Supervised Machine Learning Techniques Research Articles

Employee turnover in multinational corporations: a supervised machine learning approach

AbstractThis research explores the potential of supervised machine learning techniques in transforming raw data into strategic knowledge in the context of human resource management. By analyzing a database with over 205 variables and 2,932 observations related to a telco multinational corporation, this study tests the predictive and analytical power of classification decision trees in detecting the determinants of voluntary employee turnover. The results show the determinants of groups of employees who may voluntarily leave the company, highlighting the level of analytical depth of the classification tree. This study contributes to the field of human resource management by highlighting the strategic value of the classification decision tree in identifying the characteristics of groups of employees with a high propensity to voluntarily leave the firm. As practical implication, our study provides an approach that any organization can use to self-assess its own turnover risk and develop tailored retention practices.

Energy parameter modeling in plug-in hybrid electric vehicles using supervised machine learning approaches

In this study, supervised machine learning techniques like random forest (RF), K-nearest neighbour (KNN), multiple linear regression (MLR), and artificial neural networks (ANNs) were used to predict energy parameters in plug-in hybrid electric vehicles. The primary objective of the study is to propose a machine learning model for estimating combined energy consumption for city and highway driving. For the prediction of combined, city, and highway fuel consumption, accuracy is found to range from 3.40 to 6.13, 0.029 to 0.0625, 0.030 to 0.091, and 0.022 to 0.038 for KNN, MLR, RF, and ANN, respectively, in terms of mean square error (MSE). The accuracy of the artificial neural network model is found to be higher than that of the random forest, K-nearest neighbour, and multiple linear regression for the estimations of the selected energy parameters. For the prediction of the combined fuel consumption, the accuracy of the artificial neural network is 183.51, 42.53, and 1.85 times higher than that of the K-nearest neighbour, random forest, and multiple linear regression, respectively. For the prediction of city fuel consumption (C), the accuracy of the ANN is 113.33, 19.75, and 2.06 times greater than that of the KNN, RF, and MLR, respectively. The predicted accuracy of highway fuel consumption (H) is 34.58, 16.62, and 1.17 times higher for ANN than that of the KNN, RF, and MLR, respectively. The accuracy of the artificial neural network is 1.046 times higher in terms of MSE when three hidden layers are used than when two hidden layers are used for the prediction of combined fuel consumption. The study suggests that supervised machine learning approaches like artificial neural networks and random forests can predict or model the energy parameters of plug-in hybrid electric vehicles with a significant level of accuracy.

VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology

In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell’s mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.

Hierarchical SVM for Semantic Segmentation of 3D Point Clouds for Infrastructure Scenes

The incorporation of building information modeling (BIM) has brought about significant advancements in civil engineering, enhancing efficiency and sustainability across project life cycles. The utilization of advanced 3D point cloud technologies such as laser scanning extends the application of BIM, particularly in operations and maintenance, prompting the exploration of automated solutions for labor-intensive point cloud modeling. This paper presents a demonstration of supervised machine learning—specifically, a support vector machine—for the analysis and segmentation of 3D point clouds, which is a pivotal step in 3D modeling. The point cloud semantic segmentation workflow is extensively reviewed to encompass critical elements such as neighborhood selection, feature extraction, and feature selection, leading to the development of an optimized methodology for this process. Diverse strategies are implemented at each phase to enhance the overall workflow and ensure resilient results. The methodology is then evaluated using diverse datasets from infrastructure scenes of bridges and compared with state-of-the-art deep learning models. The findings highlight the effectiveness of supervised machine learning techniques at accurately segmenting 3D point clouds, outperforming deep learning models such as PointNet and PointNet++ with smaller training datasets. Through the implementation of advanced segmentation techniques, there is a partial reduction in the time required for 3D modeling of point clouds, thereby further enhancing the efficiency and effectiveness of the BIM process.

Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs

Geothermal energy is a sustainable energy source that meets the needs of the climate crisis and global warming caused by fossil fuel burning. Geothermal resources are found in complex geological settings, with faults and interconnected networks of fractures acting as pathways for fluid circulation. Identifying faults and fractures is an essential component of exploiting geothermal resources. However, accurately predicting fractures without high-resolution geophysical logs (e.g., image logs) and well-core samples is challenging. Soft computing techniques, such as machine learning, make it possible to map fracture networks at a finer resolution. This study employed four supervised machine learning techniques (multilayer perceptron (MLP), random forests (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR)) to identify fractures in geothermal carbonate reservoirs in the sub-basins of East China. The models were trained and tested on a diverse well-logging dataset collected at the field scale. A comparison of the predicted results revealed that XGBoost with optimized hyperparameters and data division achieved the best performance than RF, MLP, and SVR with RMSE = 0.02 and R2 = 0.92. The Q-learning algorithm outperformed grid search, Bayesian, and ant colony optimizations. The blind well test demonstrates that it is possible to accurately identify fractures by applying machine learning algorithms to standard well logs. In addition, the comparative analysis indicates that XGBoost was able to handle the complex relationship between input parameters (e.g., DTP > RD > DEN > GR > CAL > RS > U > CNL) and fracture in geologically complex geothermal carbonate reservoirs. Furthermore, comparing the XGBoost model with previous studies proved superior in training and testing. This study suggests that XGBoost with Q-learning-based optimized hyperparameters and data division is a suitable algorithm for identifying fractures using well-log data to explore complex geothermal systems in carbonate rocks.Graphical abstract

Improving Cardiovascular Stroke Risk Prediction with Machine Learning

Improving Cardiovascular Stroke Risk Prediction with Machine Learning

Classification of anomalies in photovoltaic systems using supervised machine learning techniques and real data

In a photovoltaic (PV) plant, various types of anomalies can lead to a decrease in energy conversion efficiency. These anomalies represent deviations from the normal behavior of the plant. Understanding the system’s behavior and identifying deviations from normality are crucial for implementing preventive and corrective measures to ensure the expected economic return on investment in the plant. One effective approach for anomaly detection and classification is the utilization of Supervised Machine Learning Techniques (SMLT). However, this approach comes with several challenges, including the classification of the training dataset, temporal variations, uncertainties, model interpretability, data scarcity, and generalization of the model to different systems and locations, among others. In this way, this study aims to explore the application of SMLT in PV systems and compare different methods. To achieve this, a methodology was developed to create a training and testing synthetic dataset based on real irradiance data, followed by a new process flow for ensemble SMLT. Finally, the new process flow for ensemble SMLT was tested on a real PV plant and synthetic dataset. The methodology for the proposed algorithm was an ensemble of Random Forest with K-nearest neighbors (k-NN) and an inference machine for specific classes. The results indicate that the algorithm successfully classified anomalies, achieving an AUC of 0.9815 for the synthetic dataset and an AUC of 0.9861 for the real dataset, as well as an accuracy of 0.9647 for the real dataset. It is evident that SMLT can serve as valuable tools for detecting and addressing issues, particularly due to the abundance of data and the diverse characteristics inherent to PV plants.

CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING

The term ―chronic kidney disease‖ means lasting damage to the kidneys that can get worse over time. If the damage is very bad, your kidneys may stop working. This is called kidney failure, or end-stage Kidney disease (ESRD). Kidney disease patients have the potential to get into the chronic phase and chronic kidney disease (CKD) is a decrease in kidney function gradually. So, doctors can diagnose kidney disease patients. So, predicting whether patients with Kidney disease have entered a phase of chronic kidney disease or not by showing the best accuracy result of comparing supervised classification machine learning algorithms. The aim is to investigate machine learning- based techniques for CKD forecasting by predicting results in the best accuracy. The analysis of the dataset by supervised machine learning technique (SMLT) to capture several information like, variable identification, univariate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparation, and data visualization will be done on the entire given dataset. Additionally, to compare and discuss the performance of various machine learning algorithms from the given hospital dataset with an evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with the best accuracy with precision, Recall and F1 Score. Key Words: Chronic kidney, Damage, Univariate, bi-variate, Multi-variate.

