Radial Basis Function Kernel Research Articles

Metaheuristic-enhanced Deep Learning Model for Accurate Alzheimer's Disease Diagnosis from MRI Imaging

Alzheimer’s Disease (AD) is the neuro-degenerative dementia, where the precise and early recognition of AD is vital for timely treatment to reduce mortality rate. A new automated model is implemented in this work for early discovery of AD in the Magnetic Resonance Imaging (MRI) brain scans. Initially, the input brain scans are taken from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Further, the acquired raw brain scans are visually improved by employing the binary normalization technique. The denoised brain scans are fed to the pre-trained Convolutional Neural Network (CNN) named GoogleNet for feature extraction. Next, the extracted richer feature values are fed to the Long Short Term Memory (LSTM) network for classifying the brain scan as Normal Control (NC), Mild Cognitive Impairment (MCI) and AD. In this manuscript, a Honey Badger Optimization Algorithm (HBOA) technique is incorporated with the LSTM networks for hyper-parameters optimization, where this procedure helps in diminishing the LSTM network’s complexity and computational time. The experimental results conducted on the ADNI database underscore the HBOA-based LSTM network's effectiveness, showcasing a remarkable mean classification accuracy of 97.83% in multi-class classification. Moreover, the sensitivity of HBOA based LSTM for AD/NC is 96.73% which is high when compared to the existing methodologies such as SVM with radial basis kernel function and NCSINs. This performance surpasses that of other comparative models for AD detection, emphasizing the superior capabilities and potential of the proposed method in the early detection.

A novel early stage drip irrigation system cost estimation model based on management and environmental variables

One of the most significant, intricate, and little-discussed aspects of pressurized irrigation is cost estimation. This study attempts to model the early-stage cost of the drip irrigation system using a database of 515 projects divided into four sections the cost of the pumping station and central control system (TCP), the cost of on-farm equipment (TCF), the cost of installation and operation on-farm and pumping station (TCI), and the total cost (TCT). First, 39 environmental and management features affecting the cost of the listed sectors were extracted for each of the 515 projects previously mentioned. A database (a matrix of 515 × 43) was created, and the costs of all projects were updated for the baseline year of 2022. Then, several feature selection algorithms, such as WCC, LCA, GA, PSO, ACO, ICA, LA, HTS, FOA, DSOS, and CUK, were employed to choose the most significant features that had the biggest influence on the system cost. The selection of features was carried out for all features (a total of 39 features) as well as for easily available features (those features that existed before the irrigation system’s design phase, 18 features). Then, different machine learning models such as Multivariate Linear Regression, Support Vector Regression, Artificial Neural Networks, Gene Expression Programming, Genetic Algorithms, Deep Learning, and Decision Trees, were used to estimate the costs of each of the of the aforementioned sections. Support vector machine (SVM) and optimization algorithms (Wrapper) were found to be the best learner and feature selection techniques, respectively, out of all the available feature selection algorithms. The two LCA and FOA algorithms produced the best estimation, according to the evaluation criteria results. Their RMSE for all features was 0.0020 and 0.0018, respectively, and their R2 was 0.94 and 0.94. For readily available features, these criteria were 0.0006 and 0.95 for both algorithms. In the part of the overall feature, the early-stage cost modeling with selected features revealed that the SVM model (with RBF Kernel) is the best model among the four cost sections discussed. Its evaluation criteria in the training stage are R2 = 0.923, RMSE = 0.008, and VE = 0.082; in the testing stage, they are R2 = 0.893, RMSE = 0.009, and VE = 0.102. The ANN model (MLP) was found to be the best model for a subset of features in the easily available feature part, with R2 = 0.912, RMSE = 0.008, and VE = 0.083 in the training stage and R2 = 0.882, RMSE = 0.009, and VE = 0.103 in the testing stage. The findings of this study can be utilized to highly accurately estimate the cost of local irrigation systems based on the recognized environmental and management parameters and by employing particular models.

