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In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

Distributed generations (DGs) have been increasingly addressing the ongoing power deficit in the electricity market. However, a significant concern in DG-integrated microgrids is the detection of accidental islanding. To tackle this issue, this article proposes a cost-friendly, novel data-driven passive islanding detection scheme named EEMD-HOBRC, combining noise-assisted ensemble empirical mode decomposition (EEMD) and a hybrid optimization-based random forest classifier (HOBRFC). The detection scheme employs a diverse set of features extracted from both raw and EEMD decomposed signals. Essential features are selected using the binary grey wolf optimizer (BGWO) to reduce computational burden. To further improve classification accuracy, the parameters of the random forest classifier are optimized through a hybrid particle swarm and reformed grey wolf optimization (PSRGWO) technique with Cohen's kappa index as the cost function. The proposed technique is rigorously validated in two different multi-DG environments, encompassing islanding and various nonislanding events. The results demonstrate the effectiveness of the approach in terms of enhanced accuracy, detection time, and performance under both noisy and noise-free conditions. The accuracy of detection under ideal and high noise scenarios is found to be 99.88% and 99.2%, respectively, with maximum detection time of 34.27 ms. Comparative analysis with other algorithms also supports the superiority of the proposed technique. Finally, the method is successfully applied to shrink the nondetection zone (NDZ) with minimal power mismatch, further enhancing its utility in practical applications. [ABSTRACT FROM AUTHOR]

Aeroengines use numerous sensors to detect equipment health and ensure proper operation. Currently, filtering useful sensor data and removing useless data is challenging in predicting the remaining useful life (RUL) of an aeroengine using deep learning. To reduce computational costs and improve prediction performance, we use random forest to evaluate the feature importance of sensor data. Based on the size of the feature corresponding to the Gini index, we select the appropriate sensor. This helps us to determine which sensor to use and ensures that the computational resources are not wasted on unnecessary sensors. Considering that the RUL of equipment changes in a progressively more complex manner as the equipment is used over time, we propose an improved squeeze and excitation block (SSE) and combine it with a convolutional neural network (CNN). By enhancing the feature selection ability of CNN through segmented squeeze and excitation block, the model can focus on important information within features to effectively improve prediction performance. We compared our experiments with other RUL experiments on the CMAPSS aeroengine dataset and then conducted ablation experiments to verify the critical role of the methods we used. [ABSTRACT FROM AUTHOR]

The primary aim of this study is to explore the utility of machine learning algorithms for predicting personal PM2.5 exposures of elderly participants and to evaluate the effect of individual variables on model performance. Personal PM2.5 was measured on five consecutive days across seasons in 66 retired adults in Beijing (BJ) and Nanjing (NJ), China. The potential predictors were extracted from routine monitoring data (ambient PM2.5 concentrations and meteorological factors), basic questionnaires (personal and household characteristics), and time-activity diary (TAD). Prediction models were developed based on either traditional multiple linear regression (MLR) or five advanced machine learning methods. Our results revealed that personal PM2.5 exposures were well predicted by both MLR and machine learning models with predictors extracted from routine monitoring data, which was indicated by the high nested cross-validation (CV) R 2 ranging from 0.76 to 0.88. The addition of predictors from either the questionnaire or TAD did not improve predictive accuracy for all algorithms. The ambient PM2.5 concentrations were the most important predictor. Overall, the random forest, support vector machine, and extreme gradient boosting algorithms outperformed the reference MLR method. Compared with the traditional MLR approach, the CV R 2 of the RF model increased up to 7% (from 0.82 ± 0.13 to 0.88 ± 0.10), while the RMSE reduced up to 18% (from 19.8 ± 5.4 to 16.3 ± 4.5) in BJ. [ABSTRACT FROM AUTHOR]

Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms. [ABSTRACT FROM AUTHOR]

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN. [ABSTRACT FROM AUTHOR]

The cyber-physical power system (CPPS) is a modern infrastructure utilising information and communication technology that has become more vulnerable to cyberattacks in recent years. The attack magnitude injected by the adversary is stealthier and it cannot be detected using conventional bad data detection techniques. Protecting sensitive data from data integrity attacks (DIA) is essential for ensuring system security and reliability. A tragic event will occur if the attack goes unreported. Therefore, DIA detection is highly vital for the operator in the control centre to make important decisions. This paper addresses the attack impact on WAC applications and attack detection using the model-based method and data-driven-based methods. On the basis of the validation of performance indicators, various detection approaches are simulated and compared to determine the best detection strategy. Simulation results show that in the model-based anomaly detection method, the recursive polynomial model estimator (RPME) has better detection performance than the recursive least square estimator (RLSE). The convolutional neural network- (CNN-) based data-driven anomaly detection technique outperforms other machine learning (ML) techniques such as support vector machine (SVM), K-nearest neighbour (KNN), and random forest (RF). On the WSCC 3 machine 9-bus system, the efficacy of the suggested methods is evaluated. [ABSTRACT FROM AUTHOR]

Credit-risk prediction is one of the challenging tasks in the banking industry. In this study, a hybrid convolutional neural network—support vector machine/random forest/decision tree (CNN—SVM/RF/DT) model has been proposed for efficient credit-risk prediction. We proposed four classifiers to develop the model. A fully connected layer with soft-max trained using an end-to-end process makes up the first classifier and by deleting the final fully connected with soft-max layer, the other three classifiers—a SVM, RF, and DT classifier stacked after the flattening layer. Different parameter values were considered and fine-tuned throughout testing to select appropriate parameters. In accordance with the experimental findings, a fully connected CNN and a hybrid CNN with SVM, DT, and RF, respectively, achieved a prediction performance of 86.70%, 98.60%, 96.90%, and 95.50%. According to the results, our suggested hybrid method exceeds the fully connected CNN in its ability to predict credit risk. [ABSTRACT FROM AUTHOR]

