Key employee's turnover is one of the most important concerns of Human Resource Managers (HRM),Because the organization by losing its valuable staff, suffers from the loss of skills and experience gained over the years, so predicting employee turnover helps HRMs to hire and retain permanent employees. One of the effective tools in this regard is the use of different data mining methods. Many researchers have done research in this field. This study reviewes recently published articles based on machine Learning models, using Kaggle Human Resource (HR) databases [1-5] to compare them with this proposed models. In the article [9], the authors have selected 11 of the most important features by collecting common features from previous articles and filtering them using feature review and selection Algorithms. After converting non-numerical variables to numerical and normalizing the data in the range [0, 1], those attrition prediction approach is based on machine, deep and Ensemble Learning models and is experimented on a large-sized and a medium-sized simulated HR datasets and then a real small-sized dataset from a total of 450 responses. Those approach achieves higher Accuracy (0. 96, 0. 98 and 0. 99 respectively) for the three datasets when compared previous solutions. In 2021, authors examined the relationship between features using Pearson correlation coefficient and selected 11 features with the highest correlation coefficient. Then used from six different machine Learning Algorithms including Random Forest (RF), Logistic Regression (LR), …, to predict employee turnover. The highest accuracy they obtained was 0. 85 for RF [3]. In the article[1], the authors used two IBM datasets and a database containing HR information from a regional bank in the USA to predict employees turnover. After cleaning and preprocessing the data, the performance of 10 different machine Learning Algorithms such as Decision Tree (DT), RF, LR, Neural Network, …, was evaluated using ROC criteria on 10 small, medium, and large subsets of randomly selected, unassigned primary datasets. The average accuracy of Algorithms is 0. 83 in small datasets, 0. 81 in medium datasets and 0. 86 in large datasets. The authors of the paper [4] used three main experiments on IBM Watson simulated datasets to predict employees turnover. The first experiment involved training the original class-imbalanced dataset with the following machine Learning models: support vector machine with several kernel functions, random forest and K-nearest neighbour (KNN). The second experiment focused on using adaptive synthetic (ADASYN) approach to overcome class imbalance, then retraining on the new dataset using the abovementioned machine Learning models. As a result, training an ADASYN-balanced dataset with KNN (K = 3) achieved the highest performance, with 0. 93 F1-score. this turnover prediction approach is based on tree-based Ensemble Learning models and is experimented on a large-sized standard simulated HR dataset (hr_data), including 15, 000 samples with 10 features and a medium-sized (IBM) including 1470 samples with 34 features. The employees turnover rate in the IBM is 16. 1% and in the hr_data is 23. 8%, so datasets are unbalanced. To balance the data, the random-under-sampling technique and its combination of random-over-sampling with a ratio of 0. 5965 for the IBM and 0. 6558 for the hr_data has been used. In the preprocessing stage, Features with zero variance and samples containing the missing value were also removed. Then categorical (non-numeric) values ​​were converted to binary fields and then All features were scaled using data normalization in [0, 1]. In order to reduce the feature dimensions in the IBM dataset, we used the "Non-negative Matrix Factorization" (NMF) technique (n_components=17, max_iter=500) and For initialization, non-negative singular value analysis method with zeros filled with X value has been used. After reviewing and cleaning the data, in the processing stage, six different classification Algorithms, including KNN (k=1), RF (number of trees= 1500), DT, ExtraTreesClassifier (number of trees= 1000) and Support Vector Classifier were training on 70% of data. The optimal value of the hyperparameters for the Algorithms, was set using RandomizedSearchCV and GridSearchCV techniques. In order to investigate the effect of balancing and Dimensionality Reduction on the performance of models, experiments were performed in 3 stages (befor balancing, after balancing befor Dimensionality Reduction, after balancing and Dimensionality Reduction) on 30% of the remaining data. The results shown in Table (2-4) indicate that this proposed model, which uses tree-based optimized Ensemble Learning Algorithms with data balancing and NMF dimensionality reduction method, increases the f1score of turnover prediction. In the hr_data dataset, the best f1score for the RandomForest algorithm was 99. 52% and for the IBM HR dataset, the best f1score for the ExtraTreesClassifier algorithm was 95. 82%, which is higher than previous research. Table 5 compares the results of previous research with this research. Since, the prediction of employee attrition will not be enough without finding the characteristics that affect it, therefore, after building models and evaluating their performance, using a combined feature selection method by averaging the results of the single-variable feature selection method called "SelectKBest", and A wrapper feature selection method called "Recursive feature elimination" (RFE) with four Learning Algorithms RF, DT, ExtraTreesClassifier and AdaBoost, the most effective features were selected. SelectKBest combines the chi2 univariate statistical test with the selection of K features based on the statistical result between the features and the target variable. Also, in the RFE method, machine Learning Algorithms are used to remove the least important features after recursive training, so that finally the number of features reaches the set number (17 features in this article). The performance results of the models based on the selected features are shown in Table 6. The most effective characteristics are "age", "daily rate", "over time", "NumCompaniesWorked" and, "monthly income".