Enhancing internet of things attack detection using principal component analysis and kernel principal component analysis with cosine distance and sigmoid kernel

The widespread adoption of internet of things (IoT) devices has brought about unprecedented levels of connectivity and convenience. However, it has also introduced significant challenges, particularly in the areas of security and privacy. This study addresses the critical issue of intrusion detection within IoT environments, with a specific focus on analyzing the Iot-23 dataset. Our methodology involves employing principal component analysis (PCA) and kernel PCA for dimensionality reduction. Subsequently, we utilize the k-nearest neighbors (KNN) algorithm for classification purposes. To optimize the performance of the KNN algorithm, we experiment with various feature scaling techniques, such as StandardScaler, MinMaxScaler, and RobustScaler, utilizing different distance metrics. In our analysis, we discovered that employing the cosine distance metric in combination with KNN resulted in superior intrusion detection performance when utilizing PCA. Additionally, when utilizing kernel PCA, we evaluated multiple kernel functions and determined that the radial basis function and sigmoid kernel yielded the most favorable results.

Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning

Problems arising from suboptimal production planning can cause inventory management to be less effective and efficient in the company. The lack of integrated presentation of information also causes less efficiency in making decisions. This study aims to obtain the best kernel function forecasting model by predicting ground rod sales using the Support Vector Regression (SVR) method in order to determine the level of forecasting accuracy and the results of ground rod forecasting in the future which are presented in an optimal data visualization. This problem-solving is done with the Support Vector Regression method, which consists of linear kernel functions, polynomial kernel functions, and radial basis function (RBF) kernel functions with the Grid Search Algorithm. Based on the results of the best parameter search that has been done using the grid search algorithm, it can be concluded that the best kernel function forecasting model is a linear kernel function with a value of C = 100 and ε = 10-3. The accuracy of this forecasting model has a MAPE value of training data and testing data of 2.048% and 1.569%, where this value is the smallest MAPE value compared to the MAPE value of the other two functions. After getting the best model, forecasting was carried out within five months, obtaining an average of 6,647 monthly pieces. The results of forecasting and historical sales are reviewed in a visualization of Business Intelligence data so that it is well exposed, where the forecasting shows an increase from every month.

Optimizing Polynomial and Regularization Techniques for Enhanced Housing Price Prediction Accuracy

This study investigates the effectiveness of various regression models for predicting housing prices using the California Housing dataset. The models evaluated include Linear Regression, Ridge Regression, Best Polynomial Ridge Regression, Lasso Regression, Elastic Net Regression, and Support Vector Regression (SVR) with an RBF kernel. The analysis reveals that SVR with an RBF kernel exhibits the poorest performance, characterized by the highest Mean Squared Error (MSE) and the lowest R² score, indicating limited effectiveness for this dataset. Conversely, Linear Regression, Ridge Regression, and Best Polynomial Ridge Regression demonstrate significantly lower MSE values and nearly identical R² scores, each explaining approximately 60% of the variance in housing prices. Among these, Best Polynomial Ridge Regression marginally outperforms Linear and Ridge Regression, suggesting that including polynomial features enhances model performance. Although Lasso Regression shows slightly higher MSE than the leading models, it still performs better than Elastic Net and SVR. Overall, the study identifies linear regression, ridge regression, and best polynomial ridge regression as the most effective models for this dataset, with SVR and an RBF kernel being the least effective.

Fault Diagnosis and Optimized Operation and Maintenance of Drop-out Fuses in Alpine Regions Using MKL-SVM and GRU

