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In the era of advanced cyber developments, intrusions becomes a common event in any network. Although there are research studies and developers found ways to improve the detection models, there is some problem that persists in the intrusion models such as extracting key features from a large dataset, and delayed detection is a critical issue that needs to be addressed. Hence the proposed study aimed to develop a model that could extract key features from the dataset and use them effectively in the detection of threats. The study incorporates two approaches, one is feature extraction by the K-Nearest Neighbourhood, and feature selection by the K-Best approach. And the other is the balanced Gini-Entropy approach for the Random Forest (RF) classifier. This combined approach by KNN, K-best, and RF is referred to as (KK-RF). This combined approach of feature extraction, selection, and classification results in an effective threat detection model with high accuracy of about 99.61%. Moreover the proposed model has achieved precision and the recall rates of 97.3 and 96.6% respectively. Concurrently, the model attained markable F1-score of 96.6 respectively. Also, from the comparison results, it is observed that the proposed model had higher performance. [ABSTRACT FROM AUTHOR]

Polycystic Ovary Syndrome (PCOS) is a hormonal state that affects women within the reproductive age group. Early and accurate diagnosis of medical conditions is crucial for the successful implementation of medication and prevention of significant health problems. In recent times, there has been an increasing amount of evidence suggesting that machine learning (ML) methods have significant potential in facilitating the diagnosis and classification of PCOS. The objective of this research is to evaluate the viability of using several machine learning algorithms, such as Random Forests, Decision Trees, Artificial Neural Networks, and XGBoost, for the purpose of classifying PCOS using clinical and laboratory features. Furthermore, the use of the Sine Cosine Algorithm was implemented for the purpose of selecting features in order to improve the accuracy and efficiency of the model. A sample of 500 individuals was selected for the purpose of training and assessing the models, with half of the participants (250) diagnosed with PCOS and the other half not exhibiting the disease. The findings suggest that the classification algorithms had strong performance in classifying PCOS, with XGBoost having the best level of performance. In addition, the use of the Sine Cosine Algorithm resulted in enhanced model precision and effectiveness via the identification of the most pertinent features for the classification objective. The results of this study emphasise the significant capabilities of machine learning methods, namely XGBoost and the Sine Cosine Algorithm, in aiding the diagnosis and classification of PCOS in a medical setting. Additional investigation is required in order to substantiate the models and prove their clinical significance in the provision of patient care. [ABSTRACT FROM AUTHOR]

As a crucial node in the East Asia–Australasia migratory bird network, the Liaohe River Estuary Wetland (LREW) contributes significantly to coastal ecological balance and biodiversity. Accurate and timely mapping of the LREW is essential for monitoring ecological changes and understanding shifts in the wetland's ecosystem structure and functions. In this study, an innovative approach that integrates radar (Sentinel-1), optical (Sentinel-2), and DEM data on Google Earth Engine (GEE) is proposed to achieve a more optimized and accurate wetland mapping. Recursive Feature Elimination (RFE) is employed to improve feature collection, facilitating the construction of an optimal feature set for Random Forest (RF) classification to extract detailed LREW information. Mapping conducted from 2017 to 2023 using the proposed approach demonstrates its effectiveness and stability in mapping the LREW, achieving overall accuracies ranging from 89.84% to 93.73% and Kappa from 0.85 to 0.91. Compared to single-source methods, the integration of multi-source data substantially enhances mapping accuracy, while RF-RFE effectively eliminates redundant features, further refining classification precision. Texture features (Sum Average, SAVG) and the Water Index 2019 (WI2019) were found to significantly influence wetland mapping. The importance of different feature types is ranked as follows: spectral features > vegetation/water body index > PCA features > SAR texture features > red-edge index > other spectral index > spectral texture features > backscatter coefficient > H-Alpha. During the study period, the wetland area increased from 849.60 km2 to 979.49 km2. This expansion was caused primarily by wetland protection policies that led to the dismantling of coastal aquaculture ponds, resulting in a reduction of 90.28 km2 in waterbodies over 7 years, with many areas transitioning to tidal flats and Suaeda salsa habitats. In conclusion, the proposed method is effective in mapping the LRE wetlands and provides reliable data support for wetland ecological protection and restoration. [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]

Case-based reasoning (CBR) is widely used in medical decision support systems because of its similarity to human reasoning. Despite the effectiveness of the latter, its performance can be improved when reinforced by other automatic learning techniques. This is what induced us to propose a hybrid model 3FS-CBR-IRF (Three feature selection- Case based reasoning- Improved random forest) that combines case-based reasoning and random forests. Firstly, a feature selection step is used by three techniques such as: Correlation, Dropping Constant Features and Chi-square to select the relevant features. Secondly, we have modeled the retrieval phase of the CBR system by the modified random forests; in this step two pruning methods (Pre-pruning and Post-pruning) are applied only to the highly ranked features to improve the performance of the CBR system. Thirdly, the adaptation phase of the retrieval cases based on the adaptation rules is proposed. The developed approach is evaluated on 13 medical databases using the precision and several other performance criteria, the results obtained are satisfactory with an average precision of 91% for the first model (CBR-FS-RF) which improves the CBR system by feature selection techniques with classic random forest, 97% for the model (CBR-FS-IRF) which improve the CBR system by feature selection techniques with improved random forests and 98% for the second model (CBR-FS-IRF) with adaptation. The results showed that the proposed pruning method selects the best tree in less time and with better precision than hybridization with classical random forest that means an improvement of the performance of the CBR system. [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 sewer networks, failure prediction plays a significant role in operation and maintenance plans of wastewater utilities. This study aims to determine the effective variables on the failures by using feature selection algorithms (FS) and achieve maximum model accuracy with minimum variables. Also, four scenarios based on the suggested FS algorithms were developed. In these scenarios, the best prediction models were investigated using machine learning classifiers (ML) such as neural network classifier (NNC), gradient boosting machine (GBM), random forest (FR), and hybrid model (HM). The classification performance of ML models was evaluated using accuracy, precision, F1_score, and receiver operating characteristics (ROC) curve. The model accuracies ranging from 0.99 for accuracy to 1 for the ROC curve were achieved through ML algorithms. In conclusion, the ML algorithms suggested in this study may be a decision support tool for wastewater utilities in prioritizing the replacement, maintenance, and inspection of sewer pipes. [ABSTRACT FROM AUTHOR]

