Salary Estimator using Big Data

Cancer survival classification using integrated data sets and intermediate information.

Technological developments are things that must be followed by companies to achieve a competitive advantage to improve performance. To achieve and improve performance, companies need active employee engagement by encouraging motivation and fulfilling their job satisfaction. This study aims to analyze the effect of motivation and job satisfaction on performance with employee engagement as a mediating variable. The research sample is Information Technology (IT) companies located in the cities of Jakarta and Bandung, Indonesia. Research respondents are system developers who handle system development activities for a project or part of an ongoing project. By using the convenience sampling technique 103 responses were obtained from IT developers. The research model analysis method uses Partial Least Square (PLS) with SMART PLS Ver 3.0 software. Empirical findings prove that motivation has a positive effect on the performance of IT employees, while job satisfaction is independent. Employee engagement does not directly affect employee performance, but the effect of mediation through motivation and job satisfaction can have a significant effect on employee performance. The research findings have managerial implications, in increasing high employee involvement, motivation needs to be encouraged to be more active and innovative, and facilitate the achievement of the desired results. AcknowledgmentThis study was made possible because of the full support of the Region III Education Service Institute (LL-DIKTI III), the Ministry of Education and Culture of the Republic of Indonesia, and the Research Center at Mercu Buana University, Jakarta.

E-banking services are replacing traditional services and creating a new scale in transformation. The different e-channels like ATMs, Credit & Debit Cards, Tele-banking, Mobile-banking, Online-banking, Smart Cards etc., are changing the face of the Indian banks. In the post-reforms period, Indian banking is passing through crucial stages. There is a paradigm shift in different parameters of transformation. Banks are on the verge of major changes,and the single largest force behind this transformation is Information Technology (IT). IT further intensifies competition, ensures better efficiency of operations, risk management etc. The first and foremost set of application of IT that could benefit from technology advances, relates to payment system. Due to the technology advancements, efficiency of the banks has improved especially with respect to quality, timely delivery of services at affordable cost. In a study it is examined that retail banking and ATMs are improving the efficiency of banks. (Justin Paul 2006).

This study evaluates the impact of three Feature Selection (FS) algorithms in an Object Based Image Analysis (OBIA) framework for Very-High-Resolution (VHR) Land Use-Land Cover (LULC) classification. The three selected FS algorithms, Correlation Based Selection (CFS), Mean Decrease in Accuracy (MDA) and Random Forest (RF) based Recursive Feature Elimination (RFE), were tested on Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest (RF) classifiers. The results demonstrate that the accuracy of SVM and KNN classifiers are the most sensitive to FS. The RF appeared to be more robust to high dimensionality, although a significant increase in accuracy was found by using the RFE method. In terms of classification accuracy, SVM performed the best using FS, followed by RF and KNN. Finally, only a small number of features is needed to achieve the highest performance using each classifier. This study emphasizes the benefits of rigorous FS for maximizing performance, as well as for minimizing model complexity and interpretation.

Heart disease is still one of the top causes of death globally, demanding early and effective prediction tools. Traditional predictive models frequently have limitations because they rely on single‐source statistics, which may not represent the entire scenario of a patient's complex health profile. Feature selection is an essential preprocessing step in machine learning that improves model efficiency by eliminating irrelevant or redundant features. This study evaluates the impact of feature selection on six classification modelsNaïve Bayes, Logistic Regression, Decision Tree, K‐Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forestacross four datasets. Performance was assessed using accuracy, precision, recall, F1‐score, and area under the curve (AUC) before and after feature selection. The results indicate that feature selection had varying effects across different models and datasets. In Dataset‐01, Random Forest maintained its strong performance (98.70% accuracy) even after feature selection, while SVM and KNN saw noticeable drops, with accuracy decreasing from 88.96% to 79.87% and 87.01% to 81.17% respectively. For Dataset‐02, SVM showed improvement, increasing from 80.00% to 85.00%, while Naïve Bayes and KNN also improved, reaching 82.50% and 82.50% accuracy, respectively. In Dataset‐03, Logistic Regression and KNN experienced declines, with accuracy dropping from 88.76% to 83.71% and 91.57% to 87.08%, respectively, while Random Forest fell from 97.75% to 91.57%. Dataset‐04 showed mixed results, with Logistic Regression improving from 86.67% to 91.11%, but KNN and Random Forest dropping from 97.78% to 84.44% and 91.11% to 82.22%, respectively. These findings suggest that while feature selection enhances performance for some models, it can negatively impact others, particularly those reliant on a larger feature set. Future research should explore adaptive feature selection techniques to optimize model performance across diverse datasets.