Extreme climate in alpine regions leads to frequent failures of drop-out fuses, but existing methods have low detection accuracy and response speed. This article combines MKL-SVM (Multiple Kernel Learning-Support Vector Machine) and GRU (Gated Recurrent Unit) model to achieve precise fault diagnosis of drop-out fuses, leveraging their efficient processing of high-dimensional time series data. Sensors are used to collect data such as voltage, current, temperature, and wind speed. Based on GRU, global trends, temporal hidden states, and dynamic nonlinear features of the data are extracted as inputs for subsequent MKL-SVM. In MKL-SVM, a linear kernel function is used to simplify the linear relationship between static features; targeting temporal features, RBF (Radial Basis Function) kernel effectively captures dynamic trends, fluctuations, and periodic changes in temporal features. To further enhance the classification ability of the model, the kernel function parameters, including linear kernel weights and RBF kernel widths, are adjusted through cross-validation. Finally, based on the model diagnosis results and predicted data, an optimized operation and maintenance plan is formulated to reduce maintenance costs and improve equipment operating efficiency. The results show that the fusion model can significantly improve the reliability of fault detection and adapt to the unique environmental challenges of alpine regions. The accuracy of fault diagnosis for drop-out fuses is 95%, with an MSE (mean square error) value of about 0.15 and a maintenance cost reduction of about 21.89%. This method can achieve accurate fault diagnosis and efficient optimization of operation and maintenance under extreme weather conditions.

Nonlinear Small Sample Data Regression with a New Rational-Quadratic Minkowski Kernel for Tobacco Laser Perforation Process Tar Reduction Estimation.

This paper investigates the nonlinear relationship between tobacco harmful content tar reduction and laser perforation parameters. To find a model to demonstrate the relationship between the laser perforation parameters and the cigarette tar reduction level, an online platform based on Python Streamlit was built to collect and publish related data. After the initial analysis of the collected experimental data, the quadratic nonlinear regression model demonstrates a significant fit to the experimental data. However, although the nonlinear regression has much higher accuracy than the linear regression plane, the prediction normalized root mean squared error (NRMSE) is still high, over 10%, which indicates that the regression relationship is more complex than the simple quadratic function expression. On the other hand, the sample dataset used for modeling is very limited, which restricts its exploration and the development of a model comparable to those built with big data. To address this challenge for small sample size data in modeling this complex nonlinear relationship, a novel rational-quadratic Minkowski (RM)-based kernel was designed. This RM-kernel model acquires higher accuracy than other kernels in both SVM and Gaussian process regression. Furthermore, this new kernel also shows less sensitivity to hyperparameter change, the greater ability to capture complex relationships, and more flexibility than the RBF kernel and RQ kernel. Subsequently, the kernel-based RM regression model was successfully implemented for laser perforation parameter selection, yielding consistent results that align with human sensory test data.

Comparison of Machine Learning Algorithms in Classifying Districts/Cities in Indonesia According to the Human Development Index (HDI) in 2021

The human development index (HDI) is one of the measuring tools for achieving the quality of life of a region or even a country, including Indonesia. There are 3 basic components of the HDI, namely the dimensions of health, knowledge, and decent living. Development in Indonesia is uneven as indicated by the Human Development Index (HDI) of districts/cities in 2021 which varies greatly. The purpose of this study is to compare several machine learning algorithms to classify districts/cities in Indonesia according to the Human Development Index (HDI) in 2021. There are six machine learning algorithms used in this study, namely Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearset Neighbor (K-NN), Random Forest, Decision Tree, and Naive Bayes. The k-Fold Cross Validation method is applied to form the training set and testing set, with 10 folds and 1 repetition. The results of the study showed that the classification results of the SVM algorithm using the Radial Basis Function (RBF) kernel parameters with sigma = 0.4864648 and C = 1 were the best among the other five algorithms with an average accuracy of 76.08% and a maximum accuracy of 88.24%.

MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification.

With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation expense, and increasing the model classification performance is a significant challenge. Therefore, this study proposes a multi-strategy improved black-winged kite algorithm (MSBKA) for feature selection of natural disaster tweets classification based on the wrapper method's principle. Firstly, BKA is improved by utilizing the enhanced Circle mapping, integrating the hierarchical reverse learning, and introducing the Nelder-Mead method. Then, MSBKA is combined with the excellent classifier SVM (RBF kernel function) to construct a hybrid model. Finally, the MSBKA-SVM model performs feature selection and tweet classification tasks. The empirical analysis of the data from four natural disasters shows that the proposed model has achieved an accuracy of 0.8822. Compared with GA, PSO, SSA, and BKA, the accuracy is increased by 4.34%, 2.13%, 2.94%, and 6.35%, respectively. This research proves that the MSBKA-SVM model can play a supporting role in reducing disaster risk.