With the continuous growth of electric vehicle ownership, energy consumption has become an important factor restricting its development. Real-time and accurate energy consumption estimation is essential to alleviate range anxiety and help with energy-efficient route planning. This study aims to explore the factors affecting energy consumption, and estimate the energy consumption of electric vehicles under different road characteristics. Based on the real-world driving data of electric vehicles in Nanjing over a long period, this study analyzed the influencing factors of energy consumption, including trip conditions, weather, driving behavior, and road characteristics. Based on feature selection and correlation analysis, an energy consumption estimation framework for electric vehicles was proposed to estimate energy consumption under different road characteristics using extreme gradient boosting. The results show that energy consumption is significantly affected by trip conditions, weather, driving behavior, and road characteristics. The modeling of different road characteristics helps improve the accuracy of model estimation. The error of the model proposed in this paper is within the reasonable range (−0.25–0.25 kWh/km), which is better than the random forest and k-nearest neighbor models. [ABSTRACT FROM AUTHOR]

Credit scoring models are crucial tools for lenders to assess credit risks. Researchers from academia and the financial industry have shown intense interest in these models. However, real credit datasets often have high dimensionality and class imbalance, making it challenging to develop accurate and effective credit scoring models. To address these challenges, a new approach called the Multiple-Optimized Ensemble Learning (MOEL) method has been proposed. In MOEL, a technique called Multiple Diverse Optimized Subsets (MDOS) generates multiple diverse optimized subsets from various weighted random forests. From each subset, more effective and relevant features are selected. Then, a new evaluation measure is applied to each subset to determine the more optimized subsets. These subsets are applied to a novel Mahalanobis-based oversampling (MOS) technique to provide balanced subsets for the base classifier, which lessens the detrimental effects of imbalanced datasets. Finally, a stacking-based ensemble method is applied to the balanced subsets for integration of the base models. The proposed model was evaluated against six high-dimensional imbalanced credit scoring datasets, and it outperformed state-of-the-art methods, exhibiting a mean rank of 1.5 and 1.333 in terms of F1_score and G-mean, respectively. [ABSTRACT FROM AUTHOR]

Stock index trend prediction is a very important topic in the finance. The purpose of this paper is to compare six ensemble learning related techniques for stock index direction prediction, including four boosting methods (Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Tree (GBDT)), one bagging method (Random Forest (RF)) and one tree-structured machine learning method (Decision Tree (DT)). The Shanghai Composite Index is chosen for experimental evaluation. A factor library of seventy-two technical factors, thirty-five macro factors and seven micro factors are our inputs. Our predictions are one month ahead, and each prediction model is evaluated by the Area Under Curve (AUC). The results indicate that ensemble learning techniques perform well in stock index prediction, with all AUC values above 0.5. RF is considered as the top algorithm with an AUC value of 0.7355 before feature selection and 0.6736 after feature selection. Also, we predict the stock index trend using a comprehensive factor library and three single factor libraries, respectively. The results show that forecasting stock index directions with a complete factor library is of great importance, which could achieve more stable forecasting results. This study contributes to literature in that it is, to the best of our knowledge, the first to make an extensive evaluation of ensemble learning related methods by constructing a comprehensive factor library and three single factor libraries. [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: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx. Methods: Patients who underwent LTx between January 2017 and December 2019 were reviewed. The conventional logistic regression (LR) model was fitted by the independent risk factors which were determined by multivariate LR. The optimal ML model was determined based on 7 feature selection methods and 8 ML algorithms. Model performance was assessed by the area under the curve (AUC) and brier score, which were internally validated by the bootstrap method. Results: A total of 381 LTx patients were included, and 40 (10.5%) patients developed AS. Multivariate analysis indicated that male, pulmonary arterial hypertension, and postoperative 6-min walking test were significantly associated with AS (all P < 0.001). The conventional LR model showed performance with an AUC of 0.689 and brier score of 0.091. In total, 56 ML models were developed and the optimal ML model was the model fitted using a random forest algorithm with a determination coefficient feature selection method. The optimal model exhibited the highest AUC and brier score values of 0.760 (95% confidence interval [CI], 0.666–0.864) and 0.085 (95% CI, 0.058–0.117) among all ML models, which was superior to the conventional LR model. Conclusions: The optimal ML model, which was developed by clinical characteristics, allows for the satisfactory prediction of AS in patients after LTx. [ABSTRACT FROM AUTHOR]

In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in ensuring the accuracy of predictive models by identifying the most influential variables. This study, focusing on a retrospective cohort of 4778 COVID-19 patients from Iran, explores the performance of various FS methods, including filter, embedded, and hybrid approaches, in predicting mortality outcomes. The researchers leveraged 115 routine clinical, laboratory, and demographic features and employed 13 ML models to assess the effectiveness of these FS methods based on classification accuracy, predictive accuracy, and statistical tests. The results indicate that a Hybrid Boruta-VI model combined with the Random Forest algorithm demonstrated superior performance, achieving an accuracy of 0.89, an F1 score of 0.76, and an AUC value of 0.95 on test data. Key variables identified as important predictors of adverse outcomes include age, oxygen saturation levels, albumin levels, neutrophil counts, platelet levels, and markers of kidney function. These findings highlight the potential of advanced FS techniques and ML models in enhancing early disease detection and informing clinical decision-making. [ABSTRACT FROM AUTHOR]