The study of groundwater levels is of paramount importance due to its critical role in water resource management, agriculture, and ecosystem sustainability. This study uses machine Learning Algorithms to predict groundwater levels in observation wells across Tehran. A range of input parameters, including satellite-derived data from GRACE, GLDAS, and ERA5, were employed to train models for estimating groundwater level fluctuations. The primary aim was to evaluate and compare the performance of 12 different machine Learning models, including Random Forest, AdaBoost, Support Vector Machine, and Artificial Neural Networks, among others, in terms of their prediction accuracy. The results indicated that Ensemble-based models generally outperformed individual Algorithms, achieving the highest coefficients of determination (R²) and the lowest error metrics. Spatial analysis of the errors revealed that the northern part of the study area experienced higher prediction errors than the southern region, likely due to more significant groundwater level fluctuations, influenced by regional climatic conditions and topography. Furthermore, the study demonstrated that combining various input parameters, such as terrestrial water storage, total soil moisture, and precipitation, improved the accuracy of the groundwater level predictions. The models were evaluated using standard error metrics, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Pearson Correlation Coefficient (R), with results showing strong agreement between predicted and observed data. The findings suggest that machine Learning models, especially those leveraging high-resolution satellite and reanalysis data, can be highly effective for groundwater level prediction and management in regions with limited in-situ measurement data. This study provides valuable insights into the application of machine Learning for groundwater monitoring, with promising results for future implementation in water resource management.

Considering the importance of dynamic security assessment and the necessity of implementing control measures after a disturbance, online dynamic security assessment has replaced offline assessment. Methods which are commonly used in dynamic security evaluation are not suitable for serious events which have a high rate of occurrence. Therefore, it is essential to perform real time transient stability assessment in order to increase operators opportunity to take remedial actions. For this purpose, in this study, Ensemble machine Learning methods have been used to evaluate online dynamic security. The investigated problem is a multi-class classification that deals with classifying of system’s dynamic security status. The proposed method has been evaluated on two standard systems. Also, a comparison has been made between the proposed Ensemble Learning methods and individual Algorithms. The results indicate that the proposed method, not only accurate but also has good performance in evaluating online dynamic security.

This paper discusses the problems of short-term forecasting of cryptocurrency time series using a supervised machine Learning (ML) approach. For this goal, we applied two of the most powerful Ensemble methods including Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As the dataset was collected from daily close prices of three of the most capitalized coins: Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP), and as features we used  past price information and technical indicators (moving average). To check the effectiveness of these models we made an out-of-sample forecast for selected time series by using the one step ahead technique. The accuracy rate of the forecasted prices by using RF and GBM were calculated. The results verify the applicability of the ML Ensembles approach for the forecasting of cryptocurrency prices. The out of sample accuracy of short-term prediction daily close prices obtained by the SGBM and RF in terms of Mean Absolut Percentage Error (MAPE) for the three most capitalized cryptocurrencies (BTC, ETH, and XRP) were within 0.92-2.61 %.

Atrial fibrillation (AF) is a life threatening disease and can cause stroke, heart failure, and sometimes death. To reduce the rate of mortality and morbidity due to increased prevalence of AF, early detection of the same becomes a prior concern. Traditional machine Learning (TML) Algorithms and Ensemble machine Learning (EML) Algorithms are proposed to detect AF in this article. The performances of both these methods are compared in this study. Methodology involves computation of RR interval features extracted from electrocardiogram and its classification into: normal, AF, and other rhythms. TML techniques such as Classification and Regression Tree, K Nearest Neighbor, C4. 5, Iterative Dichotomiser 3, Support Vector Machine and EML classifier such as Random Forest (RF), and Rotation Forest are used for classification. The proposed method is evaluated using PhysioNet challenge 2017. During the tenfold cross validation, it is observed that RF classifier provided good classification accuracy of 99. 10% with area under the curve of 0. 998. Apart from contributing a new methodology, the proposed study also experimentally proves higher performance with Ensemble Learning method, RF. The methodology has many applications in health care management systems including defibrillators, cardiac pacemakers, etc.

Crowdfunding is a new technology-enabled innovative process that is changing the capital market space. Internet-based applications, particularly those related to Web 2. 0, have had a significant impact on sectors of society such as education, business, and medicine. The goal of this research is to fill a gap in the literature on mathematical modelling and prediction of Ensemble Learning in order to evaluate crowdfunding projects. The Mathematical model determines the cost of funding for the entrepreneur and the return investors will receive per period. A correct financial model is essential in order to keep all three stakeholders involved in the long term. The results show the designed model improved performance in predicting the evaluation of success or failure of Crowdfunding projects.

Data clustering is one of the main steps in data mining, which is responsible for exploring hidden patterns in non-tagged data. Due to the complexity of the problem and the weakness of the basic clustering methods, most studies today are guided by clustering Ensemble methods. Diversity in primary results is one of the most important factors that can affect the quality of the final results. Also, the quality of the initial results is another factor that affects the quality of the results of the Ensemble. Both factors have been considered in recent research on Ensemble clustering. Here, a new framework for improving the efficiency of clustering has been proposed, which is based on the use of a subset of primary clusters, and the proposed method answers the above questions and ambiguities. The selection of this subset plays a vital role in the efficiency of the assembly. Since evolutionary intelligent Algorithms have been able to solve the majority of complex engineering problems, this paper also uses these intelligent methods to select subsets of primary clusters. This selection is done using three intelligent methods (genetic algorithm, simulation annealing and particle swarm optimization). In this paper a clustering Ensemble method is proposed which is based on a subset of primary clusters. The main idea behind this method is using more stable clusters in the Ensemble. The stability is applied as a goodness measure of the clusters. The clusters which satisfy a threshold of this measure are selected to participate in the Ensemble. For combining the chosen clusters, a co-association based consensus function is applied. A new EAC based method which is called Extended Evidence Accumulation Clustering, EEAC, is proposed for constructing the Co-association Matrix from the subset of clusters. Experimental results on several standard datasets with normalized mutual information evaluation, Fisher and accuracy criteria compared to Alizadeh, Azimi, Berikov, CLWGC, RCESCC, KME, CFSFDP, DBSCAB, NSC and Chen methods show the significant improvement of the proposed method in comparison with other ones.