Nadaraya–Watson kernel smoothing as a random energy model

Abstract Precise asymptotics have revealed many surprises in high-dimensional regression. These advances, however, have not extended to perhaps the simplest estimator: direct Nadaraya–Watson (NW) kernel smoothing. Here, we describe how one can use ideas from the analysis of the random energy model (REM) in statistical physics to compute sharp asymptotics for the NW estimator when the sample size is exponential in the dimension. As a simple starting point for investigation, we focus on the case in which one aims to estimate a single-index target function using a radial basis function kernel on the sphere. Our main result is a pointwise asymptotic for the NW predictor, showing that it re-scales the argument of the true link function. Our work provides a first step towards a detailed understanding of kernel smoothing in high dimensions.

Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms.

The purpose of the current study was to develop and validate a biomarker-based prediction model for metastasis in patients with colorectal cancer (CRC). Two datasets, GSE68468 and GSE41568, were retrieved from the Gene Expression Omnibus (GEO) database. In the GSE68468 dataset, key biomarkers were identified through a screening process involving differential expression analysis, redundancy analysis, and recursive feature elimination technique. Subsequently, the prediction model was developed and internally validated using five machine learning (ML) algorithms including lasso and elastic-net regularized generalized linear model (glmnet), k-nearest neighbors (kNN), support vector machine (SVM) with Radial Basis Function Kernel, random forest (RF), and eXtreme Gradient Boosting (XGBoost). The predictive performance of the algorithm with the highest accuracy was then externally validated on the GSE41568 dataset. Among 22,283 registered genes in the GSE68468 dataset, the screening process identified 16 key genes including MMP3, CCDC102B, CDH2, SCGB1A1, KRT7, CYP1B1, LAMC3, ALB, DIXDC1, VWF, MMP1, CYP4B1, NKX3-2, TMEM158, GADD45B, SERPINA1 and these genes were used to build the prediction model. On the internal validation dataset, the prediction performance of five ML algorithms was as follows; RF (accuracy=0.97 and kappa=0.91), XGBoost (0.93, 0.81), kNN (0.93, 0.81), glmnet (0.93, 0.82) and SVM (0.92, 0.80). Top five biomarkers were MMP3, CCDC102B, CDH2, VWF and MMP1. The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset. The results of this study have identified biomarkers through ML algorithms which help to identify patients with CRC prone to metastasis.

Improving Urban Heat Island Predictions Based on Support Vector Regression and Multi-Sensor Remote Sensing: A Case Study in Malang City

The Urban Heat Island (UHI) phenomenon causes significant temperature increases in urban areas, adversely affecting the environment and public health. This research develops a prediction model of land surface temperature in Malang City using Support Vector Regression (SVR) with remote sensing data from Landsat-8, Sentinel-2, and SRTM. A cloud masking process is applied to improve image quality, while features such as NDVI, NDBI, NDWI, NDMI, elevation, and LST are calculated and normalized. The test results show that the Radial Basis Function (RBF) kernel with hyperparameters C = 10, Epsilon = 0.1, and gamma = 1 provides the best performance, with R² of 0.887, MSE of 1.625, and MAPE of 2.71%. This study shows that SVR with RBF kernel and appropriate tuning parameters can improve prediction accuracy. These results provide a strong basis for the development of more effective prediction models in managing UHI in big cities .

-decay half-life predictions with support vector machine

In this study, we investigate the application of support vector machines utilizing a radial basis function kernel for predicting nuclear -decay half-lives. Our approach integrates a comprehensive set of physics-derived features, including characteristics derived from nuclear structure, to systematically evaluate their impact on predictive accuracy. In addition to traditional parameters such as proton and neutron numbers, as well as terms based on the liquid drop model (e.g., volume, surface, Coulomb features), we incorporate decay energies and orbital angular momentum quantum numbers for both parent and daughter nuclei. Our analysis of 2232 nuclear data points demonstrates that the use of the radial basis function kernel yields predictive models with root mean square errors of 0.819 (for set1) and 0.352 (for set2), aligning with results obtained from comparable machine learning methodologies. Furthermore, Shapley additive explanations values highlight the predominant role of parent nuclei in predicting -decay half-lives within the support vector machines. These results highlight the effectiveness of machine learning in nuclear structure research, opening up new possibilities for predicting the -decay half-lives of previously unstudied nuclei.