As climate change intensifies, the frequency and severity of waterlogging are expected to increase, necessitating a deeper understanding of the cucumber response to this stress. In this study, three public RNA-seq datasets (PRJNA799460, PRJNA844418, and PRJNA678740) comprising 36 samples were analyzed. Various feature selection algorithms including Uncertainty, Relief, SVM (Support Vector Machine), Correlation, and logistic least absolute shrinkage, and selection operator (LASSO) were performed to identify the most significant genes related to the waterlogging stress response. These feature selection techniques, which have different characteristics, were used to reduce the complexity of the data and thereby identify the most significant genes related to the waterlogging stress response. Uncertainty, Relief, SVM, Correlation, and LASSO identified 4, 4, 10, 21, and 13 genes, respectively. Differential gene correlation analysis (DGCA) focusing on the 36 selected genes identified changes in correlation patterns between the selected genes under waterlogged versus control conditions, providing deeper insights into the regulatory networks and interactions among the selected genes. DGCA revealed significant changes in the correlation of 13 genes between control and waterlogging conditions. Finally, we validated 13 genes using the Random Forest (RF) classifier, which achieved 100% accuracy and a 1.0 Area Under the Curve (AUC) score. The SHapley Additive exPlanations (SHAP) values clearly showed the significant impact of LOC101209599, LOC101217277, and LOC101216320 on the model's predictive power. In addition, we employed the Boruta as a wrapper feature selection method to further validate our gene selection strategy. Eight of the 13 genes were common across the four feature weighting algorithms, LASSO, DGCA, and Boruta, underscoring the robustness and reliability of our gene selection strategy. Notably, the genes LOC101209599, LOC101217277, and LOC101216320 were among genes identified by multiple feature selection methods from different categories (filtering, wrapper, and embedded). Pathways associated with these specific genes play a pivotal role in regulating stress tolerance, root development, nutrient absorption, sugar metabolism, gene expression, protein degradation, and calcium signaling. These intricate regulatory mechanisms are crucial for cucumbers to adapt effectively to waterlogging conditions. These findings provide valuable insights for uncovering targets in breeding new cucumber varieties with enhanced stress tolerance. [ABSTRACT FROM AUTHOR]

Introduction: Clinical reasoning in veterinary medicine is often based on clinicians' personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures. Materials and methods: Dogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features. Results: A total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year. Conclusion: Machine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process. [ABSTRACT FROM AUTHOR]

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account. Use of machine learning to summarize complex in vivo data and provide predictive utility for Influenza A virus ferret lethality, morbidity, and transmission models. [ABSTRACT FROM AUTHOR]

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal–external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts. [ABSTRACT FROM AUTHOR]

This article aims to evaluate the performance of solar radiation forecasts produced by CMA-WSP v2.0 (version 2 of the China Meteorological Administration Wind and Solar Energy Prediction System) and to explore the application of machine learning algorithms from the scikit-learn Python library to improve the solar radiation prediction made by the CMA-WSP v2.0. It is found that the performance of the solar radiation forecasting from the CMA-WSP v2.0 is closely related to the weather conditions, with notable diurnal fluctuations. The mean absolute percentage error (MAPE) produced by the CMA-WSP v2.0 is approximately 74% between 11:00 and 13:00. However, the MAPE ranges from 193% to 242% at 07:00–08:00 and 17:00–18:00, which is greater than that observed at other daytime periods. The MAPE is relatively low (high) for both sunny and cloudy (overcast and rainy) conditions, with a high probability of an absolute percentage error below 25% (above 100%). The forecasts tend to underestimate (overestimate) the observed solar radiation in sunny and cloudy (overcast and rainy) conditions. By applying machine learning models (such as linear regression, decision trees, K-nearest neighbors, random forests regression, adaptive boosting, and gradient boosting regression) to revise the solar radiation forecasts, the MAPE produced by the CMA-WSP v2.0 is significantly reduced. The reduction in the MAPE is closely connected to the weather conditions. The models of K-nearest neighbors, random forests regression, and decision trees can reduce the MAPE in all weather conditions. The K-nearest neighbor model exhibits the most optimal performance among these models, particularly in rainy conditions. The random forest regression model demonstrates the second-best performance compared to that of the K-nearest neighbor model. The gradient boosting regression model has been observed to reduce the MAPE of the CMA-WSP v2.0 in all weather conditions except rainy. In contrast, the adaptive boosting (linear regression) model exhibited a diminished capacity to improve the CMA-WSP v2.0 solar radiation prediction, with a slight reduction in MAPE observed only in sunny (sunny and cloudy) conditions. In addition, the input feature selection has a considerable influence on the performance of the machine learning model. The incorporation of the time series data associated with the diurnal variation of solar radiation as an input feature can further improve the model's performance. [ABSTRACT FROM AUTHOR]

This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available. [ABSTRACT FROM AUTHOR]

As a severe inflammatory response syndrome, sepsis presents complex challenges in predicting patient outcomes due to its unclear pathogenesis and the unstable discharge status of affected individuals. In this study, we develop a machine learning-based method for predicting the discharge status of sepsis patients, aiming to improve treatment decisions. To enhance the robustness of our analysis against outliers, we incorporate robust statistical methods, specifically the minimum covariance determinant technique. We utilize the random forest imputation method to effectively manage and impute missing data. For feature selection, we employ Lasso penalized logistic regression, which efficiently identifies significant predictors and reduces model complexity, setting the stage for the application of more complex predictive methods. Our predictive analysis incorporates multiple machine learning methods, including random forest, support vector machine, and XGBoost. We compare the prediction performance of these methods with Lasso penalized logistic regression to identify the most effective approach. Each method's performance is rigorously evaluated through ten iterations of 10-fold cross-validation to ensure robust and reliable results. Our comparative analysis reveals that XGBoost surpasses the other models, demonstrating its exceptional capability to navigate the complexities of sepsis data effectively. [ABSTRACT FROM AUTHOR]