Power quality disturbances (PQDs) can lead to significant operational and financial losses in power systems. Accurate detection and classification of PQDs are essential for maintaining power quality and preventing power system failures. This research article introduces an innovative approach for the precise detection and classification of single- and multiple-state power quality disturbances (PQDs) using the Stockwell transform (ST) and a random forest classifier. To create realistic PQD signals, seventeen distinct classes are generated in accordance with IEEE Standard 1159, employing mathematical equations implemented in MATLAB software. The ST is employed to extract relevant features from the PQD signals, which are subsequently utilized as input for the random forest classifier. The classifier employs bootstrapping sampling to generate multiple training sets from the original dataset. Each training set is used to construct a decision tree by recursively partitioning the data based on significant features. To mitigate overfitting and enhance robustness, a random subset of features is selected at each node of the decision tree, thereby reducing tree correlation. The performance of the random forest classifier is compared with other widely utilized machine learning classifiers. The results exhibit the efficacy of the proposed approach in accurately detecting and classifying PQ events, highlighting its superiority over alternative methods. [ABSTRACT FROM AUTHOR]

Introduction. Sepsis, the leading cause of death in hospitalized patients globally, was investigated in this study, examining the varying effects of positive fluid balance on sepsis subtypes through causal inference. Methods. In this study, data from the eICU database were utilized, extracting 35 features from sepsis patients. Fluid balance during ICU stay was the treatment, and ICU mortality was the primary outcome. Data preprocessing ensured linear assumptions for logistic regression. Binarized positive fluid balance with mortality was examined using DoWhy's logistic regression, while continuous data were analyzed with random forest T-learner. ATE served as the primary metric. Results. Results revealed that septic patients with higher fluid balance had worse mortality outcomes, with an ATE of 0.042 (95% CI: (0.034, 0.047)) using logistic regression and an ATE of 0.0340 (95% CI: (0.028–0.040)) using T-learner. In the pulmonary sepsis subtype, higher mortality was associated with increased fluid balance, showing an ATE of 0.047 (95% CI: (0.037, 0.055)) using logistic regression and an ATE of 0.28 (95% CI: (0.22, 0.34)) with T-learner. Conversely, urinary sepsis patients had improved mortality with higher fluid balance, presenting an ATE of −0.135 (95% CI: (−0.024, −0.0035)) using logistic regression and an ATE of −0.28 (95% CI: (−0.34, −0.22)) with T-learner. Conclusion. Our research implies that fluid balance impact on ICU mortality differs among sepsis subtypes. Positive fluid balance raises mortality in sepsis and pulmonary sepsis but may protect against urinary sepsis. Further trials are needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Batteries are the most expensive component of battery electric vehicles (BEVs), but they degrade over time and battery operation. State of health (SOH) forecasting models learn how battery operation over long-time periods of weeks or months influences battery aging. Currently, existing methods for SOH forecasting of lithium-ion batteries based on deep neural network (DNN) models lack explainability of their forecasts due to their inherent black box character. However, the explainability of forecasts is essential to build user trust into the forecasting models. In this work, we address this problem from two perspectives: First, we compared four machine learning (ML) models like decision tree and random forest, which are inherently transparent, to two new DNN architectures with a more inherent black box character. Second, we proposed a new method using Gaussian-filtered saliency maps to visualize battery operational states that are relevant to DNN models. This method is applied to the best DNN models previously trained. We used an extensive data corpus consisting of five public data sets with different operational conditions, battery types, and aging trajectories. Furthermore, we show that the Gaussian-filtered saliency maps meaningfully visualize battery operational states that are consistent with findings from controlled laboratory aging experiments. Thus, this work was able to add transparency and interpretability to the SOH forecasting results of two state-of-the-art DNNs, while maintaining their superior performance compared to transparent ML models, while mitigating their inherent black box character. [ABSTRACT FROM AUTHOR]

The final approach phase of an aircraft accounts for nearly half of all aviation incidents worldwide due to low-level wind shear, heavy downpours, runway excursions, and unsteady approaches. Adopting the missed approach (MAP) procedures may prevent a risky landing, which is usually executed in those situations, but it is safety-critical and a rare occurrence. This study employed machine learning-ensemble imbalance learning to predict MAPs under low-level wind shear conditions based on environmental and situational parameters. The models were developed using the 2017–2021 Hong Kong International Airport (HKIA) Pilot Reports (PIREPs). Initially, imbalance data were applied to machine learning models such as the random forest (RF), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), but these were unable to accurately predict the occurrence of MAPs. Then, these models were used as base estimators for ensemble imbalance learning methods, including the self-paced ensemble (SPE) framework, the balance cascade model, and the easy ensemble model. The SPE framework utilizing XGboost as the base estimator performed better than other frameworks in terms of recall, F1-score, balanced accuracy, and geometric mean. Afterwards, SHAP was utilized to interpret the SPE framework with XGboost as the base estimator. Results showed that low-level wind shear magnitude, runway orientation, and vertical location of low-level wind shear contributed most to MAPs. Runways 07C and 07R had the most MAPs. Most MAPs were initiated when low-level wind shear was within 500 feet of the ground. Strong tailwind triggered MAPs more than headwind. For aviation safety researchers and airport authorities, the framework proposed in this study is a valuable tool. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is one of the significant common neurological disorders of the current age that causes uncontrollable movements like shaking, stiffness, and difficulty. The early clinical diagnosis of this disease is essential for preventing the progression of PD. Hence, an innovative method is proposed here based on combining the crow search algorithm and decision tree (CSADT) for the early PD diagnosis. This approach is used on four crucial Parkinson's datasets, including meander, spiral, voice, and speech-Sakar. Using the presented method, PD is effectively diagnosed by evaluating each dataset's critical features and extracting the primary practical outcomes. The used algorithm was compared with other machine learning algorithms of k-nearest neighbor (KNN), support vector machine (SVM), naive Baye (NB), multilayer perceptron (MLP), decision tree (DT), random tree, logistic regression, support vector machine of radial base functions (SVM of RBFs), and combined classifier in terms of accuracy, recall, and combination measure F1. The analytical results emphasize the used algorithm's superiority over the other selected ones. The proposed model yields nearly 100% accuracy through various trials on the datasets. Notably, a high detection speed achieved the lowest detection time of 2.6 seconds. The main novelty of this paper is attributed to the accuracy of the presented PD diagnosis method, which is much higher than its counterparts. [ABSTRACT FROM AUTHOR]

