were identified as extreme cases. These cases were then scrutinized alongside daily meteorological parameters spanning from 2015 to 2019. To create forecasts for the occurrence of these extreme cases one day in advance, three distinct methods were employed: Logistic regression, Random Forest for Time Series, and Croston's method. Results: For each of the analyzed stroke centers, the cross-correlations between instances of extremely high stroke numbers and meteorological factors yielded negligible results. Predictive performance achieved by forecasts generated through multivariate logistic regression and Random Forest for time series analysis, which incorporated meteorological data, was on par with that of Croston's method. Notably, Croston's method relies solely on the stroke time series data. All three forecasting methods exhibited limited predictive accuracy. Conclusions: The task of predicting days characterized by an exceptionally high number of strokes proved to be challenging across all three explored methods. The inclusion of meteorological parameters did not yield substantive improvements in forecasting accuracy. [ABSTRACT FROM AUTHOR]

In an era marked by pervasive digital connectivity, cybersecurity concerns have escalated. The rapid evolution of technology has led to a spectrum of cyber threats, including sophisticated zero-day attacks. This research addresses the challenge of existing intrusion detection systems in identifying zero-day attacks using the CIC-MalMem-2022 dataset and autoencoders for anomaly detection. The trained autoencoder is integrated with XGBoost and Random Forest, resulting in the models XGBoost-AE and Random Forest-AE. The study demonstrates that incorporating an anomaly detector into traditional models significantly enhances performance. The Random Forest-AE model achieved 100% accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC), outperforming the methods proposed by Balasubramanian et al., Khan, Mezina et al., Smith et al., and Dener et al. When tested on unseen data, the Random Forest-AE model achieved an accuracy of 99.9892%, precision of 100%, recall of 99.9803%, F1 score of 99.9901%, and MCC of 99.8313%. This research highlights the effectiveness of the proposed model in maintaining high accuracy even with previously unseen data. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a deep learning model to predict skeletal malocclusions with an acceptable level of accuracy using airway and cephalometric landmark values obtained from analyzing different CBCT images. Background: In orthodontics, multitudinous studies have reported the correlation between orthodontic treatment and changes in the anatomy as well as the functioning of the airway. Typically, the values obtained from various measurements of cephalometric landmarks are used to determine skeletal class based on the interpretation an orthodontist experiences, which sometimes may not be accurate. Methods: Samples of skeletal anatomical data were retrospectively obtained and recorded in Digital Imaging and Communications in Medicine (DICOM) file format. The DICOM files were used to reconstruct 3D models using 3DSlicer (slicer.org) by thresholding airway regions to build up 3D polygon models of airway regions for each sample. The 3D models were measured for different landmarks that included measurements across the nasopharynx, the oropharynx, and the hypopharynx. Male and female subjects were combined as one data set to develop supervised learning models. These measurements were utilized to build 7 artificial intelligence-based supervised learning models. Results: The supervised learning model with the best accuracy was Random Forest, with a value of 0.74. All the other models were lower in terms of their accuracy. The recall scores for Class I, II, and III malocclusions were 0.71, 0.69, and 0.77, respectively, which represented the total number of actual positive cases predicted correctly, making the sensitivity of the model high. Conclusion: In this study, it is observed that the Random Forest model was the most accurate model for predicting the skeletal malocclusion based on various airway and cephalometric landmarks. [ABSTRACT FROM AUTHOR]

Background and purpose: Early detection, diagnosis, and treatment of colorectal cancer and its precancerous lesions can significantly improve patients' survival rates. The purpose of this research is to identify methylation markers specific to colorectal cancer tissues and validate their diagnostic capability in colorectal cancer and precancerous changes by measuring the level of DNA methylation in stool samples. Method: We analyzed samples from six cancer tissues and adjacent normal tissues and fecal samples from 758 participants, including 62 patients with interfering diseases. Bioinformatics databases were used to screen for candidate biomarkers for CRC, and quantitative methylation-specific PCR methods were applied for identification. The methylation levels of the candidate biomarkers in fecal and tissue samples were measured. Logistic regression and random forest models were built and validated using fecal sample data from one of the centers, and the independent or combined diagnostic value of the candidate biomarkers in fecal samples for CRC and precancerous lesions was analyzed. Finally, the diagnostic capability and stability of the model were validated at another medical center. Results: This study identified two colorectal cancer CpG sites with tissue specificity. These two biomarkers have certain diagnostic power when used individually, but their diagnostic value for colorectal cancer and colorectal adenoma is more significant when they are used in combination. Conclusion: The results indicate that a DNA methylation biomarker combined diagnostic model based on two CpG sites, cg13096260 and cg12587766, has the potential for screening and diagnosing precancerous lesions and colorectal cancer. Additionally, compared to traditional diagnostic models, machine learning algorithms perform better but may yield more false-positive results, necessitating further investigation. [ABSTRACT FROM AUTHOR]

Background: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database. Methods: A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction. Results: Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score. Conclusions: In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases. [ABSTRACT FROM AUTHOR]

Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge. [ABSTRACT FROM AUTHOR]