Burnout in healthcare professionals (HCPs) is a global concern. Few studies use theoretical and conceptual models to assess work system stressors contributing to HCP burnout. A survey consisting of demographic characteristics and work system stressors based on the National Academy of Medicine's theoretical model was administered to 589 HCPs (participation rate: 64%). We compare four classifiers with four feature selection methods to predict HCP burnout. We use SHapley Additive exPlanations (SHAP) to prioritize key work system stressors contributing to HCP burnout and explain the predictions from the best model, random forest using recursive feature elimination (AUC: 0.75). Time pressure, work-life integration, inadequate technology, administrative burden, values and expectations, professional relationships, lack of recognition for QI activities, moral distress, and patient factors are the work system stressors that have the highest impact on predicting HCP burnout. Identifying and prioritizing key factors to mitigate HCP burnout is important for the healthcare system to allocate resources and improve patient safety and quality of care. [ABSTRACT FROM AUTHOR]

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This study aims to improve the performance of classification algorithms in dealing with unbalanced and high-dimensional health in stroke prediction by integrating correlation feature selection and hybrid sampling techniques. Several previous studies that used machine learning methods to predict stroke still had less than optimal accuracy. This is because stroke data has several problems, including missing values, many attributes, and data imbalance can cause a decrease in the performance of the classification method. Therefore, this research uses an integrated approach to feature selection and hybrid sampling. The objective of the feature selection technique is to identify important attributes within stroke data. After that, the SMOTE-Enn hybrid sampling approach is utilized to address data imbalance. The research findings indicate that employing correlation-based feature selection along with SMOTE-Enn and the Random Forest algorithm leads to improved performance compared to no sampling with the SVM and XGBoost methods, with an increase in accuracy of 3%, recall of 91.3%, and AUC of 45.2%. Thus, the proposed method performed better than recent stroke classification studies. [ABSTRACT FROM AUTHOR]

In the field of bolt loosening detection, although some progress has been made, there are still challenges such as high operational complexity, single feature extraction methods, and insufficient analysis model performance, especially in large steel truss structures, where there is a lack of efficient and accurate bolt loosening identification solutions. In response to these shortcomings, this article proposes an innovative bolt loosening damage recognition method based on sound signals. This method integrates feature extraction techniques of Mel frequency cepstral coefficients (MFCCs) and wavelet packet energy spectra (WPES), and comprehensively characterizes sound signals by constructing MFCC-WPES combined features. Subsequently, the random forest (RF) algorithm optimized by genetic algorithm was used for feature selection and model training, aiming to improve recognition accuracy and robustness. The experimental results show that this method can not only accurately identify bolt loosening signals in steel truss structure bolt loosening detection, but also has strong identification ability for environmental noise. Compared with traditional methods, the proposed solution in this article shows significant improvements in both performance and practicality, providing a new perspective and solution for the technological advancement of bolt loosening detection in steel truss structures. [ABSTRACT FROM AUTHOR]

The groundwater salinization process complexity and the lack of data on its controlling factors are the main challenges for accurate predictions and mapping of aquifer salinity. For this purpose, effective machine learning (ML) methodologies are employed for effective modeling and mapping of groundwater salinity (GWS) in the Mio-Pliocene aquifer in the Sidi Okba region, Algeria, based on limited dataset of electrical conductivity (EC) measurements and readily available digital elevation model (DEM) derivatives. The dataset was randomly split into training (70%) and testing (30%) sets, and three wrapper selection methods, recursive feature elimination (RFE), forward feature selection (FFS), and backward feature selection (BFS) are applied to train the data. The resulting combinations are used as inputs for five ML models, namely random forest (RF), hybrid neuro-fuzzy inference system (HyFIS), K-nearest neighbors (KNN), cubist regression model (CRM), and support vector machine (SVM). The best-performing model is identified and applied to predict and map GWS across the entire study area. It is highlighted that the applied methods yield input variation combinations as critical factors that are often overlocked by many researchers, which substantially impacts the models' accuracy. Among different alternatives the RF model emerged as the most effective for predicting and mapping GWS in the study area, which led to the high performance in both the training (RMSE = 1.016, R = 0.854, and MAE = 0.759) and testing (RMSE = 1.069, R = 0.831, and MAE = 0.921) phases. The generated digital map highlighted the alarming situation regarding excessive GWS levels in the study area, particularly in zones of low elevations and far from the Foum Elgherza dam and Elbiraz wadi. Overall, this study represents a significant advancement over previous approaches, offering enhanced predictive performance for GWS with the minimum number of input variables. [ABSTRACT FROM AUTHOR]

Due to the diversity and randomness of residential consumption behavior, as well as the different share of domestic electricity demand by appliances, class imbalance problems exist in the non‐intrusive load monitoring (NILM) system. The recognition model becomes biased toward the majority class, which makes the recognition of the minority class difficult. To tackle this challenge, we propose a novel NILM framework for appliance recognition to overcome the insufficient learning problems of the minority class, which combines a multi‐domain feature extraction module, a two‐stage feature selection, and a data oversampling method. Multiple features are extracted to make full of the complementarity of combined features to enhance appliance recognition. Furthermore, a two‐stage feature selection based on minimal‐redundancy‐maximal‐relevance (mRMR) and random forest (RF) is developed to select an optimal feature set. Subsequently, an oversampling method combining K‐medoids and the synthetic minority oversampling technique (KM‐SMOTE) with sampling weights is proposed for data augmentation of the minority class to handle imbalance learning problems. The effectiveness of the proposed method is verified by the experiments on the public dataset PLAID. The results show that, compared with the state‐of‐the‐art techniques, the overall recognition accuracy and F1‐score with the proposed method are enhanced by 4.61% and 2.04%, respectively. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures. [ABSTRACT FROM AUTHOR]

The crude protein (CP) content is an important determining factor for the quality of alfalfa, and its accurate and rapid evaluation is a challenge for the industry. A model was developed by combining Fourier transform infrared spectroscopy (FTIS) and chemometric analysis. Fourier spectra were collected in the range of 4000~400 cm−1. Adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky–Golay (SG) were used for preprocessing the spectral data; competitive adaptive reweighted sampling (CARS) and the characteristic peaks of CP functional groups and moieties were used for feature selection; partial least squares regression (PLSR) and random forest regression (RFR) were used for quantitative prediction modelling. By comparing the combined prediction results of CP content, the predictive performance of airPLST-cars-PLSR-CV was the best, with an R P 2 of 0.99 and an RMSEP of 0.053, which is suitable for establishing a small-sample prediction model. The research results show that the combination of the PLSR model can achieve an accurate prediction of the crude protein content of alfalfa forage, which can provide a reliable and effective new detection method for the crude protein content of alfalfa forage. [ABSTRACT FROM AUTHOR]