This project aims to investigate the best machine learning (ML) algorithm for classifying sounds originating from the environment that were considered noise pollution in smart cities. Sound collection was carried out using necessary sound capture tools, after which ML classification models were utilized for sound recognition. Additionally, noise pollution monitoring using Python was conducted to provide accurate results for sixteen different types of noise that were collected in sixteen cities in Malaysia. The numbers on the diagonal represent the correctly classified noises from the test set. Using these correlation matrices, the F1 score was calculated, and a comparison was performed for all models. The best model was found to be random forest. [ABSTRACT FROM AUTHOR]

The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second-most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model's accuracy in heart disease prediction. When compared to the other five models, the random forest and k-nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k-nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. Six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for accuracy, precision, recall, and F measure are used to evaluate models. [ABSTRACT FROM AUTHOR]

Humans are familiar with "diabetes," a chronic metabolic disease that causes resistance to insulin in the human body, and about 425 million cases worldwide. Diabetes is a hazard to human health since it can gradually cause significant damage to the heart, blood vessels, eyes, kidneys, and nerves. As a result, it is critical to recognize diabetes early on to minimize its negative consequences. Over the years, artificial intelligence (AI) technology and data mining methods are playing a crucial role in detecting diabetic patients. Considering this opportunity, we present a fine-tuned random forest algorithm with the best parameters (RFWBP) that is used with the RF algorithm and feature engineering to detect diabetes patients at an early stage. We have employed several data processing techniques (e.g., normalization, conversion into numerical data) to raw data during the prepossessing phase. After that, we further applied some data mining techniques, adding related characteristics to the primary dataset. Finally, we train the proposed RFWBP and conventional methods like the AdaBoost algorithm, support vector machine, logistic regression, naive Bayes, multilayer perceptron, and a regular random forest with the dataset. Furthermore, we also utilized 5-fold cross-validation to enhance the performance of the RFWBP classifier. The proposed RFWBP achieved an accuracy of 95.83% and 90.68% with and without 5-fold cross-validation, respectively. Moreover, the proposed RFWBP is compared with conventional machine learning methods to evaluate the performance. The experimental results confirm that the proposed RFWBP outperformed conventional machine learning methods. [ABSTRACT FROM AUTHOR]

Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early-stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi-square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K-nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best-fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time. [ABSTRACT FROM AUTHOR]

Landslides occur in most countries. As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide-prone areas for various construction projects. In recent years, supervised machine learning algorithms have been widely used in landslide susceptibility evaluation, but many flaws remain in the selection of nonlandslide point samples for comparative analysis. It is significant to improve the authenticity of sample data and reduce the impact of noise. China's Funing County was used as a case study in this paper, which first identified 122 landslide incidents based on historical data, fieldwork, and remote sensing images to create a landslide inventory in the research area. In addition, 12 causal factors of landslides were determined, including elevation, slope, aspect, plan curvature, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, normalized difference vegetation index (NDVI), lithology, and land cover. K-means clustering was used to purify the factor data factors before data-driven certainty factor (CF) and frequency ratio (FR) models, and machine learning models, random forest (RF) and artificial neural network (ANN), were used for a comparative study on landslide susceptibility evaluation in Funing County. The results show that the selection method of nonlandslide sample data will affect the accuracy of different evaluation models. The purified sample data improved the prediction accuracy of the four models, with significant prediction accuracy improvements observed in the ANN model. The purification of nonlandslide sample data by K-means method is of great significance for the drawing of landslide sensitivity map. [ABSTRACT FROM AUTHOR]

Secondary crashes (SCs) are typically defined as the crash that occurs within the spatiotemporal boundaries of the impact area of the primary crashes (PCs), which will intensify traffic congestion and induce a series of road safety issues. Predicting and analyzing the time and distance gaps between the SCs and PCs will help to prevent the occurrence of SCs. In this paper, a combined data-driven method of static and dynamic approaches is applied to identify SCs. Then, the random forests (RF) method is implemented to predict the two gaps using temporal, primary crash, roadway, and real-time traffic characteristics data collected from 2016 to 2019 at California interstate freeways. Subsequently, the SHapley Additive explanation (SHAP) approach is employed to interpret the RF outputs. The results show that the traffic volume, speed, lighting, and population are considered the most significant factors in both gaps. Furthermore, the main and interaction effects of factors are also quantified. High volume possibly promotes the time and distance gaps, while low volume inhibits them. And volume affects the distance gap inconsiderably when it falls between 300 and 400 veh/5 min. Traffic conditions with high speed and low volume are strongly associated with short-time and short-distance gaps. Darker surroundings probably accelerate the occurrence of SCs. Moreover, crashes involving the violation categories of improper turns or unsafe lane changes likely result in long time and distance gaps. These results have important implications for proposing traffic management and improving road safety. [ABSTRACT FROM AUTHOR]

This study discusses the practical engineering problem of determining random load sources on coal-rock structures. A novel combined regularization technique combining mollification method (MM) and discrete regularization (DR), which was called MM-DR technique, was proposed to reconstruct random load sources on coal-rock structures. MM-DR technology is compared with DR, Tikhonov regularization (TR), and maximum entropy regularization (MER) in load reconstruction. The results show that the reconstructed random load sources are more consistent with the real load sources using MM-DR technique combined with particle swarm optimization (PSO) and L-curve method, which was named as PSO-L method, and selecting optimal ω value of kernel function is beneficial to overcome the ill-posed of random load sources reconstruction and to obtain the stable and approximate solutions. The method proposed in this study provides a theoretical basis for the recognition of coal-rock interface. [ABSTRACT FROM AUTHOR]

Surgery is the most important treatment for perianal abscesses. However, the gut microbiota of patients with perianal abscess and the effects of perianal abscess on the gut microbiota after surgery are unknown. In this study, significant changes in interleukin 6 and tumor necrosis factor-α in the blood of healthy subjects, patients with perianal abscesses, and patients after perianal abscess surgery were identified. 16S rRNA gene sequencing technology was used to detect the changes in the gut microbiota among 30 healthy individuals and 30 patients with perianal abscess before and after surgery. Venn diagrams and alpha diversity analyses indicated differences in the abundance and uniformity of gut microbiota between the healthy individuals and patients with perianal abscesses before and after surgery. Beta diversity analysis indicated that the grouping effects among the control, abscess, and surgery groups were good. The classification and compositional analysis showed significant differences in the gut microbiota between healthy individuals and patients with perianal abscesses before and after surgery. LEfSe analysis, random forest analysis, and ROC curve analysis showed that Klebsiella (AUC = 0.7467) and Bilophila (AUC = 0.72) could be potential biomarkers for the diagnosis of perianal abscess. The functional prediction results showed that the differential microbiota is significantly enriched in the pathways related to nutrition and drug metabolism. This study may have important implications for the clinical management and prognostic assessment of patients with perianal abscesses. [ABSTRACT FROM AUTHOR]