Alpine natural heritage sites hold significant value due to their unique global resources. Studying land cover changes in these areas is crucial for maintaining and preserving multiple their values. This study takes Kalajun-Kuerdening, one of the components of Xinjiang Tianshan World Natural Heritage Site, as an example to analyze land cover changes and their driving factors in alpine heritage sites. Highlights include: (1) Between 1994 and 2023, Forest and Grassland increased by 55.96 km2 and 18.16 km2, with notable forest growth from 2007 to 2017. Trends in Forest changes align with forest protection policies, and a substantial amount of Bareland converted to Grassland indicates an increase in vegetation cover. (2) Elevation, precipitation, temperature, and evapotranspiration are key drivers of land cover changes, as validated by Random Forest algorithm and Geodetector model. (3) Favorable conditions for Grassland to Forest transition include annual precipitation between 275 and 375 mm, annual temperature between −2 and 3 °C, annual evapotranspiration between 580 and 750 mm, elevation between 1800 and 2600 m, and aspect between 0 to 110° and 220 to 259.9°. Continuous monitoring of land cover changes and their driving factors in mountain heritage sites contributes to the protection of the ecological environment and provides data and information support for addressing climate change, resource management, and policy making. [ABSTRACT FROM AUTHOR]

When conducting condition recognition research on AC contactor vibration signals through time-frequency analysis, the feature data exhibit a high degree of redundancy, which leads to repetitive information and hinders the accuracy of recognition. To address the redundancy issue in the features of AC contactor vibration signals, this study introduces a feature selection method based on Regularized Random Forest with Recursive Selection (RFRS). Initially, a test platform for AC contactor vibration signals was established, and time-frequency domain features of the AC contactor vibration signals were extracted. Subsequently, the traditional Random Forest (RF) was refined by optimizing its stopping criteria using the Recursive Feature Elimination approach and by incorporating a regularization coefficient during the splitting process to direct the split towards significant features. This modification not only enhances the Random Forest's capacity to leverage existing information but also introduces a bias, enabling it to favor important features. Finally, through case analysis, the proposed method effectively reduced the dimensionality of the feature set and achieved an average of 87.37% for Recall, 87.41% for F1-Score, 88.38% for Precision, and 85.74% for Accuracy. The overall performance of this method surpasses that of the three mainstream feature selection methods: Spearman's rank correlation coefficient method, the embedded method, and the filter method. This study thus provides a rather effective feature selection approach for the state recognition study of AC contactors. [ABSTRACT FROM AUTHOR]

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients. [ABSTRACT FROM AUTHOR]

Metabolic syndrome (MetS) is a cluster of interconnected metabolic risk factors, including abdominal obesity, high blood pressure, and elevated fasting blood glucose levels, that result in an increased risk of heart disease and stroke. In this research, we aim to identify the risk factors that have an impact on MetS in the Bangladeshi population. Subsequently, we intend to construct predictive machine learning (ML) models and ultimately, assess the accuracy and reliability of these models. In this particular study, we utilized the ATP III criteria as the basis for evaluating various health parameters from a dataset comprising 8185 participants in Bangladesh. After employing multiple ML algorithms, we identified that 27.8% of the population exhibited a prevalence of MetS. The prevalence of MetS was higher among females, accounting for 58.3% of the cases, compared to males with a prevalence of 41.7%. Initially, we identified the crucial variables using Chi-Square and Random Forest techniques. Subsequently, the obtained optimal variables are employed to train various models including Decision Trees, Random Forests, Support Vector Machines, Extreme Gradient Boosting, K-nearest neighbors, and Logistic Regression. Particularly we employed the ATP III criteria, which utilizes the Waist-to-Height Ratio (WHtR) as an anthropometric index for diagnosing abdominal obesity. Our analysis indicated that Age, SBP, WHtR, FBG, WC, DBP, marital status, HC, TGs, and smoking emerged as the most significant factors when using Chi-Square and Random Forest analyses. However, further investigation is necessary to evaluate its precision as a classification tool and to improve the accuracy of all classifiers for MetS prediction. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) remains one of the most lethal urological malignancies even though a great number of improvements in diagnosis and management have achieved over the past few decades. Accumulated evidence revealed that histone deacetylases (HDACs) play vital role in cell proliferation, differentiation and apoptosis. Nevertheless, the biological functions of histone deacetylation modification related genes in ccRCC remains poorly understood. Method: Bulk transcriptomic data and clinical information of ccRCC patients were obtained from the TCGA database and collected from the Chinese PLA General Hospital. A total of 36 histone deacetylation genes were selected and studied in our research. Univariate cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, random forest (RF) analysis, and protein-protein interaction (PPI) network analysis were applied to identify key genes affecting the prognosis of ccRCC. The 'oncoPredict' algorithm was utilized for drug-sensitive analysis. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to explore the potential biological function. The ssGSEA algorithm was used for tumor immune microenvironment analysis. The expression levels of HDAC10 were validated by RT-PCR and immunohistochemistry (IHC). 5-ethynyl-2′-deoxyuridine (EdU assay), CCK-8 assay, cell transwell migration and invasion assay and colony formation assay were performed to detect the proliferation and invasion ability of ccRCC cells. A nomogram incorporating HDAC10 and clinicopathological characteristics was established to predict the prognosis of ccRCC patients. Result: Two machine learning algorithms and PPI analysis identified four histone deacetylation genes that have a significant association with the prognosis of ccRCC, with HDAC10 being the key gene among them. HDAC10 is highly expressed in ccRCC and its high expression is associated with poor prognosis for ccRCC patients. Pathway enrichment and the experiments of EdU staining, CCK-8 assay, cell transwell migration and invasion assay and colony formation assay demonstrated that HDAC10 mediated the proliferation and metastasis of ccRCC cells and involved in reshaping the tumor microenvironment (TME) of ccRCC. A clinically reliable prognostic predictive model was established by incorporating HDAC10 and other clinicopathological characteristics (https://nomogramhdac10.shinyapps.io/HDAC10%5fNomogram/). Conclusion: Our study found the increased expression of HDAC10 was closely associated with poor prognosis of ccRCC patients. HDAC10 showed a pro-tumorigenic effect on ccRCC and promote the proliferation and metastasis of ccRCC, which may provide new light on targeted therapy for ccRCC. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to assess the use of machine learning methodologies in the diagnosis of endometriosis (EM). Methods: This study included a total of 106 patients with EM and 203 patients with non-EM conditions (like simple cysts and simple uterine fibroids), all admitted to the Shunyi Women's and Children's Hospital of Beijing Children's Hospital between January 2017 and September 2022. All participants were free of comorbidities and their diagnoses were confirmed via postoperative pathology. Comparative analysis was conducted between the EM and non-EM groups. Baseline data were assessed, including white blood cell count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, mean platelet volume, hemoglobin, carbohydrate antigen 125 (CA125), carbohydrate antigen 199, coagulation parameters, and other serologic indicators. An optimal predictive model was developed using an artificial intelligence algorithm to determine the presence of EM. The objective is to provide new insights for the clinical diagnosis and treatment of EM. Results: The random forest algorithm demonstrated superior performance when compared to decision trees, LogitBoost, artificial neural networks, naïve Bayes, support vector machines, and linear regression in machine learning methods. Combining CA125 with the NLR yielded a better prediction of EM than using CA125 alone when applying the random forest algorithm. The accuracy of predicting EM with CA125 combined with NLR was 78.16%, with a sensitivity of 86.21% and an area under the curve (AUC) of 0.85 (P < 0.05). In contrast, using CA125 alone resulted in an EM prediction accuracy of 75.8%, with a sensitivity of 79.3% and an AUC of 0.82 (P < 0.05). Conclusion: The diagnostic value of serum CA125 combined with the NLR for EM is higher than that of serum CA125 alone. This finding indicates that NLR could serve as a new supplementary biomarker along with serum CA125 in the diagnosis of EM. [ABSTRACT FROM AUTHOR]