Classrooms play a pivotal role in students' learning, and maintaining optimal indoor air quality is crucial for their well-being and academic performance. Elevated CO2 levels can impair cognitive abilities, underscoring the importance of accurate predictions of CO2 concentrations. To address the issue of inadequate analysis of factors affecting classroom CO2 levels in existing models, leading to suboptimal feature selection and limited prediction accuracy, we introduce the RF-TPE-LSTM model in this study. Our model integrates factors that affect classroom CO2 levels to enhance predictions, including occupancy, temperature, humidity, and other relevant factors. It combines three key components: random forest (RF), tree-structured Parzen estimator (TPE), and long short-term memory (LSTM). By leveraging these techniques, our model enhances the predictive capabilities and refines itself through Bayesian optimization using TPE. Experiments conducted on a self-collected dataset of classroom CO2 concentrations and influencing factors demonstrated significant improvements in the MAE, RMSE, MAPE, and R2. Specifically, the MAE, RMSE, and MAPE were reduced to 2.96, 5.54, and 0.60%, respectively, with the R2 exceeding 98%, highlighting the model's effectiveness in assessing indoor air quality. [ABSTRACT FROM AUTHOR]

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO4), nitrate (NO3), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO3), phosphate (PO4), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously. [ABSTRACT FROM AUTHOR]

This work identifies the most effective machine learning techniques and supervised learning models to estimate power output from photovoltaic (PV) plants precisely. The performance of various regression models is analyzed by harnessing experimental data, including Random Forest regressor, Support Vector regression (SVR), Multi-layer Perceptron regressor (MLP), Linear regressor (LR), Gradient Boosting, k-Nearest Neighbors regressor (KNN), Ridge regressor (Rr), Lasso regressor (Lsr), Polynomial regressor (Plr) and XGBoost regressor (XGB). The methodology applied starts with meticulous data preprocessing steps to ensure dataset integrity. Following the preprocessing phase, which entails eliminating missing values and outliers using Isolation Feature selection based on a correlation threshold is performed to identify relevant parameters for accurate prediction in PV systems. Subsequently, Isolation Forest is employed for outlier detection, followed by model training and evaluation using key performance metrics such as Root-Mean-Squared Error (RMSE), Normalized Root-Mean-Squared Error (NRMSE), Mean Absolute Error (MAE), and R-squared (R2), Integral Absolute Error (IAE), and Standard Deviation of the Difference (SDD). Among the models evaluated, Random Forest emerges as the top performer, highlighting promising results with an RMSE of 19.413, NRMSE of 0.048%, and an R2 score of 0.968. Furthermore, the Random Forest regressor (the best-performing model) is integrated into a MATLAB application for real-time predictions, enhancing its usability and accessibility for a wide range of applications in renewable energy. [ABSTRACT FROM AUTHOR]

A major challenge of precision oncology is the identification and prioritization of suitable treatment options based on molecular biomarkers of the considered tumor. In pursuit of this goal, large cancer cell line panels have successfully been studied to elucidate the relationship between cellular features and treatment response. Due to the high dimensionality of these datasets, machine learning (ML) is commonly used for their analysis. However, choosing a suitable algorithm and set of input features can be challenging. We performed a comprehensive benchmarking of ML methods and dimension reduction (DR) techniques for predicting drug response metrics. Using the Genomics of Drug Sensitivity in Cancer cell line panel, we trained random forests, neural networks, boosting trees and elastic nets for 179 anti-cancer compounds with feature sets derived from nine DR approaches. We compare the results regarding statistical performance, runtime and interpretability. Additionally, we provide strategies for assessing model performance compared with a simple baseline model and measuring the trade-off between models of different complexity. Lastly, we show that complex ML models benefit from using an optimized DR strategy, and that standard models—even when using considerably fewer features—can still be superior in performance. [ABSTRACT FROM AUTHOR]

In this study, an artificial neural network (ANN) model was developed based on molecular descriptors to predict the surface tension of liquids. A dataset containing various features was constructed by collecting experimental data from 25 different fluids and extracting molecular structural descriptors. Feature selection was performed using the forward search wrapper method based on Random Forest, identifying 7 significant features (Temperature, MinAbsEStateIndex, LabuteASA, MolMR, Chi1v, qed and FpDensityMorgan3) for surface tension prediction. Subsequently, an ANN model was constructed with the selected features as inputs to predict the surface tension of liquids. The derived model demonstrates high accuracy with a correlation coefficient (R) exceeding 0.999 and a notably low mean square error (MSE = 1.843e−5). Moreover, the ANN model exhibited a total average absolute deviation (AAD) of 0.98 %, comparable to that of the REFPROP, which had a total AAD of 1.26 %. This quantitative model serves an easy tool for gaining insights into the molecular underpinnings of surface tension and predicting its value across various fluids. [ABSTRACT FROM AUTHOR]

The success of software projects for organizations heavily relies on accurate workforce and cost estimates. Initially, effort estimation was based on non-algorithmic methods, but with technological advancements, algorithmic approaches such as regression emerged. In recent years, there has been a growing interest in using Machine Learning and Artificial Intelligence for software cost estimation. In this study, Linear Regression, Multilayer Perceptron, Bagging, SMOreg, IBk, KStar, RandomTree, and RandomForest algorithms were trained with four open-source datasets. Firstly, models were trained with original feature sets, then six different hybrid feature selection methods were proposed to eliminate low-impact features and prevent overfitting. These hybrid feature selection methods, developed using evaluation methods like Relief, Classifier, and Correlation, along with search methods like RandomSearch, PSO, GA, and Ranker. And trained models tested by the 10-fold cross-validation technique. The results showed the ability to quickly obtain adaptable models and the effectiveness of feature selection. KStar, SMOreg, Multilayer Perceptron, and Linear Regression algorithms, along with PSO and GA search methods, yielded satisfactory results even with different feature subsets. [ABSTRACT FROM AUTHOR]