An Early Predictive Model for the Onset of Knees Osteoarthritis in Nigeria

The primary risk factors for patients with Knee Osteoarthritis (KOA) were determined in this study, and a predictive model was developed using the data found. In order to comprehend the body of information regarding musculoskeletal-related diseases, a thorough study of relevant literature was conducted. One ailment that falls within the musculoskeletal category is knee osteoarthritis, and the risk factors were extracted and confirmed by medical professionals. clinical data encompassing characteristics tracked during KOA patients' treatment were gathered from Ile-Ife, Osun State, Nigeria at the OAU Teaching Hospital Complex (OAUTHC), , as well as from a few other people Utilizing questionnaires, . For this investigation, the entire dataset comprising data on 83 patients was used. WEKA software was used to compare four supervised machine learning techniques so as to create the model. The accuracy of the was 97.59% when examining the 36 originally identified attributes without selecting any featue. The outcomes additionally demonstrated The minimal amount of variables pertinent to the osteoarthritis condition of the knee. Subsequent findings demonstrated the relevance of each feature found in order to create a prognosis model for knee osteoarthritis that is both effective and efficient. Age is the most important factor for KOA, according to the study's findings, and all 36 characteristics were found to be useful in forecasting the likelihood of Knee Osteoarthritis..

Real And Fake Job Post Detection Using Machine Learning

The transition to online platforms has streamlined processes, reducing manual efforts. Job postings, now predominantly online, offer companies a broader reach for talent acquisition. However, amidst legitimate postings, fraudulent ones exist. This study aims to distinguish between real and fake job postings using machine learning techniques with optimal accuracy. Employing various data mining methods and classification algorithms—Logistic Regression, KNN, Decision Tree, XGBoost, Support Vector, Random Forest, and Multilayer Perceptron—we predict job authenticity. Supervised machine learning techniques guide dataset analysis, including variable identification and univariate, bivariate, and multivariate analyses, alongside handling missing values. Comprehensive data validation, cleaning, preparation, and visualization are conducted. Our experimentation, utilizing the Employment Scam Aegean Dataset (EMSCAD) with 17,881 samples, informs our approach. Ultimately, this research aims to enhance job seekers' security by effectively identifying fraudulent job postings. remove the writing issues. Key Words: Logistic Regression, KNN, Decision Tree, XGBoost, Support Vector, Random Forest, and Multilayer Perceptron.

Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques

Predicting stock trends with precision in the ever-evolving financial markets continues to be a formidable challenge. This research investigates an innovative approach that amalgamates the capabilities of BERT (Bidirectional Encoder Representations from Transformers) for sentiment classification (Pang et al., 2002; ?) with supervised machine learning techniques to elevate the accuracy of stock trend prediction. By harnessing the natural language processing process of BERT and its capacity to understand context and sentiment in textual data, coupled with established machine learning methodologies, we aim to provide a robust solution to the intricacies of stock market prediction. By leveraging BERT's natural language processing capabilities, we extract sentiment features from financial news articles. These sentiment scores, combined with traditional financial indicators, form a comprehensive set of features for our predictive model. We aggregate daily net sentiment, among other metrics, and demonstrate its statistically significant predictive efficacy concerning subsequent movements in the stock market. We employed a machine learning model to establish a quantitative relationship between the aggregation of daily net sentiment and trends in stock market movements. Which improved the state-of-the-art performance by 15 percentage points. This research contributes to the ongoing effort to improve stock trend prediction methods, ultimately aiding market participants in making informed investment choices.

Determinants of PTA design: Insights from machine learning

Preferential trade agreements (PTAs) have emerged as the dominant form of international trade governance. Provisions included in PTAs are increasingly numerous, broad in their purview, deep in their scope, and varied between agreements. We study the economic, political, and geographic determinants of PTA design differences. For each of the hundreds of classified PTA provisions, we consider 287 country-pair characteristics as potential determinants, covering many individual mechanisms the literature has studied. We employ random forests, a supervised machine learning technique, to handle this high dimensionality and complexity. We use a robust variable importance measure to identify the most critical determinants of the inclusion of each PTA provision. Contagion due to competition for export markets, geographic proximity, and governance quality emerge as essential determinants of PTA design. These results motivate future exploration of individual mechanisms our exercise points to.