Many species of fishes around the world are soniferous. The types of sounds fishes produce vary among species and regions but consist typically of low-frequency (< 1.5 kHz) pulses and grunts. These sounds can potentially be used to monitor fishes non-intrusively and could complement traditional monitoring techniques. However, the significant time required for human analysts to manually label fish sounds in acoustic recordings does not yet allow passive acoustics to be used as a viable tool for monitoring fishes. In this paper, we compare two different approaches to automatically detect fish sounds. One is a more traditional machine learning technique based on the detection of acoustic transients in the spectrogram and the classification using Random Forest (RF). The other is using a deep learning approach and is based on the classification of overlapping segments (0.2 s) of spectrogram using a ResNet18 Convolutional Neural Network (CNN). Both algorithms were trained using 21,950 manually annotated fish and non-fish sounds collected from 2014 to 2019 at five different locations in the Strait of Georgia, British Columbia, Canada. The performance of the detectors was tested on part of the data from the Strait of Georgia that was withheld from the training phase, data from Barkley Sound, British Columbia, and data collected in the Port of Miami, Florida, United States. The CNN performed up to 1.9 times better than the RF (F1 score: 0.82 vs. 0.43). In some cases, the CNN was able to find more faint fish sounds than the analyst and performed well in environments different from the one it was trained in (Miami F1 score: 0.88). Noise analysis in the 20--1,000 Hz frequency band shows that the CNN is still reliable in noise levels greater than 130 dB re 1 μPa in the Port of Miami but becomes less reliable in Barkley Sound past 100 dB re 1 μPa due to mooring noise. The proposed approach can efficiently monitor (unidentified) fish sounds in a variety of environments and can also facilitate the development of species-specific detectors. We provide the software FishSound Finder, an easy-to-use open-source implementation of the CNN detector with detailed documentation. [ABSTRACT FROM AUTHOR]

This study examines the impact of internet usage frequency and behavior on social status discordance using CSS2021 data, and adopts a causal random forest model to test the heterogeneity of the impact of internet usage on social status discordance. The results reveal a clear inconsistency between objective and subjective social status among Chinese residents. In addition, increasing internet usage frequency can make actors more inclined to underestimate their social status. Moreover, capital enhancement through the internet significantly reduces the likelihood of status inflation. Finally, individuals with higher social status are less likely to have an upward bias in status cognition than those with lower social status when they use the internet frequently and use it for capital enhancement. These findings advance our understanding of social status identification among Chinese residents and provide different perspectives for studying social stratification in the digital age. [ABSTRACT FROM AUTHOR]

Motivation In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. Meanwhile, clinical datasets are often small and must be aggregated from multiple hospitals. Online data sharing, however, is seen as a significant challenge due to privacy concerns, potentially impeding big data's role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing. Results We introduce a novel multi-omics clustering approach utilizing unsupervised random forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark datasets as well as on cancer data from The Cancer Genome Atlas. Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing. Availability and implementation The proposed methods are available as an R-package (https://github.com/pievos101/uRF) [ABSTRACT FROM AUTHOR]