The Internet of Things, or IoT, is an important technology applied in various applications such as smart homes and innovative healthcare. Due to its architecture, IoT-based devices suffer from various security challenges, most commonly, botnet attacks. This article aims to develop a hybrid feature selection method to find the most influential features based on three feature selection methods, correlation, generalized normal distribution optimization, and lasso, to detect botnet attacks in IoT devices. The UNSW-NB15 dataset is used to assess the proposed system. Several classification models including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and bagging are utilized for the classification purpose. The proposed system was evaluated using several performance metrics. The results showed the correlation feature selection method had the most accurate botnet attack detection rate. RF also outperformed other models with a 95.11% detection rate in binary classification and 83.96% in multi-classification. On the other hand, results showed that the proposed hybrid method outperformed the feature selection methods with an increase of about 3% in both classifications. The AdaBoost model achieved an accuracy of 99.28% with binary classification by using 18 features, and the RF model achieved an accuracy of 86.62% with multi-classification by using 22 features. The robustness and efficacy of the proposed approach were demonstrated by comparing the study’s results with several other studies that have used the same dataset. The results of the study can be implemented in real applications to detect network interference of a dynamic nature in real-time and assist intrusion detection systems (IDS) in addressing these attacks. [ABSTRACT FROM AUTHOR]

A significant percentage of chronic kidney disease (CKD) patients has recently increased, prediction procedures have become effective, using machine learning models and optimizers to identify most relevant features and improve classification accuracy. The goal is to avoid overfitting, minimize computational time in the classification model, and provide a faster and cost-effective model. This study aims to improve the accuracy of the CKD prediction model by combining the Elitist-Ant System algorithm with a Multi-Layer Perceptron Neural Network (EAS-MLPNN). EAS selectssignificant features while maintaining an appropriate analytical result. The used dataset verifies the model’s performance. Where observed results from the experiments showed that, the EAS-MLPNN model made better predictions than traditional classifiers including support vector machine, MLPNN, and random forest with 94.67% accuracy indicating a 2% enhancement of the classification accuracy. [ABSTRACT FROM AUTHOR]

A way to quickly and accurately extract building information from visible light images acquired by drones is a hot issue in remote sensing image processing research. In response to the above problems, this paper combined feature extraction, random forest regression, and improved morphological alternating filtering algorithms on the basis of in-depth research on the morphological building index and enhanced morphological building index. A targeted improvement was made to the building extraction method. The main improvements in this paper are as follows. (1) A method for building enhancement of UAV visible light images based on random forest regression is proposed. By fusing the original visible light band, visible light remote sensing index features, texture features, etc., the feature space is constructed, and no artificial feature selection is required while enhancing the building information in the case of the situation. (2) Aiming at the problem of a lot of noise in the homogeneous area inside the enhanced image of the building, the image is morphologically filtered through EASFs. Finally, in order to verify the effectiveness of the method in the paper, the representative city center building area and the city surrounding building area are selected for extraction experiments. The main building types of the two areas are quite different. In the above two areas, the F1 value of the proposed method is increased by 10.40% and 10.08% compared with the MBI algorithm, and the F1 of the first proposed an improved enhanced morphological building index value is increased by 5.23% and 7.87% compared with the EMBI algorithm. The results show that this method can better extract the building information from the UAV's visible light image. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has shown a significant impact in tackling various complicated problems in different application domains, including healthcare, economics, ecological, stock market, surveillance, and commercial applications. Machine Learning techniques are good enough to deal with a wide range of data, uncover fascinating links, offer insights, and spot trends. ML can improve disease diagnosis accuracy, predictability, performance, and reliability. This paper reviews various machine learning techniques applied to different medical datasets and proposes an ensemble method for helping in the early diagnosis of different diseases. The study compares existing machine learning techniques with the proposed ensemble method. The ensemble method uses the AdaBoost algorithm to combine the traits of choice trees, random forests, and support vector machines. Three feature selection techniques, Fisher’s score, information gain, and genetic algorithm, are used to select appropriate dataset features. The ensemble method also uses the K-fold cross-validation technique (where k=15) for validating results. SMOTE was employed to balance some of the datasets because they were quite unbalanced. All the methods used in this study are evaluated based on accuracy, AU Curve, Recall, Precision, and F1-score. The paper uses different medical datasets at the University of California Irvine and the Kaggle directory to compare machine-learning models with the proposed ensemble method. The encouraging results show that the ensemble method outperforms the existing machine-learning techniques. The paper thoroughly analyzes how machine learning is used in the medical industry, covering established technologies and their impact on medical diagnosis. An early diagnosis is needed to prevent people from deadly diseases. Hence, this study proposes an ensemble method that may be used to diagnose different diseases early. [ABSTRACT FROM AUTHOR]

The proliferation of malware gravely threatens the security of computer systems and sensitive data. This work aims to improve malware detection by using advanced feature selection techniques. The study utilizes the Firefly Algorithm (FA) for feature selection in binary and multiclass classifications to enhance the discrimination capabilities of selected features. The selected features from the binary and multiclass classifications are combined to generate a comprehensive feature set. The Obfuscated-MalMem2022 dataset is employed in the experimental evaluation. The Random Forest (RF) method completes the classification problem. Remarkably, the results demonstrate that RF performs better with the combined feature set than with features chosen separately from the binary and multiclass classifications by the FA method. RF attains a remarkable 99.983% accuracy in binary classification, demonstrating the potency of the selected features in differentiating between malicious and benign data. Moreover, RF demonstrates an impressive accuracy of 87.304% in multiclass classification, highlighting the strength of the proposed methodology. [ABSTRACT FROM AUTHOR]