Background. Granuloma annulare (GA) and sarcoidosis are granulomatous inflammatory diseases that share similarities. Objective. To identify the histological and immunohistochemical (IHC) features of GA and sarcoidosis. Methods. A retrospective review of 36 patients with GA and 26 with sarcoidosis was performed. Results from hematoxylin and eosin (H&E) staining and IHC staining of MMP-9 and pSTAT1 within the skin lesions of GA and sarcoidosis were analyzed, and random forest was applied for developing a predictive model. Results. Significantly greater expressions of MMP-9 (especially in elastic fibers, EFs, P < 0.0001) and pSTAT1 (P = 0.0003) were observed in lesion samples of GA versus sarcoidosis patients. In GA patients, MMP-9 was significantly upregulated in the interstitial type (P = 0.0222), while staining of pSTAT1 was positively correlated with the area of mucinous collagen in palisading GA (R = 0.5356 , P = 0.0484). In sarcoidosis patients, MMP-9 (R = − 0.7127 , P = 0.0009) and pSTAT1 (R = − 0.5604 , P = 0.0067) were found to show stronger expressions in lesions with less lymphocyte infiltration. The predictive model demonstrated an AUC of 0.9675. Conclusion. These results indicate that MMP-9 and pSTAT1 might exert roles in granulomatous inflammation in different modes, and the presence of more robust MMP-9 staining in EFs appears to be more suggestive of GA. [ABSTRACT FROM AUTHOR]

To diagnose an illness in healthcare, doctors typically conduct physical exams and review the patient's medical history, followed by diagnostic tests and procedures to determine the underlying cause of symptoms. Chronic kidney disease (CKD) is currently the leading cause of death, with a rapidly increasing number of patients, resulting in 1.7 million deaths annually. While various diagnostic methods are available, this study utilizes machine learning due to its high accuracy. In this study, we have used the hybrid technique to build our proposed model. In our proposed model, we have used the Pearson correlation for feature selection. In the first step, the best models were selected on the basis of critical literature analysis. In the second step, the combination of these models is used in our proposed hybrid model. Gaussian Naïve Bayes, gradient boosting, and decision tree classifier are used as a base classifier, and the random forest classifier is used as a meta-classifier in the proposed hybrid model. The objective of this study is to evaluate the best machine learning classification techniques and identify the best-used machine learning classifier in terms of accuracy. This provides a solution for overfitting and achieves the highest accuracy. It also highlights some of the challenges that affect the result of better performance. In this study, we critically review the existing available machine learning classification techniques. We evaluate in terms of accuracy, and a comprehensive analytical evaluation of the related work is presented with a tabular system. In implementation, we have used the top four models and built a hybrid model using UCI chronic kidney disease dataset for prediction. Gradient boosting achieves around 99% accuracy, random forest achieves 98%, decision tree classifier achieves 96% accuracy, and our proposed hybrid model performs best getting 100% accuracy on the same dataset. Some of the main machine learning algorithms used to predict the occurrence of CKD are Naïve Bayes, decision tree, K-nearest neighbor, random forest, support vector machine, LDA, GB, and neural network. In this study, we apply GB (gradient boosting), Gaussian Naïve Bayes, and decision tree along with random forest on the same set of features and compare the accuracy score. [ABSTRACT FROM AUTHOR]

Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull. [ABSTRACT FROM AUTHOR]

The combination of traditional Chinese medicine (TCM) and Western medicine is a promising method for treating rheumatoid arthritis (RA). Combining the two fully exploits the advantages of Western and TCM to treat RA and has the potential to greatly improve the therapeutic effect on RA. In this study, we developed a combination drug training set by using 16 characteristic variables based on the characteristics of small molecules of TCM ingredients and Food and Drug Administration-certified combination drug data downloaded from the DrugCombDB database. Furthermore, we compared the prediction and classification abilities of five models: the k -nearest neighbors, naive Bayes, support vector machine, random forest, and AdaBoost algorithms. The random forest model was selected as the classification and prediction model for Western and TCM and Western combination drugs. We collected data for 41 small molecules of TCM ingredients from the Traditional Chinese Medicine Systems Pharmacology database and 10 small molecule drugs commonly used in anti-RA treatment from the DrugBank database. Combinations of Western and TCM for anti-RA treatment were screened. Finally, the CellTiter-Glo method was used to determine the synergy of these combinations, and the 15 most predicted drug combinations were carried out experimental verification. Myricetin, rhein, nobiletin, and fisetin had high synergy with celecoxib, and rhein had high synergy with hydroxychloroquine. The preliminary findings of this study can be further applied for practical clinical anti-RA combined treatment strategies and serve as a reference for clinical treatment of RA with integrated Western and TCM. [ABSTRACT FROM AUTHOR]

Crash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. Secondly, the multiparameter fusion clustering analysis method is used to indicate that the sample data of different time series have different effects on the crash risk. Then, the random forest model is used to screen several traffic flow variables that affect the highway crash risk. Thereafter, the downstream mean speed (ASD1D2), the upstream mean occupancy (AOU1U2), and the speed difference (DSU1D1) on the nearest detector were determined as the explanatory variables of the crash risk prediction model. Finally, based on the three variables, the dynamic Bayesian network model for highway traffic crash risk prediction is proposed. The overall prediction accuracy of this model is 84.9%, the crash prediction accuracy is 60.8%, and the non-crash prediction accuracy is 92.3%. Also, the prediction results show that the dynamic Bayesian model has better prediction effect than the static Bayesian model for the same sample data. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of the prediction method of future development measures and indicators of fault block reservoir in the current oilfield practical application, a prediction method of fault block reservoir measures and indicators based on the random forest method and LightGBM is proposed, which can help the oilfield make more effective decisions in the middle and later development. Firstly, using the advantages of random forest (RF) in dealing with high-dimensional data sets, the main controlling factors are selected by feature analysis. Then, the measure prediction model is established by using the 1DCNN-LightGBM algorithm. Firstly, 1DCNN processes the reservoir dynamic data and then trains the LightGBM model with the extracted time series characteristics and static data characteristics as input to predict the measure indexes of fault block reservoir. The evaluation results show that the prediction models proposed in this paper have good performance and can obtain more accurate prediction results and more stable prediction performance. It provides a basis for the future planning and optimization of the oilfield. [ABSTRACT FROM AUTHOR]