Objective: Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification models. We focus on quantifying the effects of different imbalance degrees and sample sizes on model performance, identifying optimal cut-off values, and evaluating the efficacy of various methods to enhance model accuracy in highly imbalanced and small sample size scenarios. Methods: We collected medical records of patients receiving assisted reproductive treatment in a reproductive medicine center. Random forest was used to screen the key variables for the prediction target. Various datasets with different imbalance degrees and sample sizes were constructed to compare the classification performance of logistic regression models. Metrics such as AUC, G-mean, F1-Score, Accuracy, Recall, and Precision were used for evaluation. Four imbalance treatment methods (SMOTE, ADASYN, OSS, and CNN) were applied to datasets with low positive rates and small sample sizes to assess their effectiveness. Results: The logistic model's performance was low when the positive rate was below 10% but stabilized beyond this threshold. Similarly, sample sizes below 1200 yielded poor results, with improvement seen above this threshold. For robustness, the optimal cut-offs for positive rate and sample size were identified as 15% and 1500, respectively. SMOTE and ADASYN oversampling significantly improved classification performance in datasets with low positive rates and small sample sizes. Conclusions: The study identifies a positive rate of 15% and a sample size of 1500 as optimal cut-offs for stable logistic model performance. For datasets with low positive rates and small sample sizes, SMOTE and ADASYN are recommended to improve balance and model accuracy. [ABSTRACT FROM AUTHOR]

Traditionally, the design of an expert system involves acquiring knowledge, in the form of symbolic rules, directly from the expert(s), which is a complex and time‐consuming task. Although expert systems approach is quite old, it is still present, especially where explicit knowledge representation and reasoning, which assure interpretability and explainability, are necessary. Therefore, machine learning methods have been devised to extract rules from data, to facilitate that task. However, those methods are quite inflexible in adapting to the application domain and provide no help in designing the expert system. In this work, we present a framework and corresponding tool, namely ACRES, for semi‐automatically generating expert systems from datasets. ACRES allows for data preprocessing, which helps in structuring knowledge in the form of a tree, called rule hierarchy, which represents (possible) dependencies among data variables and is used for rule formation. This improves interpretability and explainability of the produced systems. We have also designed and evaluated alternative methods for rule extraction from data and for calculation and use of certainty factors, to represent uncertainty; CFs can be dynamically updated. Experimental results on seven well‐known datasets show that the proposed rule extraction methods are comparable to other popular machine learning approaches like decision trees, CART, JRip, PART, Random Forest, and so on, for the classification task. Finally, we give insights on two applications of ACRES. [ABSTRACT FROM AUTHOR]

Combining different drugs synergistically is an essential aspect of developing effective treatments. Although there is a plethora of research on computational prediction for new combination therapies, there is limited to no research on combination therapies in the treatment of viral diseases. This paper proposes AI-based models for predicting novel antiviral combinations to treat virus diseases synergistically. To do this, we assembled a comprehensive dataset comprising information on viral strains, drug compounds, and their known interactions. As far as we know, this is the first dataset and learning model on combination therapy for viruses. Our proposal includes using a random forest model, an SVM model, and a deep model to train viral combination therapy. The machine learning models showed the highest performance, and the predicted values were validated by a t-test, indicating the effectiveness of the proposed methods. One of the predicted combinations of acyclovir and ribavirin has been experimentally confirmed to have a synergistic antiviral effect against herpes simplex type-1 virus, as described in the literature. [ABSTRACT FROM AUTHOR]

The chloride migration coefficient (CMC) of concrete is crucial for evaluating its durability. This study develops ensemble models to predict the CMC of concrete, addressing the limitations of traditional, labor-intensive laboratory tests. We developed three ensemble models: an inverse variance-based model, an Artificial Neural Network (ANN)-based model, and a tree-based model using the random forest regression algorithm. These models were trained on a dataset comprising 843 concrete mix proportions from existing literature. Results indicate that ensemble models outperform single models such as ANN and Support Vector Regression (SVR) in predicting CMC, with the combined random forest and ANN model showing the highest accuracy. Sensitivity analysis using Shapley Additive Explanations (SHAP) reveals that the CMC is most influenced by the water-to-cement ratio and curing age. Additionally, we designed a graphical user interface (GUI) to facilitate the practical application of our models. This research offers a robust methodology for evaluating concrete durability and potential for extending the prediction to other concrete properties. [ABSTRACT FROM AUTHOR]

Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond. [ABSTRACT FROM AUTHOR]

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min–Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635–0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC. [ABSTRACT FROM AUTHOR]

Hazel dormice Muscardinus avellanarius have severely declined since 2000 leading to increased legislative protection in the UK and Europe. Artificial nestboxes are widely used for its conservation and monitoring. Previous research has focused on how to identify suitable areas for nestboxes, but where to place individual boxes to promote occupancy is less well understood. Here, we demonstrate the use of machine learning Random Forest regression to predict nestbox occupancy from a wide range of microhabitat variables using a UK woodland as a case study. Random forest models are powerful predictive tools that allow simultaneous testing of many predictors with relatively few observations. Field data included observed nestbox occupancy (2017–2021) and measurements of 76 microhabitat variables collected in the summer of 2021 from 45 occupied and unused nestboxes located in a deciduous woodland in Berkshire, UK. We applied Random Forest regression to identify important variables and predict nestbox occupancy demonstrating robust approaches to tune model hyperparameters and evaluate importance metrics. In our study area, nestboxes were more likely to be occupied in sites with more hazel Corylus avellana, greater overall tree abundance but not fully closed canopies (optimal 80–85%), more honeysuckle Lolium periclymenum and hawthorn Crataegus monogyna, and when located further from footpaths and woodland margins. Occupancy over the study period was well predicted using microhabitat variables (13.3% OOB error) but future occupancy was more uncertain (33.3% error for 2021–2023 records). Modelling approaches that allow consideration of numerous variables from few locations or observations can be help identify relevant features and predict desirable outcomes of conservation actions. Here we demonstrate this approach identifying microhabitat variables that influence artificial nestbox occupancy by hazel dormice in a UK woodland. Findings offer some recommendations for local management that could promote nestbox occupancy and improve monitoring and conservation efforts. [ABSTRACT FROM AUTHOR]