Understanding household energy-consumption patterns is essential for developing effective energy-conservation strategies. This study aims to identify 'out-profiled' consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with multinomial logistic regression (RFE-MLR) to identify optimal feature subsets, random forest (RF) to determine feature importance, and a combined fuzzy rough feature selection with fuzzy rough nearest neighbors (FRFS-FRNN) for handling uncertainty and imprecision in data. These methods were applied to a dataset based on a survey of 383 households in Brazil, capturing various factors such as household size, income levels, geographical location, and appliance usage. Our analysis revealed that key features such as the number of people in the household, heating and air conditioning usage, and income levels significantly influence energy consumption. The novelty of our work lies in the comprehensive application of these advanced feature-selection techniques to identify atypical consumption patterns in a specific regional context. The results showed that households without heating and air conditioning equipment in medium- or high-consumption profiles, and those with lower- or medium-income levels in medium- or high-consumption profiles, were considered out-profiled. These findings provide actionable insights for energy providers and policymakers, enabling the design of targeted energy-conservation strategies. This study demonstrates the importance of tailored approaches in promoting sustainable energy consumption and highlights notable deviations in energy-use patterns, offering a foundation for future research and policy development. [ABSTRACT FROM AUTHOR]

The proliferation of wireless networks as a primary data transmission channel has brought about a surge in data volume but also raised security threats and privacy concerns. Intrusion Detection Systems (IDS) have proven effective in safeguarding data transmission, yet they struggle to detect minor or rare attacks that mimic normal traffic. To address this challenge, we propose a novel two-layer classification model integrating K-nearest neighbor (KNN) and support vector machines (SVMs). In the first layer, KNN classifies data into Normal, Major, and Minor Attack categories, while the second layer further distinguishes Major Attacks into DoS or Probe and Minor Attacks into U2R or R2. Our framework incorporates Common Correlated Feature Selection (CCFS) to optimize feature discrimination, partitioning training data into three groups. Furthermore, we explore data preprocessing techniques to enhance data interpretation and maintain statistical normalcy in traffic connection features. Our experimental analysis utilizes the NSL-KDD dataset, demonstrating that our approach significantly improves detection rates, particularly for Minor Attacks, achieving a remarkable 92.33% detection rate for U2R and 91.33% for R2L attacks. This represents a substantial 10% enhancement over existing methods, outperforming Multi-Layer Perceptron (MLP) by 13.23%, Random Forest (RF) by 10.11%, J48- Decision Tree (DT) by 9.23%, and Naïve Bayes (NB) by 9.42% and 8.17% in terms of detection rates. These results underscore the effectiveness of our approach in enhancing intrusion detection performance. [ABSTRACT FROM AUTHOR]

Diabetes is a widespread chronic condition that impacts people all over the globe and requires a clear and timely diagnosis. Untreated diabetes leads to retinopathy, nephropathy, and damage to the nervous system. In this context, Machine Learning (ML) might be used to detect health problems early, diagnose them, and track their progress. Ensemble techniques are a promising approach that combines many classifiers to improve forecast accuracy and resilience. This study investigates the categorization of diabetes using an ensemble machine learning technique known as a voting classifier. Using a variety of classifiers, including Light Gradient Boosting Machine (LightGBM), Gradient Boost classifier (GBC), and Random Forest (RF). The predictions are aggregated using voting methods to get a final classification result. The research is carried out using two benchmarking datasets: the Pima Indian Diabetes Dataset (PIDD) and the German Dataset. The Boruta technique is used to choose the best attributes from the datasets, while the Random Over Sampling approach balances the range of classes and eliminates abnormal data using the interquartile range approach. The findings showed that the combination of the Boruta feature selection algorithm and ensemble Voting Classifier performed better for both PIDD and German datasets with an accuracy of 93% and 90% respectively. These algorithms are evaluated and the maximum accuracy is produced using the combination of the Boruta feature selection algorithm and ensemble Voting Classifier. This research helps medical professionals in the early prediction of diabetes, reducing physician's time. [ABSTRACT FROM AUTHOR]

Recently, the Internet of Things (IoT) has become a buzzword in various technology fields because of its many applications. Healthcare is one of the most important fields in daily life and holds significant interest for IoT and artificial intelligence researchers. In the area of healthcare, the mental healthcare field has attracted many researchers and funding organizations for various reasons. Among those reasons are the adverse impact of mental conditions on both individuals and society, the high costs of mental care, the nature of mental conditions and their hidden and unclear symptoms, and the stigma associated with them. In this work, an approach to building an IoT mental health monitoring and detection system has been proposed. The depression problem is used as a case study for the mental conditions. The proposed approach involves data collection, data pre-processing, feature extraction, feature selection, and model building. Machine learning (ML) and data mining techniques, along with different data types for training and testing the models, have been utilized to accomplish the tasks of the proposed approach. Four ML algorithms have been investigated to detect and predict the diagnosis of the mental state. These algorithms are random forest, decision tree, support vector machine (SVM) and k-nearest neighbors (KNN). Our ML detection model has achieved a high detection accuracy of 95% using the random forest algorithm. Comparisons in different aspects with other works are presented. Our work has the advantages of using ML, adopting different data types, and therefore achieving better classification. [ABSTRACT FROM AUTHOR]

Machine learning (ML) techniques empower computers to learn from data and make predictions or decisions in various domains, while preprocessing methods assist in cleaning and transforming data before it can be effectively utilized by ML. Feature selection in ML is a critical process that significantly influences the performance and effectiveness of models. By carefully choosing the most relevant and informative attributes from the dataset, feature selection enhances model accuracy, reduces overfitting, and minimizes computational complexity. In this study, we leverage the UAH-DriveSet dataset to classify driver behavior, employing Filter, embedded, and wrapper methods encompassing 10 distinct feature selection techniques. Through the utilization of diverse ML algorithms, we effectively categorize driver behavior into normal, drowsy, and aggressive classes. The second objective is to employ feature selection techniques to pinpoint the most influential features impacting driver behavior. As a results, random forest emerges as the top-performing classifier, achieving an impressive accuracy of 96.4% and an F1-score of 96.36% using backward feature selection in 7.43 s, while K-nearest neighbour (K-NN) attains an accuracy of 96.29% with forward feature selection in 0.05 s. Following our comprehensive results, we deduce that the primary influential features for studying driver behavior include speed (km/h), course, yaw, impact time, road width, distance to the ahead vehicle, vehicle position, and number of detected vehicles. [ABSTRACT FROM AUTHOR]