Gene identification has been an increasingly important task due to developments of Human Genome Project. Splice site prediction lies at the heart of identifying human genes, thus development of new methods which detect the splice site accurately is crucial. Machine learning classifiers are utilized to detect the splice sites. Performance of those classifiers mainly depends on DNA encoding methods (feature extraction) and feature selection. The feature extraction methods try to capture as much information as the DNA sequences have, while the feature selection methods provide useful biological knowledge by cleaning out the redundant information. According to the literature, Markovian models are popular encoding methods and the support vector machine (SVM) is known as the best algorithm for classification of splice sites. However, random forest (RF) may outperform the SVM in this domain using those Markovian encoding methods. In this study, performance of RF has been investigated as feature selection and classification in splice site domain. We proposed three methods, namely MM1-RF, MM2-RF and MCM-RF by combining RF with first order Markov Model (MM1), second order Markov model (MM2), and Markov Chain Model (MCM). We compared the performance of the RF with the SVM competitively on HS3D and NN269 benchmark datasets. Also, we evaluated the efficiency of the proposed methods with other current state of arts methods such as Reduced MM1-SVM, SVM-B and LVMM2. The experimental results show that the RF outperforms the SVM when the same Markovian encoding methods are used on both donor and acceptor datasets. Furthermore, the RF classifier performs much faster than the SVM classifier in detecting the splice sites.

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

BackgroundMachine learning (ML) has shown great potential in recognizing complex disease patterns and supporting clinical decision-making. Diabetic foot ulcers (DFUs) represent a significant multifactorial medical problem with high incidence and severe outcomes, providing an ideal example for a comprehensive framework that encompasses all essential steps for implementing ML in a clinically relevant fashion.ObjectiveThis paper aims to provide a framework for the proper use of ML algorithms to predict clinical outcomes of multifactorial diseases and their treatments.MethodsThe comparison of ML models was performed on a DFU dataset. The selection of patient characteristics associated with wound healing was based on outcomes of statistical tests, that is, ANOVA and chi-square test, and validated on expert recommendations. Imputation and balancing of patient records were performed with MIDAS (Multiple Imputation with Denoising Autoencoders) Touch and adaptive synthetic sampling, respectively. Logistic regression, support vector machine (SVM), k-nearest neighbors, random forest (RF), extreme gradient boosting (XGBoost), Bayesian additive regression trees, and artificial neural network were trained, cross-validated, and optimized using random sampling on the patient dataset. To evaluate model calibration and clinical utility, calibration curves, Brier scores, and decision curve analysis (DCA) were performed.ResultsThe exploratory dataset consisted of 700 patient records with 199 variables. After dataset cleaning, the variables used for model training included age, smoking status, toe systolic pressure, blood pressure, oxygen saturation, hemoglobin, hemoglobin A1c, estimated glomerular filtration rate, wound location, diabetes type, Texas wound classification, neuropathy, and wound area measurement. The SVM obtained a stable accuracy of 0.853 (95% CI 0.810-0.896) with an area under the receiver operating characteristic curve of 0.922 (95% CI 0.889-0.955). The RF and XGBoost acquired an accuracy of 0.838 (95% CI 0.793-0.883) and 0.815 (95% CI 0.768-0.862), respectively, with areas under the receiver operating characteristic curve of 0.917 (95% CI 0.883-0.951) for RF and 0.889 (95% CI 0.849-0.929) for XGBoost. SVM, RF, and XGBoost were well-calibrated, with average Brier scores around 0.127 (SD 0.013). DCA showed that the SVM provided the highest net clinical benefit across relevant risk thresholds.ConclusionsHandling missing values, feature selection, and addressing class imbalance are critical components of the key steps in developing ML applications for clinical research. Seven models were selected for comparing their predictive power regarding complete wound healing, and each model representing a different branch in ML. In this initial DFU dataset used as an example, the SVM achieved the best performance in predicting clinical outcomes, followed by RF and XGBoost. The model’s calibration and clinical utility were determined through calibration curves, Brier scores, and DCA, demonstrating its potential relevance in clinical decision-making.

Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazelnut oil

Background: Cardiovascular diseases belong to the top three leading causes of mortality in the Philippines with 17.8 % of the total deaths. Lifestyle-related habits such as alcohol consumption, smoking, poor diet and nutrition, high sedentary behavior, overweight, and obesity have been increasingly implicated in the high rates of heart disease among Filipinos leading to a significant burden to the country's healthcare system. The objective of this study was to predict the presence of heart disease using various machine learning algorithms (support vector machine, naïve Bayes, random forest, logistic regression, decision tree, and adaptive boosting) evaluated on an anonymized publicly available cardiovascular disease dataset. Methodology: Various machine learning algorithms were applied on an anonymized publicly available cardiovascular dataset from a machine learning data repository (IEEE Dataport). A web-based application system named Heart Alert was developed based on the best machine learning model that would predict the risk of developing heart disease. An assessment of the effects of different optimization techniques as to the imputation methods (mean, median, mode, and multiple imputation by chained equations) and as to the feature selection method (recursive feature elimination) on the classification performance of the machine learning algorithms was made. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). Results: The support vector machine without imputation and feature selection obtained the highest performance metrics (90.2% accuracy, 87.7% sensitivity, 93.6% specificity, 94.9% precision, 91.2% F1-score and an area under the receiver operating characteristic curve of 0.902) and was used to implement the heart disease prediction system (Heart Alert). Following very closely were random forest with mean or median imputation and logistic regression with mode imputation, all having no feature selection which also performed well. Conclusion: The performance of the best four machine learning models suggests that for this dataset, imputation technique for missing values may or may not be done. Likewise, recursive feature elimination for feature selection may not apply as all variables seem to be important in heart disease prediction. An early accurate diagnosis leading to prompt intervention efforts is very crucial as it improves the patient's quality of life and diminishes the risk of developing cardiac events.

Detection of congestive heart failure from short-term heart rate variability segments using hybrid feature selection approach

Objectives: To develop a machine learning-based model to select the most important features from a high-dimensional dataset to classify the patterns at high accuracy and reduce their dimensionality. Methods: The proposed feature selection method (FSIFC) forms and combines feature clusters incrementally and produces feature subsets each time. The method uses K-means clustering and Mutual Information (MI) to refine the feature selection process iteratively. Initially, two clusters of features are formed using K-means clustering (K=2) by taking features as the basis of clustering instead of taking the patterns (a traditional way). From these two clusters, the features with the highest MI value in each cluster are kept in a feature subset. Classification accuracies (CA) of the feature subset are calculated using three classifiers namely Support Vector Machines (SVM), Random Forest (RF), and k-nearest Neighbor (knn). The process is repeated by incrementing the value of K i.e. number of clusters; until a maximum user-defined value of K is reached. The best value of CA obtained from these trials is recorded and the corresponding feature set is finally accepted. Findings: The proposed method is demonstrated using ten datasets and the results are compared with the existing published results using three classifiers to determine the method's performance. The ten datasets are classified with average CAs of 92.72%, 93.13%, and 91.5%, using the SVM, RF, and K-NN classifiers respectively. The proposed method selects a maximum of thirty features from the datasets. In terms of selecting the most effective and the smallest feature sets, the proposed method outperforms eight other feature selection methods considering CAs. Novelty: The proposed model applies feature reduction using combined feature clustering and filter methods in an incremental way. This provides an improved selection of relevant features while removing those which are irrelevant at different trials. Keywords: Feature selection, High-dimensional datasets, K-means algorithm, Mutual information, Machine learning