Predictive modeling for breast cancer classification in the context of Bangladeshi patients by use of machine learning approach with explainable AI

Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model’s predictions and understand the impact of each feature on the model’s output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.

Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review.

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post-operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most-widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions.

Enhancing ground classification models for TBM tunneling: Detecting label errors in datasets

Tunnel Boring Machine (TBM) construction, particularly with closed-face TBMs, faces uncertainties due to the inability of the operator to directly observe the ground ahead. These uncertainties can lead to time delays, cost overruns, and accidents. While supervised machine learning techniques have been used to predict geology from TBM sensor data, their performance drops significantly when applied to other projects, indicating poor generalization. To ensure accurate results and improved generalization to future data, supervised learning models require high-quality, well-labeled data which is not usually the case for TBM datasets. This paper addresses the issue of “noisy” labels in TBM datasets, which human operators and engineers often label with varying interpretations. A data-centric framework was adapted and applied to an Earth Pressure Balance Machines (EPBM) tunnel dataset to detect and identify these mislabeled datapoints. The framework's outputs were validated using two techniques and apply several methods to clean the dataset. The best-performing method was selected for the test set. The paper concludes by discussing the limitations of the proposed method, the challenges encountered, and future research directions in this area.

Prediction of COVID-19 By Applying Supervised Machine Learning Techniques

In the era of modern lifestyle, humans going to modernize everything with innovative brains finding the solution for the latest pandemic of this century which is COVID-19. The diagnosis of COVID-19 is a critical and slow process, to tackle this we decided to diagnose COVID-19 quickly by using machine learning (ML) due to its prediction for time-saving. Therefore, in this research, a patient admitted to the intensive care unit (ICU) has vital signs such as heart rate, oxygen saturation (SPO2), blood pressure, and respiratory rate using wearable sensors which are the main vital signs of COVID-19. the predictive models using machine learning algorithms support vector machine (SVM), random forest (RF), and bagging tree (BG) for more accurate predictions. The proposed prediction model results used dataset, and models trained with Support Vector Machine in AUC= 0.9.1, ROC = 0.90 curve, Random Forest AUC = 0.920, and ROC = 0.91, and Bagging Tree AUC = 0.913, and ROC = 0.91 which shows the prediction accuracy. This research indicates machine learning's capability to predict COVID19 quickly instead of conventional methods that have previously been adopted in pandemics such as influenza.

Refining risk prediction in pediatric acute lymphoblastic leukemia through DNA methylation profiling

Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children, and despite considerable progress in treatment outcomes, relapses still pose significant risks of mortality and long-term complications. To address this challenge, we employed a supervised machine learning technique, specifically random survival forests, to predict the risk of relapse and mortality using array-based DNA methylation data from a cohort of 763 pediatric ALL patients treated in Nordic countries. The relapse risk predictor (RRP) was constructed based on 16 CpG sites, demonstrating c-indexes of 0.667 and 0.677 in the training and test sets, respectively. The mortality risk predictor (MRP), comprising 53 CpG sites, exhibited c-indexes of 0.751 and 0.754 in the training and test sets, respectively. To validate the prognostic value of the predictors, we further analyzed two independent cohorts of Canadian (n = 42) and Nordic (n = 384) ALL patients. The external validation confirmed our findings, with the RRP achieving a c-index of 0.667 in the Canadian cohort, and the RRP and MRP achieving c-indexes of 0.529 and 0.621, respectively, in an independent Nordic cohort. The precision of the RRP and MRP models improved when incorporating traditional risk group data, underscoring the potential for synergistic integration of clinical prognostic factors. The MRP model also enabled the definition of a risk group with high rates of relapse and mortality. Our results demonstrate the potential of DNA methylation as a prognostic factor and a tool to refine risk stratification in pediatric ALL. This may lead to personalized treatment strategies based on epigenetic profiling.