Background: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs. Methods: We applied machine learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) and TensorFlow Keras to a dataset of miRNA sequences and gene targets, assessing the predictive power of several classifiers: naïve Bayes, multilayer perceptron, Hoeffding tree, random forest, and random tree. The data were further subjected to InfoGain feature selection to identify the most informative miRNA sequence and gene target descriptors. The ML models' abilities to distinguish between miRNA implicated in EC and control group miRNA was then tested. Results: Of the tested WEKA classifiers, the top 3 performing ones were random forest, Hoeffding tree, and naïve Bayes. The TensorFlow Keras neural network model was subsequently trained and tested, the model's predictive power was further validated using an independent dataset. The TensorFlow Keras gave an accuracy 0.91. The WEKA best algorithm (naïve Bayes) model yielded an accuracy of 0.94. Conclusions: The results demonstrate the potential of ML-based miRNA classifiers in diagnosing EC. However, further studies are necessary to validate these findings and explore the full clinical potential of this approach. [ABSTRACT FROM AUTHOR]

In the context of the 5G network, the proliferation of access devices results in heightened network traffic and shifts in traffic patterns, and network intrusion detection faces greater challenges. A feature selection algorithm is proposed for network intrusion detection systems that uses an improved binary pigeon-inspired optimizer (SABPIO) algorithm to tackle the challenges posed by the high dimensionality and complexity of network traffic, resulting in complex models, reduced accuracy, and longer detection times. First, the raw dataset is pre-processed by uniquely one-hot encoded and standardized. Next, feature selection is performed using SABPIO, which employs simulated annealing and the population decay factor to identify the most relevant subset of features for subsequent review and evaluation. Finally, the selected subset of features is fed into decision trees and random forest classifiers to evaluate the effectiveness of SABPIO. The proposed algorithm has been validated through experimentation on three publicly available datasets: UNSW-NB15, NLS-KDD, and CIC-IDS-2017. The experimental findings demonstrate that SABPIO identifies the most indicative subset of features through rational computation. This method significantly abbreviates the system's training duration, enhances detection rates, and compared to the use of all features, minimally reduces the training and testing times by factors of 3.2 and 0.3, respectively. Furthermore, it enhances the F1-score of the feature subset selected by CPIO and Boost algorithms when compared to CPIO and XGBoost, resulting in improvements ranging from 1.21% to 2.19%, and 1.79% to 4.52%. [ABSTRACT FROM AUTHOR]

Under varying operational conditions, the contact and relative movement of a polymer and metal result in surface wear, accompanied by the emission of noise. The relationship between friction noise and wear is inherently complex and nonlinear. In light of these tribological characteristics, this paper introduces the implementation of a random forest algorithm and generalized regression neural network algorithm to establish a mathematical model for predicting the wear rate based on friction noise. To enhance the accuracy of wear rate regression, this study incorporates L2 norm feature selection and the sparrow search algorithm, which are tailored toward the friction characteristics. These techniques optimize the machine learning-based friction model, ultimately improving the regression accuracy of the wear rate. [ABSTRACT FROM AUTHOR]

Heart diseases are a major cause of death worldwide, highlighting the need for early detection. The electrocardiogram (ECG) records the heart’s electrical activity using electrodes. Our research focuses on the ECG data to diagnose heart disorders, particularly arrhythmias. We utilized the MIT-BIH arrhythmia dataset for comparative analysis of various machine learning techniques, including random forest, K-Nearest Neighbor, and Decision Tree, along with deep learning algorithms like Long short-term memory and Convolutional Neural Networks. This required employing various preprocessing methods like filtering and normalization and feature selection techniques such as chi-square and sequential feature selectors to improve the performance of heart disease prediction. Therefore, hybrid machine and deep learning models are proposed, and the results reveal that hybrid models perform better than conventional models. [ABSTRACT FROM AUTHOR]

Accurately estimating the dolomite content in carbonate rocks is crucial for optimizing oil and gas exploration and production strategies. Hyperspectral techniques for estimating dolomite content have advantages in terms of efficiency, cost-effectiveness, and non-destructiveness compared with traditional laboratory methods. Despite the abundance of hyperspectral data, feature selection and extraction remain challenging. In this study, hyperspectral data collected from surface outcrop in the field using the analytical spectral device (ASD) were applied to construct model for estimating dolomite content. Firstly, the data were preprocessed via outlier analysis and continuum transformation. Next, a hybrid approach integrating spectral knowledge with machine learning was proposed and applied to facilitate efficient and precise feature selection of the hyperspectral data; in this approach, preliminary screening based on spectral knowledge is followed by further hyperspectral data feature selection using a random forest algorithm. The selected features were then combined using a support vector regression algorithm to obtain the estimation model. Finally, the accuracy of the model was evaluated using the hyperspectral data from field outcrop samples. To further verify the effectiveness of this method, various combinations of eight input variables and four machine learning algorithms were compared. Among all combinations, our model achieved the highest accuracy with a test R² value of 0.91 and a root-mean-square error of only 0.122. The proposed method is practical and efficient and provides precise quantitative data for field geologists to identify the mineral distribution in outcrops. Thus, our method provides robust support for understanding reservoir characteristics and has significant practical value in geological surveys and mineral exploration. [ABSTRACT FROM AUTHOR]

Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B: 0.09 [0.05–0.12]), gestational age, pre-delivery hemoglobin (B:0.83 [0.80–0.85]) and fibrinogen levels (B:0.01 [0.01–0.01]), and pre-labor platelet count (B*1000: 0.77 [0.30–1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error: 0.62), which was subsequently deployed as a web-based calculator: https://predictivecalculators.shinyapps.io/ANN-HB. The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models. [ABSTRACT FROM AUTHOR]