This study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers' visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time‐consuming, this method can rapidly generate optimized visual images of the facility environment and promptly verify the optimization effects. In this study, a visual road schema model is established to quantify the facility environment from drivers' visual perception, and an automated optimization scheme determination approach considering the original facility environment characteristics is proposed using self‐explaining theory. Then, Cycle‐consistent generative adversarial network is used to automatically generate optimized facility environment images. To verify the optimization effect, operation speeds of the optimized facility environments are predicted using random forest. The case study shows that this method can effectively optimize the facility environment where original operation speeds are more than 20% over the speed limits, and the whole process only takes 1 h far less than several months or years in previous ways. Overall, this study advances the intelligence level in optimizing the facility environment and enhances rural road safety. [ABSTRACT FROM AUTHOR]

Accurate and reliable forecasting of cancellations is important for successful revenue management in the tourism industry. The objective of this study is to develop classification models to predict hotel booking cancellations. The work involves a number of key steps, such as data preprocessing to properly prepare the data; feature engineering to identify relevant attributes to help improve the predictive ability of the models; hyperparameter settings of the models, including choice of optimizers and incorporation of dropout layers to avoid overfitting in the neural networks; potential overfitting is evaluated using K‐fold cross‐validation; and performance is analysed using the confusion matrix and various performance metrics. The algorithms used are Multilayer Perceptron Neural Network, Radial Basis Function Neural Network, Deep Neural Network, Decision Tree Classifier, Random Forest Classifier, Ada Boost Classifier and XgBoost Classifier. Finally, the results of all models are compared, visualizing Deep Neural Network and XgBoost as the most suitable models for predicting hotel reservation cancellations. [ABSTRACT FROM AUTHOR]

Aims: Although there are various model‐based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)‐based model for predicting VCM trough concentration in adult PJI patients. Methods: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP). Results: The SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set. Conclusion: The proposed ML‐based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment. [ABSTRACT FROM AUTHOR]

Construction firms face considerable challenges in relation to finding cost-effective formwork solutions to meet increased construction demands. Project stakeholders have relied on self-consolidating concrete (SCC) to speed up the construction time because SCC is highly fluid and has numerous advantages compared to traditional concrete. To withstand SCC's high fluidity, formwork systems should be robust. Although previous research has experimentally examined various characteristics of SCC, few research studies have used machine-learning algorithms to estimate or predict the lateral pressure exerted by SCC on formwork systems. Hence, this study addressed this knowledge gap by proposing a machine-learning approach to predict the lateral pressure of SCC on vertical formwork systems. First, laboratory tests were performed to collect data on lateral pressure measurements, material factors, placement conditions, and formwork characteristics affecting the SCC lateral pressure on formwork systems. Second, four supervised machine-learning algorithms were considered in this study: k-nearest neighbor (KNN), artificial neural network (ANN), decision tree (DT), and random forest (RF). Third, the hyperparameters of the machine-learning algorithms were tuned, and their performance metrics were compared. Fourth, the most accurate predictive machine-learning model was verified on an unseen testing set. The results showed that the RF machine-learning algorithm was the best model for predicting the lateral pressure of SCC on formwork systems, with a mean percentage error of 0.8%, a mean absolute percentage error of 4.29%, and a coefficient of determination R2 of 0.9548. This study adds to the construction engineering and management body of knowledge by developing a machine-learning predictive model that can be used to accurately assess the lateral pressure exerted by SCC on formwork, which helps to ensure safe design of formwork systems and economic construction operations in formwork-related activities. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP) is an excellent building material for strengthening concrete structures, but it is difficult to accurately evaluate the bond strength of FRP-to-concrete due to the influence of various parameters. In this study, a novel hybrid model which combines particle swarm optimization (PSO) with random forest (RF) was proposed to predict the bond strength of FRP-to-concrete. The PSO algorithm was used to optimize the hyperparameters of the RF model. A total of 749 specimens collected from the literature were used to develop the proposed PSO-RF model. Each sample contains 11 parameters required for the model. These 11 parameters are (1) the compressive strength of concrete, (2) the tensile strength of concrete, (3) the width of concrete specimen, (4) the maximum aggregate size of concrete, (5) the tensile strength of FRP, (6) the thickness of FRP, (7) the elastic modulus of FRP, (8) the tensile strength of adhesive, (9) the bond length of FRP, (10) the bond width of FRP, and (11) the bond strength of FRP-to-concrete. The proposed PSO-RF model was compared with other machine learning models as well as ten empirical equations. Six statistical indices, namely root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Willmott's Index of Agreement (WIA), and Legates-McCabe's Index (LM) were used to evaluate the prediction performance of the abovementioned models. The results show that the RMSE, MAE, R2, NSE, WIA and LM values of the PSO-RF model are 1.529 kN, 0.942 kN, 0.986, 0.984, 0.996 and 0.892, respectively, for the training datasets and 2.672 kN, 1.967 kN, 0.963, 0.961, 0.989 and 0.761, respectively, for the test datasets. It can be concluded that the proposed PSO-RF model has the best comprehensive performance in predicting the bond strength of FRP-to-concrete. In addition, the sensitivity analysis of the PSO-RF model was also conducted in this study. [ABSTRACT FROM AUTHOR]