Optimizing adhesive rheology for stencil printing of fuel cell sealings using supervised machine learning

In a single low-temperature proton exchange membrane fuel cell (LT-PEMFC) stack, the sealings are responsible for preventing leakages, providing electrical insulation and compensating assembly tolerances. This study investigates stencil printing to produce fuel cell sealings and aims to identify reliable conditions to print layer thicknesses of at least 500 μm, typically used for this application and substantially exceeding the current state-of-the-art. First, rheological and print experiments were conducted with 25 distinct adhesives, including ultraviolet (UV) curable acrylics and epoxies, to determine their printability. An adhesive was considered printable when it detached from the stencil without forming bubbles, a print defect that significantly diminishes the process stability. This had to be accomplished at two different squeegee speeds (40 and 160 mm/s), confirming the robustness of the adhesive for the print process. In addition, the impact of the aperture orientation, aperture aspect ratio (AR) and separation speed of the substrate from the stencil were evaluated. Here, it was detected that a multivariable criterion was necessary to reliably predict printability. Thus, classification models based on binary logistic regression, a simple supervised machine learning technique, were proposed using the thixotropy index TI (indicator for viscosity recovery ability), steady-state viscosity at 100 s−1, adhesive surface tension and aperture AR as predictors. The derived models achieved a prediction accuracy ≥88.2%, demonstrating adequate generalisation. Moreover, it was identified that increasing TI is the most effective approach to achieve printability. Finally, the driving physical mechanisms influencing the obtained print results were assessed, and it was shown that a TI value of about 50 Pa s/s is required to reach the minimum layer thickness.

Stress Detection of Autistic Adults during Simulated Job Interviews Using a Novel Physiological Dataset and Machine Learning

The interview process has been identified as one of the major barriers to employment of autistic individuals, which contributes to the staggering rate of under and unemployment of autistic adults. Decreasing stress during the interview has been shown to improve interview performance. However, in order to effectively provide insights on stress to both interviewees and interviewers, it is necessary to first effectively measure stress. This work explores physiological stress detection through wearable sensing as a means of obtaining quantitative stress measures from young autistic adults undergoing a virtual simulated interview using supervised machine learning techniques. Several supervised learning models were explored and it was found that Elastic Net Regression had the best accuracy with individual models with an accuracy of 84.8% while Support Vector Regression models evaluated with leave-one-out cross validation had a group accuracy of 75.4%. The predictions from the stress model were used with data visualization techniques in order to provide insights on the interview process from both a group and individual viewpoint, showing that stress trends can be found and evaluated using the stress model. This work also addresses a major gap in physiological stress detection literature by presenting a novel dataset of physiological data and ground truth labels for 15 autistic young adults undergoing a simulated interview.

Modelling Stock Prices of Energy Sector using Supervised Machine Learning Techniques

This paper aims at comparing the performance of the different state-of-the-art machine learning techniques in anticipating the performance of stock prices of the energy sector. The data collected cover the period from January 2020 to February 2023 with a daily frequency for the three most imported refined petroleum products in Morocco and trained four regression machines learning (linear regression, lasso regression, ridge regression, and SVR) and four classifiers machine learning (logistic regression, decision tree, extra tree and Random Forest) so that anticipating one day ahead prices direction can take place no matter whether they are negative or positive prices. The performance of regression algorithm is then evaluated using different evaluation metrics, especially MSE, RMSE, MAE, MAPE and R2 to evaluate the performance of regression algorithm while precision, recall and F1 scores are used to evaluate the performance of classifiers algorithm. The outcomes propose that the performance of linear regression and ridge regression takes place equally and outperform other single regression that is lasso regression and SVR for-one-day predictions as a whole. In addition to that, we have come to find that in the classifiers, algorithms group all machine learning display similar predictive accuracy, this is on one hand. On the other hand, the best of them is the logistic regression. In brief, this study suggests that all performance metrics are significantly improved by ensemble learning. Therefore, this study proves that critical information affecting stock movement can be captured by utilizing historical transactions.