This paper used the data of automobile traffic accidents from 2018 to 2020 in the Chinese National Automobile Accident In-Depth Investigation System. The prediction features of traffic accident severity are innovated. Four accident features that did not participate in the importance ranking were added: accident location, accident form, road information, and collision speed. Eight accident features (engine capacity, hour of day, age of vehicle, month of year, day of week, age band of drivers, vehicle maneuver, and speed limit) have been used in previous studies. Random forest was used to rank the importance of 12 accident features, and 7 important accident features were finally adopted. By comparing the algorithms and optimizing the results, the prediction model of traffic accident degree with higher accuracy is finally obtained. [ABSTRACT FROM AUTHOR]

An index for reporting air quality is called the air quality index (AQI). It measures the impact of air pollution on a person's health over a short period of time. The purpose of the AQI is to educate the public on the negative health effects of local air pollution. The amount of air pollution in Indian cities has significantly increased. There are several ways to create a mathematical formula to determine the air quality index. Numerous studies have found a link between air pollution exposure and adverse health impacts in the population. Data mining techniques are one of the most interesting approaches to forecast AQI and analyze it. The aim of this paper is to find the most effective way for AQI prediction to assist in climate control. The most effective method can be improved upon to find the most optimal solution. Hence, the work in this paper involves intensive research and the addition of novel techniques such as SMOTE to make sure that the best possible solution to the air quality problem is obtained. Another important goal is to demonstrate and display the exact metrics involved in our work in such a way that it is educational and insightful and hence provides proper comparisons and assists future researchers. In the proposed work, three distinct methods—support vector regression (SVR), random forest regression (RFR), and CatBoost regression (CR)—have been utilized to determine the AQI of New Delhi, Bangalore, Kolkata, and Hyderabad. After comparing the results of imbalanced datasets, it was found that random forest regression provides the lowest root mean square error (RMSE) values in Bangalore (0.5674), Kolkata (0.1403), and Hyderabad (0.3826), as well as higher accuracy compared to SVR and CatBoost regression for Kolkata (90.9700%) and Hyderabad (78.3672%), while CatBoost regression provides the lowest RMSE value in New Delhi (0.2792) and the highest accuracy is obtained for New Delhi (79.8622%) and Bangalore (68.6860%). Regarding the dataset that was subjected to the synthetic minority oversampling technique (SMOTE) algorithm, it is noted that random forest regression provides the lowest RMSE values in Kolkata (0.0988) and Hyderabad (0.0628) and higher accuracies are obtained for Kolkata (93.7438%) and Hyderabad (97.6080%) in comparison to SVR and CatBoost regression, whereas CatBoost regression provides the highest accuracies for New Delhi (85.0847%) and Bangalore (90.3071%). This demonstrated definitely that datasets that had the SMOTE algorithm applied to them produced a higher accuracy. The novelty of this paper lies in the fact that the best regression models have been picked through thorough research by analyzing their accuracies. Moreover, unlike most related papers, dataset balancing is carried out through SMOTE. Moreover, all of the implementations have been documented via graphs and metrics, which clearly show the contrast in results and help show what actually caused the improvement in accuracy. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively. [ABSTRACT FROM AUTHOR]

Under the background of the deepening reform of China's electric power industry, how to make use of existing resources, keep existing employees, and identify and distinguish the influencing factors of turnover have become major problems faced by companies. At the same time, the identification of influencing factors also provides a reference for relevant enterprises to do a good job in talent reserve and recruitment. The influencing factors of employee turnover are numerous and interrelated. Identifying and distinguishing the influencing factors are the focus of academic research. Meanwhile, it provides a reference for relevant enterprises to do an excellent job in personnel reserve and recruitment. Aiming at the limitation of constructing a direct matrix based on experts' scoring method in the traditional DEMATEL model, a model combining random forest and DEMATEL is proposed, applied to identify the influencing factors of employee turnover in electric power enterprises in Qinghai Province. Based on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020, the empirical analysis is conducted to analyze the influencing factors of employee turnover from two perspectives of the degree of centrality and the degree of causal. The research results show that marital status, household registration, political status, type of job, and the location of the work unit are the key influencing factors, providing a theoretical basis for the early warning of employee turnover and confirming the feasibility of the random forest-DEMATEL method proposed by us. [ABSTRACT FROM AUTHOR]

Sarcoma, the second common type of solid tumor in children and adolescents, has a wide variety of subtypes that are often not properly diagnosed at an early stage, leading to late metastases and causing serious loss of life and property to patients and families. It exhibits a high degree of heterogeneity at the cellular, molecular, and epigenetic levels, where DNA methylation has been proposed to play a role in the diagnosis of sarcoma subtypes. Thus, this study is aimed at finding potential biomarkers at the DNA methylation level to distinguish different sarcoma subtypes. A machine learning process was designed to analyse sarcoma samples, each of which was represented by lots of methylation sites. Irrelevant sites were removed using the Boruta method, and remaining sites related to the target variables were kept for further analyses. Afterward, three feature ranking methods (LASSO, LightGBM, and MCFS) were adopted to rank these features, and six classification models were constructed by combining incremental feature selection and two classification algorithms (decision tree and random forest). Among these models, the performance of RF model was higher than that of DT model under all three ranking conditions. The specific expression of genes obtained from the annotation of highly correlated methylation site features, such as PRKAR1B, INPP5A, and GLI3, was proven to be associated with sarcoma by publications. Moreover, the quantitative rules obtained by decision tree algorithm helped us to understand the essential differences between various sarcoma types and classify sarcoma subtypes, providing a new means of clinical identification and determining new therapeutic targets. [ABSTRACT FROM AUTHOR]