Soil classification methods currently rely on soil borings or cone/piezocone penetrometer tests (CPT/CPTu). Literature provides several methods that classify soils based on two parameters (typically tip resistance and friction sleeve/porewater pressure) obtained from CPTu data, defining a soil behavior type. However, these methods face challenges in reliably classifying certain soils, such as organic soils. Robust and complex analyses are required to classify organic soils accurately. In this study, a random forest (RF) based method is developed to predict the presence and depth of organic soils. The RF model utilized features from CPTu soundings including tip resistance, sleeve friction, and pore pressure. The true location of organic soils, derived from index properties obtained from soil borings, served as the model's outputs. Unseen CPTu data was used to predict organic soils throughout the entire project alignment, serving as validation for the model. The model achieved an F1 score of 0.89. Settlement analyses were conducted to evaluate the practical implications of the model's predictions on levee fill costs. The RF-based model yielded settlement predictions that closely matched those obtained through boring predictions. In contrast, traditional classification methods underestimated settlements, resulting in lower estimates for levee fill costs. [ABSTRACT FROM AUTHOR]

[Display omitted] • A flexible pressure sensor was constructed with MAPbBr 3 in the matrix of PVDF. • A porous polymer composite with superhydrophobicity was synthesized in situ. • The mechanism of the sensor's response is explained by the DFT and MEPs. • The sensor can monitor human physiological signals and wind speed very well. • Feature engineering and machine learning improved detection accuracy in 60 tests. The application of pressure sensors based on perovskite in high-humidity environments is limited by the effect of water on their stability. Endowing sensors with superhydrophobicity is an effective strategy to overcome the issue. In this work, MAPbBr 3 /Polyvinylidene Fluoride-TFSI composite was prepared by a one-step in-situ strategy to form a flexible superhydrophobic pressure sensor, which exhibited a contact angle of 150.25°. The obtained sensor exhibited a sensitivity of 0.916 in 1 kPa, a detection limit of 0.2 Pa, a precision of 0.1 Pa, and a response/recovery of ∼100 ms, along with good thermal stability. Through density functional theory calculations, it is revealed that the formation of the porosity is attributed to the interaction between the polymer and EMIM TFSI, which further leads to superhydrophobicity. And, the perovskite structure is easy to change under pressure, affecting the carrier transport and electrical signals output, which explains the sensing mechanism. In addition, the sensor performed well in monitoring facial expression, pulse, respiration, finger bending, and wind speed ranging from 1 m/s to 6 m/s. With both the Linear Regression and the Random Forest algorithm, the sensor can monitor the wind speed with an R2 greater than 0.977 in 60 tests. [ABSTRACT FROM AUTHOR]

Disposable email address services have gained popularity recently, offering users a way to register for online services without revealing their primary email. However, this poses challenges for organizations aiming to engage with genuine users. This paper introduces a novel approach combining natural language processing (NLP) and domain validation to identify disposable email addresses. The technique employs various machine learning methods, including Support Vector Classifier (SVC), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes (GNB), Logistic Regression, XGBoost Classifier, K Neighbors Classifier, Random Forest Classifier, Linear, and Discriminant Analysis. Unlike traditional methods relying on blacklists, this technique achieves a 97% accuracy rate by effectively detecting and classifying new disposable emails. [ABSTRACT FROM AUTHOR]

With the growing availability of medical data and the enhanced performance of computers, new opportunities for data analysis in research are emerging. One of these modern approaches is machine learning (ML), an advanced form of statistics broadly defined as the application of complex algorithms. ML provides innovative methods for detecting patterns in complex datasets. This enables the identification of correlations or the prediction of specific events. These capabilities are especially valuable for multifactorial phenomena, such as those found in mental health and forensic psychiatry. ML also allows for the quantification of the quality of the emerging statistical model. The present study aims to examine various sociodemographic variables in order to detect differences in a sample of 370 offender patients and 370 non-offender patients, all with schizophrenia spectrum disorders, through discriminative model building using ML. In total, 48 variables were tested. Out of seven algorithms, gradient boosting emerged as the most suitable for the dataset. The discriminative model finally included three variables (regarding country of birth, residence status, and educational status) and yielded an area under the curve (AUC) of 0.65, meaning that the statistical discrimination of offender and non-offender patients based purely on the sociodemographic variables is rather poor. [ABSTRACT FROM AUTHOR]