In this paper, we demonstrate the need for dimensionality reduction to mitigate model overfitting on the nontrivial problem of gender classification from digital images. In this study we explore four feature selection schemes using Genetic Algorithm, Memetic Algorithms, and Random Forest, which are fed to a nonlinear support vector machine (SVM) for final classification. The performance of the model (feature) selection approaches are evaluated against two distinct datasets of facial images: FG-NET which contains toddlers to seniors and the UIUC-PAL which contains faces of adults up to seniors. This work demonstrates that feature selection can, and does, improve performance of an SVM based gender classification system significantly.

Precise rainfall forecasting is a common challenge across the globe in meteorological predictions. As rainfall forecasting involves rather complex dynamic parameters, an increasing demand for novel approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST) has attracted a wide variety of scientific applications and is extensively adopted in decision support systems. Although there are several weather prediction techniques in the existing literature, identifying significant input for modelling effective rainfall prediction is not addressed in the present mechanisms. Therefore, this investigation has examined the feasibility of using rough set based feature selection and data mining methods, namely Naive Bayes (NB), Bayesian Logistic Regression (BLR), Multi-Layer Perceptron (MLP), J48, Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM), to forecast rainfall. Feature selection or reduction process is a process of identifying a significant feature subset, in which the generated subset must characterize the information system as a complete feature set. This paper introduces a novel rough set based Maximum Frequency Weighted (MFW) feature reduction technique for finding an effective feature subset for modelling an efficient rainfall forecast system. The experimental analysis and the results indicate substantial improvements of prediction models when trained using the selected feature subset. CART and J48 classifiers have achieved an improved accuracy of 83.42% and 89.72%, respectively. From the experimental study, relative humidity2 (a4) and solar radiation (a6) have been identified as the effective parameters for modelling rainfall prediction. ACM CCS (2012) Classification : Computing methodologies → Machine learning → Machine learning algorithms → Feature selection; Information systems → Information retrieval → Retrieval tasks and goals → Clustering and classification; Applied computing → Operations research → Forecasting *To cite this article: S. Mohankumar and V. Balasubramanian, Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique, CIT. Journal of Computing and Information Technology , vol. 24, no. 2, pp. 181–194, 2016.

Heart disease is one of the leading causes of mortality worldwide, emphasizing the need for early and accurate diagnosis. Traditional machine learning (ML) models such as Random Forest (RF) and Support Vector Machines (SVM) have been widely used for heart disease classification. However, these models often lack the ability to extract hierarchical patterns and long-range dependencies present in complex medical data. To address this limitation, we propose a hybrid deep learning framework that integrates XGBoost with Capsule Networks (XGBoost-CapsNet) and Convolutional Neural Networks (CNN) with Transformer Encoders (CNN-TE) to enhance classification performance.The study utilizes a structured dataset containing essential heart disease indicators. Feature selection is performed using XGBoost, which ranks attributes based on importance. The Capsule Network (CapsNet) is then used to preserve spatial relationships and hierarchical dependencies among features. Meanwhile, the CNN-TE model extracts spatial features through convolutional layers and captures long-range dependencies using a Transformer Encoder with multi-head self-attention mechanisms. The models are trained using optimized hyperparameters and evaluated against baseline ML models (Random Forest, SVM, and standalone CNN). Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC are used for comparison.Experimental evaluations demonstrate that the proposed hybrid models significantly outperform traditional approaches. The XGBoost-CapsNet model achieves an accuracy of 98.2%, while CNN-TE reaches 97.4%, both surpassing standalone CNN (95.1%), Random Forest (94.2%), and SVM (91.8%). The AUC-ROC scores further validate the robustness of the models, with XGBoost-CapsNet scoring 0.99 and CNN-TE achieving 0.98. The use of feature selection with XGBoost improves interpretability and computational efficiency, while the hybrid deep learning models enable better feature extraction and classification accuracy.