Battery swapping stations effectively address the challenges of long charging times, lack of charging stations, and safety hazards for electric two-wheelers. With the rapid development of shared electric bicycles and takeaways, the scale of electric two-wheeler users is expanding while generating a huge demand for battery swapping. The research on the planning and operation of battery swapping stations (BSSs) for electric two-wheelers has yet to be widely discussed. This study developed a data-driven optimization model based on machine learning algorithms using Beijing's battery swapping stations and point of interest (POI) dataset. First, through the spatial features of BSS analyzed by ArcGIS, we found that the coverage of BSSs was mainly concentrated within the fifth ring road, and the utilization rate was unbalanced. Then, on a 3000 m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. The final stacking model was constructed by strengthening three single models with an accuracy of 86.21%. Compared with the original BSSs layout, the machine-learning algorithm proposed in this study can cover more factors and avoid the subjectivity of site selection. Finally, the queuing model for BSSs based on the Monte Carlo simulation was proposed. Through two scenarios, we found that the key parameters m (the number of charging slots) and λ (the user arrival rate) were influential to the outputs of service capability. [ABSTRACT FROM AUTHOR]

Traditional energy sources such as fossil fuels can cause environmental pollution on the one hand, and on the other hand, there will be a shortage of diminishing stocks. Recently, a variety of new energy sources have been proposed by scientists, such as nuclear energy, hydrogen energy, wind energy, water energy, and solar energy. There are already many technologies for converting and storing energy generated from new energy systems, such as various storage batteries. One of the keys to the commercialization of these new energy sources is to explore new materials. Researchers have performed a lot of research on new energy material preparation, mechanical properties, radiation resistance, energy storage, etc. However, new energy metal materials are still unable to combine radiation resistance, good mechanical properties, excellent energy storage, and other characteristics. There is still a lack of breakthrough materials with better performance or more stable structure. Recently, researchers have discovered that high-entropy alloys have become one of the most promising new energy metal materials. Because it not only has high energy storage and high strength, but also has high stability and high radiation resistance, and is easy to form a simple phase, the prediction of phases in high-entropy energy alloys is very critical, and the generation of designed phases in high-entropy energy alloys is a very important step. In this study, three machine learning algorithms were used to predict the generated phase classification in high-entropy alloys, namely, support-vector machine (SVM) model, decision tree (DT) model, and random forest (RF) model. The models are optimized by grid search methods and cross-validated, and performance was evaluated with the aim of significantly improving the accuracy of generative phase prediction, and the results show that the random forest algorithm has the best prediction ability, reaching 0.93 prediction accuracy. The ROC (receiver operating characteristic) curve of the model shows that the random forest algorithm has the best classification of solid-solution (SS) phases, where the classification probabilities AUC (area under the curve) area for amorphous phase (AM), intermetallic phase (IM), and solid-solution phase (SS), respectively, are 0.95, 0.96, and 1, respectively, , which can predict the generated phases of high-entropy energy alloys well. [ABSTRACT FROM AUTHOR]

Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments. [ABSTRACT FROM AUTHOR]

Objective. The effect of vaginal microbiota on spontaneous preterm birth (sPTB) has not been fully addressed, and few studies have explored the associations between vaginal taxa and sPTB in the gestational diabetes mellitus (GDM) and non-GDM groups, respectively. Study Design. To minimize external interference, a total of 41 pregnant women with sPTB and 308 controls (pregnant women without sPTB) from same regain were enrolled in this case-cohort study. Controls were randomly selected at baseline. With the exception of GDM, other characteristics were not significantly different between the two groups. Vaginal swabs were collected at early second trimester. Using 16S amplicon sequencing, the main bioinformatics analysis was performed on the platform of QIIME 2. Vaginal microbiota traits of the sPTB group were compared with controls. Finally, the effects of binary taxa on sPTB in the GDM group and the non-GDM group were analyzed, respectively. Results. The proportion of GDM in the sPTB (19.51%) was higher than the controls (7.47%, P = 0.018). The vaginal microbiota of pregnant women with sPTB exhibited higher alpha diversity metrics (observed features, P = 0.001 ; Faith's phylogenetic diversity, P = 0.013) and different beta diversity metrics (unweighted UniFrac, P = 0.006 ; Jaccard's distance, P = 0.004), compared with controls. The presence of Lactobacillus paragasseri/gasseri (aOR: 3.12, 95% CI: 1.24-7.84), Streptococcus (aOR: 3.58, 95% CI: 1.68-7.65), or Proteobacteria (aOR: 3.39, 95% CI: 1.55-7.39) was associated with an increased risk of sPTB in the non-GDM group (P < 0.05). However, the relative abundance of novel L. mulieris (a new species of the L. delbrueckii group) was associated with a decreased risk of sPTB (false discovery rate, 0.10) in all pregnant women. Conclusion. GDM may modify the association of vaginal taxa with sPTB, suggesting that maternal GDM should be considered when using vaginal taxa to identify pregnant women at high risk of sPTB. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, fuel cells have attracted a lot of attention because of their unique efficiency, high ‐power density and zero gas emission, and many studies have been conducted to improve their efficiency. The difficulties that occur must be fully grasped and minimized to optimize the energy efficiency and the performance of the fuel cells. To increase the performance of Pt/C catalysts and ensure effective synthesis, precise control of the synthesis conditions is necessary. In the present study, the effect of the synthesis process parameters on the catalyst performance used in fuel cells was comprehensively investigated using statistical methods and machine learning algorithms. The polyol synthesis process was implemented to prepare efficient Pt/C electrocatalysts with reducing synthesis cost and time. The synthesis parameters including duration of reaction, pH and reaction temperature were experimentally studied to determine the optimal working conditions. This study also intended to create an adequate mathematical model with response surface methodology and a prediction model with machine learning algorithms to predict the amount of reduced Pt and the ECSA value depending on the synthesis parameters, and to understand the interaction of parameters. Various ML algorithms that are multilayer perceptron artificial neural network (MLP‐ANN), support vector regression (SVR) and random forest (RF) model were used and each model's performance was evaluated using several performance indicators (R2, mean absolute errors, mean squared error and root mean square errors). The results show that pH is the prominent parameter for both responses. To obtain maximum Pt/C electrocatalyst performance and reduction of Pt, the optimum parameters are determined as pH of 4, reaction temperature of 135°C, and reaction duration of 1 hour. The validation results show a good agreement between predicted and experimental data is obtained with the developed model. Results have obviously shown that this approach can effective in optimizing the electrocatalyst performance with the multiple process parameters. Moreover, it was found that the MLP‐ANN model was outperformed to predict electrocatalyst performance of Pt/C more precisely compared to SVR and RF model. [ABSTRACT FROM AUTHOR]