Free speech is essential, but it can conflict with protecting marginalized groups from harm caused by hate speech. Social media platforms have become breeding grounds for this harmful content. While studies exist to detect hate speech, there are significant research gaps. First, most studies used text data instead of other modalities such as videos or audio. Second, most studies explored traditional machine learning algorithms. However, due to the increase in complexities of computational tasks, there is need to employ complex techniques and methodologies. Third, majority of the research studies have either been evaluated using very few evaluation metrics or not statistically evaluated at all. Lastly, due to the opaque, black-box nature of the complex classifiers, there is need to use explainability techniques. This research aims to address these gaps by detecting hate speech in English and Kiswahili languages using videos manually collected from YouTube. The videos were converted to text and used to train various classifiers. The performance of these classifiers was evaluated using various evaluation and statistical measurements. The experimental results suggest that the random forest classifier achieved the highest results for both languages across all evaluation measurements compared to all classifiers used. The results for English language were: accuracy 98%, AUC 96%, precision 99%, recall 97%, F1 98%, specificity 98% and MCC 96% while the results for Kiswahili language were: accuracy 90%, AUC 94%, precision 93%, recall 92%, F1 94%, specificity 87% and MCC 75%. These results suggest that the random forest classifier is robust, effective and efficient in detecting hate speech in any language. This also implies that the classifier is reliable in detecting hate speech and other related problems in social media. However, to understand the classifiers' decision-making process, we used the Local Interpretable Model-agnostic Explanations (LIME) technique to explain the predictions achieved by the random forest classifier. [ABSTRACT FROM AUTHOR]

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Communication networks are crucial components of the underlying digital infrastructure in any smart city setup. The increasing usage of computer networks brings additional cyber security concerns, and every organization has to implement preventive measures to protect valuable data and business processes. Due to the inherent distributed nature of the city infrastructures as well as the critical nature of its resources and data, any solution to the attack detection calls for distributed, efficient and privacy preserving solutions. In this paper, we extend the evaluation of our federated learning framework for network attacks detection and classification based on random forest. Previously the framework was evaluated only for attack detection using four well-known intrusion detection datasets (KDD, NSL-KDD, UNSW-NB15, and CIC-IDS-2017). In this paper, we extend the evaluation for attack classification. We also evaluate how adding differential privacy into random forest, as an additional protective mechanism, affects the framework performances. The results show that the framework outperforms the average performance of independent random forests on clients for both attack detection and classification. Adding differential privacy penalizes the performance of random forest, as expected, but the use of the proposed framework still brings benefits in comparison to the use of independent local models. The code used in this paper is publicly available, to enable transparency and facilitate reproducibility within the research community. [ABSTRACT FROM AUTHOR]

Surface waters are essential for meeting the needs of the world. In many regions, stream water quality is a major concern due to contamination from multiple sources. Stream water is also susceptible to climatic events and land-use practices influencing its catchment. Understanding the impact of such events on stream water quality is crucial for managing and protecting aquatic ecosystems and providing safe drinking water to communities that rely on these streams. Hence, monitoring and evaluating stream water quality holds significance in identifying potential hazards and implementing suitable management strategies. In this paper, a novel effort was made to determine the relative feature importance of a set of watershed characteristics (precipitation, temperature, urban land use, agricultural land use, and forest land-use factors) on four important water quality parameters (WQPs): fecal coliforms (FC), turbidity, pH, and conductivity of the Upper Green River watershed, Kentucky, USA. Random forest (RF), an ensemble learning method, was used to predict the WQPs from the causal parameters and determine the feature importance characteristics of the four WQPs previously mentioned. This model demonstrated that precipitation and temperature are the most influential factors on FC, turbidity, and pH. Forest land use and temperature are the two most important factors for conductivity. The novel feature importance factors of the RF model have likewise been confirmed for each WQP. In modeling stream WQPs, the developed the RF model outperformed the artificial neural network (ANN) model. Using the RF model, we obtain regression coefficients of (0.93, 0.74, and 0.94) for pH in training, testing, and overall. We obtain regression coefficients of (0.60, 0.64, and 0.61) using the ANN model. ⁠⁠⁠⁠⁠⁠⁠Overall, the RF model was more effective than the ANN model in modeling stream WQPs. The model identified precipitation and temperature as the most influential factors on FC, turbidity, and pH, while forest land use and temperature were the most important factors in determining conductivity. It is also found that land use factors are important to improve the accuracy of WQPs predictions from climate variables. The results of this study can be used by authorities to better understand and control pollution at the watershed scale. [ABSTRACT FROM AUTHOR]

This paper examines the reproducibility of learned explanations for black-box predictions via model distillation using classification trees. We find that common tree distillation methods fail to reproduce a single stable explanation when applied to the same teacher model due the randomness of the distillation process. We study this issue of reliable interpretation and propose a standardized framework for tree distillation to achieve reproducibility. The proposed framework consists of (1) a statistical test to stabilize tree splits, and (2) a stopping rule for tree building when using a teacher that provides an estimate of the uncertainty of its predictions, e.g. random forests. We demonstrate the empirical performance of the proposed distillation method on a variety of synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

This paper introduces a "Smart Fertilizing Using Internet of Things (IoT) Multi-Sensors" system to enhance fertilizer management in agriculture. The system has four main parts: the Nutro Determining Unit (NDU), the Nutro Sensing Unit (NSU), the Nutro UAV Variable Fertiliser Spray System, and a Variable Rate Unmanned Aerial Vehicle (UAV) Sprayer model. The NDU collects vital data on Soil Moisture (SM) and Environmental Conditions (EnC) using advanced IoT cameras, while the NSU consolidates and normalises the data for advanced analysis using Heuristic Decision Trees (HDT) and Random Forest (RF) algorithms. In India, a data-driven UAV system uses IoT and UAV technologies to determine nutrient needs and create a prescription map for fertilizer application. The approach caused increases in the efficient utilisation of resources, Crop Yield (CY), and ecological footprint when it underwent evaluation in a crop maize field that was 14 hectares in size. A fresh benchmark for Smart Farming (SF) techniques has been set up by this method of operation, which is motivated by data and symbolises an important innovation in modern and ecologically conscious SF methods. [ABSTRACT FROM AUTHOR]