Machine learning algorithms, especially random forests (RFs), have become an integrated part of the modern scientific methodology and represent an efficient alternative to conventional parametric algorithms. This study aimed to assess the influence of data features and overdispersion on RF regression performance. We assessed the effect of types of predictors (100, 75, 50, and 20% continuous, and 100% categorical), the number of predictors (p = 816 and 24), and the sample size (N = 50, 250, and 1250) on RF parameter settings. We also compared RF performance to that of classical generalized linear models (Poisson, negative binomial, and zero-inflated Poisson) and the linear model applied to log-transformed data. Two real datasets were analysed to demonstrate the usefulness of RF for overdispersed data modelling. Goodness-of-fit statistics such as root mean square error (RMSE) and biases were used to determine RF accuracy and validity. Results revealed that the number of variables to be randomly selected for each split, the proportion of samples to train the model, the minimal number of samples within each terminal node, and RF regression performance are not influenced by the sample size, number, and type of predictors. However, the ratio of observations to the number of predictors affects the stability of the best RF parameters. RF performs well for all types of covariates and different levels of dispersion. The magnitude of dispersion does not significantly influence RF predictive validity. In contrast, its predictive accuracy is significantly influenced by the magnitude of dispersion in the response variable, conditional on the explanatory variables. RF has performed almost as well as the models of the classical Poisson family in the presence of overdispersion. Given RF's advantages, it is an appropriate statistical alternative for counting data. [ABSTRACT FROM AUTHOR]

Technology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Because of that, accidents and fatalities may occur due to drivers' different behaviors such as sports style and moderation. To advance driver safety, security, and comfort, Advanced Driver Assistance Systems (ADAS) must be personalized. Modern cars have ADAS that relieves the driver of some of the tasks they perform while driving. As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. Using task models, we simulate a low-cost driver assisted scheme in real time, where various scenarios are explored and the schedulability of tasks is established before implementing them in EV. The new driver assistance scheme has several advantages over the existing options. It lowers the risk of an accident and ensures driver safety. The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy. [ABSTRACT FROM AUTHOR]

The mining of underground coal resources can trigger geological hazards such as subsidence basins, cave-in pits, and step cracks. In China, the probability integral method (PIM), the most popular method for predicting surface movement deformation caused by coal resource mining, has a prediction accuracy that is mainly influenced by both the measurement data (i.e., quantity and quality) from ground movement observatories and the parameter inversion method. To obtain more accurate PIM parameters in the absence of observational data, we propose a combined machine learning model (RF-AGA-ENN)—random forest (RF) extracts the best combination of features as the input layer of Elman neural network (ENN); ant colony algorithm (ACO) and genetic algorithm (GA) are combined (called AGA) for the weights and thresholds of ENN optimization. The results of the study show that (1) the RF-AGA-ENN model is used to obtain PIM values with MAXRE values between 1.94% and 9.18%, AVERY values between 0.98% and 3.98%, and RMSE values between 0.0050 and 0.9632. (2) Compared with the PIM parameters obtained from BP neural network, RF-ENN, RF-ACO-ENN, and RF-GA-ENN models, the PIM parameters obtained from the RF-AGA-ENN model have better stability and accuracy. (3) According to the PIM parameters obtained by the RF-AGA-ENN model, the predicted and measured values of surface settlement at the 11111 working face have a high degree of agreement. In summary, the RF-AGA-ENN model to obtain the PIM parameters has good application value. [ABSTRACT FROM AUTHOR]

Let G be a finite simple graph with Laplacian polynomial ψ G , λ = ∑ k = 0 n − 1 n − k c k λ k . In an earlier paper, the coefficients c n − 4 and c n − 5 for forests with respect to some degree-based graph invariants were computed. The aim of this paper is to continue this work by giving an exact formula for the coefficient c n − 6 . [ABSTRACT FROM AUTHOR]

Fault diagnosis of rolling bearings under variable speed is a common issue in engineering practice, but it lacks an effective diagnosis algorithm, while approaches developed for steady speed cannot be directly applied. Therefore, for effectively identifying bearing faults under variable speed, this paper proposed a multiscale fractional dimensionless indicator (MSFDI) and put forward a fault diagnosis method with random forest (RF). It can overcome the feature space aliasing problem of traditional dimensionless indicators, which will lead to increased diagnosis uncertainty. The multiorder fractional Fourier transform is carried out on bearing signals to get a series of fractional Fourier domain components, which will be used to construct the original MSFDI feature set. Moreover, reliefF selects the sensitive MSFDIs as the input of the RF algorithm to determine the health condition. The effectiveness of the proposed method is verified by experiments and case studies. [ABSTRACT FROM AUTHOR]

The Triassic Yanchang Formation in the Pengyang area of Ordos Tianhuan depression is an important oil and gas formation. However, most of the oil pays in the study formations are low-resistivity or low-contrast reservoirs with low permeability, bringing challenges to the reservoir identification and evaluation by well logs. In this paper, we first measured the nuclear magnetic resonance (NMR), phase permeability, cation exchange capacity (CEC), and X-ray diffraction for core samples. Then, the genetic types for the low-resistivity pays were analyzed based on the experiment results, water analysis, and well log data collected. It was found that large variations of formation water salinity, high irreducible water saturation, and clay conductivity are the primary genetic types. Further, the random forest (RF) algorithm with sensitive parameter inputs was used to identify the oil, oil and water, and water layers. The anomaly of spontaneous potential (∆SP) that characterizes water salinity, the relative value of gamma ray log (∆GR) that describes the bound water content, resistivity, density, and acoustic logs were taken as sensitive logs according to the genetic analysis. Finally, this identification method was verified by comparison with the traditional crossplot method and oil test results. The identification accuracy of the RF is 90%, far higher than that by the crossplot method. [ABSTRACT FROM AUTHOR]

Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main characteristic caused by insufficient insulin secretion and poses a serious risk to human health. The objective is to construct a type-2 diabetes prediction model with high classification accuracy. Advanced machine learning and predictive model techniques are utilized to achieve cutting-edge techniques for the early diagnosis of diabetes. This paper proposes an efficient performance model to predict and classify the minority class of type-2 diabetes. The impact of oversampling and undersampling approaches to reduce the effect of an unbalanced class has been compared to classification performance algorithms. Synthetic Minority Oversampling (SMOTE) and Tomek-links techniques are applied and examined. The outcomes were then compared to the original unbalanced dataset using an artificial neural network (ANN) predictive model. The model is compared with other state-of-the-art classifiers such as support vector machine (SVM), random forest (RF), and decision tree (DT). The tuned model had the best accuracy of 92.2%. The experimental findings clearly manifest the improvement in accuracy and evaluation metrics in terms of AUC and F1-measure using the SMOTE oversampling strategy rather than the baseline and undersampling schemes. The study recommends adopting dynamic hyperparameter optimization to further improve accuracy. [ABSTRACT FROM AUTHOR]

Background. The epithelial mesenchymal transition (EMT) gene has been shown to be significantly associated with the prognosis of solid tumors; however, there is a lack of models for the EMT gene to predict the prognosis of AML patients. Methods. First, we downloaded clinical data and raw transcriptome sequencing data from the TCGA database of acute myeloid leukemia (AML) patients. All currently confirmed EMT-related genes were obtained from the dbEMT 2.0 database, and 30% of the TCGA data were randomly selected as the test set. Univariate Cox regression analysis, random forest, and lasso regression were used to optimize the number of genes for model construction, and multivariate Cox regression was used for model construction. Area under the ROC curve was used to assess the efficacy of the model application, and the internal validation set was used to assess the stability of the model. Results. A total of 173 AML samples were downloaded, and a total of 1184 EMT-related genes were downloaded. The results of univariate batch Cox regression analysis suggested that 212 genes were associated with patient prognosis, random forest and lasso regression yielded 18 and 8 prognosis-related EMT genes, respectively, and the results of multifactorial COX regression model suggested that 5 genes, CBR1, HS3ST3B1, LIMA1, MIR573, and PTP4A3, were considered as independent risk factors affecting patient prognosis. The model ROC results suggested that the area under the curve was 0.868 and the internal validation results showed that the area under the curve was 0.815. Conclusion. During this study, we constructed a signature model of five EMT-related genes to predict overall survival in patients with AML; it will provide a useful tool for clinical decision making. [ABSTRACT FROM AUTHOR]

The development of big data technology makes the feature selection technology gradually perfect. The advantages of different feature selection technologies are different. Among them, random forest algorithm belongs to integrated feature selection algorithm. Integrated processing of classification results can screen out the most representative feature impact. Based on this background and random forest algorithm, this paper analyzes the evaluation of motion effect. After the measurement, this paper obtains the body data before and after the training. After the calculation, the change data of the body index are determined. The random forest feature selection method is used as the carrier to determine the corresponding index attribute set. In the process of data set input, the corresponding whole input data set is formed through data classification. The completion of training, through the comparative experiment, is conducive to clear the degree of influence of physical indicators and then complete the exercise effect evaluation. The research shows that the random forest algorithm has significant advantages in the evaluation of sports effect and can effectively improve the accuracy of classification. [ABSTRACT FROM AUTHOR]

Prediction of protein-protein interaction (PPI) sites is one of the most perplexing problems in drug discovery and computational biology. Although significant progress has been made by combining different machine learning techniques with a variety of distinct characteristics, the problem still remains unresolved. In this study, a technique for PPI sites is presented using a random forest (RF) algorithm followed by the minimum redundancy maximal relevance (mRMR) approach, and the method of incremental feature selection (IFS). Physicochemical properties of proteins and the features of the residual disorder, sequence conservation, secondary structure, and solvent accessibility are incorporated. Five 3D structural characteristics are also used to predict PPI sites. Analysis of features shows that 3D structural features such as relative solvent-accessible surface area (RASA) and surface curvature (SC) help in the prediction of PPI sites. Results show that the performance of the proposed predictor is superior to several other state-of-the-art predictors, whose average prediction accuracy is 81.44%, sensitivity is 82.17%, and specificity is 80.71%, respectively. The proposed predictor is expected to become a helpful tool for finding PPI sites, and the feature analysis presented in this study will give useful insights into protein interaction mechanisms. [ABSTRACT FROM AUTHOR]

Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes. [ABSTRACT FROM AUTHOR]

Understanding the protein-RNA interaction mechanism can help us to further explore various biological processes. The experimental techniques still have some limitations, such as the high cost of economy and time. Predicting protein-RNA-binding sites by using computational methods is an excellent research tool. Here, we developed a universal method for predicting protein-specific RNA-binding sites, so one general model for a given protein was constructed on a fixed dataset by fusing the data of different experimental techniques. At the same time, information theory was employed to characterize the sequence conservation of RNA-binding segments. Conversation difference profiles between binding and nonbinding segments were constructed by information entropy (IE), which indicates a significant difference. Finally, the 19 proteins-specific models based on random forest (RF) were built based on IE encoding. The performance on the independent datasets demonstrates that our method can obtain competitive results when compared with the current best prediction model. [ABSTRACT FROM AUTHOR]

In this study, three different apple cultivars were dried using five different drying methods and moisture ratio (MR), moisture content (MC) and drying rate values were determined. Then, different machine learning algorithms (artificial neural network, k‐nearest neighbors, random forest, gaussian processes, and support vector regression) were used to estimate MR, MC and drying rate. For MR estimation of Golden Delicious, Oregon Spur and Granny Smith cultivars, Random Forest was most successful algorithm with correlation coefficients (R) of 0.9800, 0.9873, and 0.9841, respectively. This was followed by SVR with R: 0.9323 for Golden Delicious, ANN with R: 0.9766 for Oregon Spur and 5‐NN with R: 0.9827 for Granny Smith. MC and drying rate estimation results showed that RF, SVR, and k‐NN achieved higher R for all cultivars. It was concluded that machine learning algorithms are an effective approach for the accurate estimation of the drying characteristics of apple slices. Practical applications: Machine learning‐like precise modeling techniques are used to estimate the drying characteristics of agricultural commodities. Models should be assessed and compared for optimization of drying conditions and operational costs. Machine learning models predictions agreed well with testing data sets and they could be useful for understanding and controlling the factors affecting drying behaviors. [ABSTRACT FROM AUTHOR]