In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple machines learning models, aiming to contribute to developing an efficient learning system. Fifteen distinct machine learning (ML) classifiers were implemented, among them AdaBoost (ADA), gradient boosting (GBC), extreme gradient boosting (XGboost), extra trees (ET), random forest (RF), light gradient boosting machine (light gum), and decision tree (DT) algorithms standouts. The DT exhibited exceptional performance across metrics such as area under the curve (AUC), accuracy, recall, F1 score, Kappa, precision, and matthews correlation coefficient (MCC). It achieved nearly 1.0 in these metrics while taking a short training time of only 1.7 seconds. This reveals its potential as an efficient classifier for electroencephalography (EEG) datasets and highlights its viability for practical implementation. [ABSTRACT FROM AUTHOR]

The world was affected by a novel coronavirus in December 2019 that changed human life. Several types of research have been done, substantial scientific advances have been made, and millions of dollars have been spent on bringing scholars and scientists to one platform to end this critical pandemic. Ascertaining COVID-19 diagnoses in the initial stage of the pandemic was critical, specifically for patients with no manifestations. In this case, artificial intelligence-based systems were proposed to identify the virus at an earlier phase. Thus, the present study suggests a machine vision scheme to identify COVID-19 from chest X-ray images. Three machine learning approaches, such as logistic regression (LR), decision tree (DT), and random forest (RF), were implemented with more than 95% accuracy. The deep convolutional neural network (CNN) architecture was also proposed and implemented with a 99.99% detection rate. Therefore, the present work can effectively detect COVID-19 cases in the early stages. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production. OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods. METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis. RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized. OBJECTIVES: Aiming at the problems of inefficiency, incomplete index system, and low assessment accuracy in evaluation methods of higher vocational colleges and universities. METHODS: Proposes a teaching evaluation method for higher vocational colleges and universities with a big data mining algorithm and an intelligent optimization algorithm. Firstly, the teaching evaluation index system of higher vocational colleges and universities is downgraded and analyzed by using principal component analysis; then, the random forest hyperparameters are optimized by the grey wolf optimization algorithm, and the teaching evaluation model of higher vocational colleges and universities is constructed; finally, the validity and stability of the proposed method is verified by simulation experimental analysis. RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. CONCLUSION: Solves the problems of low evaluation accuracy, incomplete system, and low efficiency of teaching evaluation methods in higher vocational colleges. [ABSTRACT FROM AUTHOR]

A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, and suitable therapies are available. The purpose of this study is to develop a stroke prediction model that will improve stroke prediction effectiveness as well as accuracy. Predicting whether someone is suffering from a stroke or not can be accomplished with this proposed machine learning algorithm. In this research, various machine learning techniques are evaluated for predicting stroke on the healthcare stroke dataset. The feature selection algorithms used here are gradient boosting and random forest, and classifiers include the decision tree classifier, Support Vector Machine (SVM) classifier, logistic regression classifier, gradient boosting classifier, random forest classifier, K neighbors classifier, and Xtreme gradient boosting classifier. In this process, different machine-learning approaches are employed to test predictive methods on different data samples. As a result obtained from the different methods applied, and the comparison of different classification models, the random forest model offers an accuracy rate of 98%. [ABSTRACT FROM AUTHOR]

Background: Systemic inflammatory response syndrome (SIRS) is a predictor of serious infectious complications, organ failure, and death in patients with severe polytrauma and is one of the reasons for delaying early total surgical treatment. To determine the risk of SIRS within 24 h after hospitalization, we developed six machine learning models. Materials and methods: Using retrospective data about the patient, the nature of the injury, the results of general and standard biochemical blood tests, and coagulation tests, six models were developed: decision tree, random forest, logistic regression, support vector and gradient boosting classifiers, logistic regressor, and neural network. The effectiveness of the models was assessed through internal and external validation. Results: Among the 439 selected patients with severe polytrauma in 230 (52.4%), SIRS was diagnosed within the first 24 h of hospitalization. The SIRS group was more strongly associated with class II bleeding (39.5% vs. 60.5%; OR 1.81 [95% CI: 1.23–2.65]; P = 0.0023), long-term vasopressor use (68.4% vs. 31.6%; OR 5.51 [95% CI: 2.37–5.23]; P < 0.0001), risk of acute coagulopathy (67.8% vs. 32.2%; OR 2.4 [95% CI: 1.55–3.77]; P < 0.0001), and greater risk of pneumonia (59.5% vs. 40.5%; OR 1.74 [95% CI: 1.19–2.54]; P = 0.0042), longer ICU length of stay (5 ± 6.3 vs. 2.7 ± 4.3 days; P < 0.0001) and mortality rate (64.5% vs. 35.5%; OR 10.87 [95% CI: 6.3–19.89]; P = 0.0391). Of all the models, the random forest classifier showed the best predictive ability in the internal (AUROC 0.89; 95% CI: 0.83–0.96) and external validation (AUROC 0.83; 95% CI: 0.75–0.91) datasets. Conclusions: The developed model made it possible to accurately predict the risk of developing SIRS in the early period after injury, allowing clinical specialists to predict patient management tactics and calculate medication and staffing needs for the patient. Level of evidence: Level 3. Trial registration: The study was retrospectively registered in the ClinicalTrials.gov database of the National Library of Medicine (NCT06323096). [ABSTRACT FROM AUTHOR]