Machine learning based parameter-free adaptive EWMA control chart to monitor process dispersion

Conventional control charts track changes in the process by using predefined process parameters. Conversely, during online monitoring, adaptive control charts modify the process parameters. To improve the process dispersion monitoring in various operational environments, this study presents an adaptive exponentially weighted moving average (AEWMA) control chart based on support vector regression (SVR). This study investigates the efficacy of different kernels such as linear, polynomial, and radial basis functions (RBF) within the SVR framework. By adapting the smoothing constant to the shift’s size in process dispersion, the suggested SVR-based AEWMA control chart makes better use of the strengths of the RBF kernel to identify shifts in the process dispersion. To demonstrate the method’s effectiveness, real-life data is used in a practical application, highlighting the adaptability and reliability of the SVR-based AEWMA control chart for monitoring process dispersion. The code and supplementary data set file may be found at (https://github.com/muhammadwaqaskazmi/ARL-SDRL-Codes).

Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition

Obesity is a chronic condition affecting millions worldwide, influenced by genetic predispositions, environmental factors, lifestyle habits, and excessive caloric intake surpassing energy expenditure. widespread prevalence, existing studies lack a comprehensive exploration of classification models that effectively address the complex interplay between lifestyle and physical attributes. This study tackles the absence of an optimal machine learning model for accurately classifying obesity based on these multifaceted factors. To address this gap, the study evaluates the performance of three machine learning algorithms: Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, Naïve Bayes, and K-Nearest Neighbor (KNN). The primary objectives are to identify the most accurate classification approach, analyze the strengths of these algorithms, and highlight the importance of lifestyle and physical attributes in obesity prediction. Experimental findings show that SVM with RBF kernel achieves the highest accuracy at 89%, surpassing the performance of the other models. This study advances the field of obesity classification by offering a detailed comparative analysis of machine learning algorithms and underscoring the critical role of integrating lifestyle and physical factors into predictive modeling.

Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence.

This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability. The methodology comprises several stages, including data collection, preprocessing, feature extraction, and model training. Various machine learning models are implemented and evaluated. Among these, the SVM model with a Radial Basis Function (RBF) kernel outperforms others, achieving an accuracy of 97%, with high precision and recall scores, confirming its robustness in classifying RSSI data and handling complex signal behavior. The confusion matrix further supports the SVM model's accuracy, showing minimal misclassification.

SVM Optimization with Kernel Function for Sentiment Analysis on Social Media twitter (X) in AFC U23 Asian Cup Case Study

This study aims to optimize the performance of the Support Vector Machine (SVM) in sentiment analysis on social media by using various kernel functions, namely linear, polynomial, and Radial Basis Function (RBF). The case study taken was a conversation related to the AFC Asian Cup U-23 taken from social media platforms. The data used in this study included three classes of sentiment: positive, neutral, and negative. The experimental results show that the linear kernel achieves the highest accuracy of 93.55% with an F1-score of 0.9296. The RBF kernel shows almost comparable performance with an accuracy of 90.05% and an F1-score of 0.8820. In contrast, the polynomial kernel showed lower performance with an accuracy of 80.65% and an F1-score of 0.7346. The results of the analysis using the confusion matrix show that linear kernels and RBF are more effective in classifying neutral and positive sentiment than polynomial kernels. This study confirms that the right selection of kernels in SVM greatly affects the accuracy and effectiveness of sentiment analysis. Linear kernels and RBFs have proven superior in handling complex sentiment analysis datasets, such as those related to the AFC U-23 Asian Cup. These findings can be used as a basis for further development in sentiment analysis applications across various domains.

Robot teleoperation assisted by composite virtual fixtures in dynamic environment

Purpose This study aims to propose a virtual fixture (VF) – assisted robot teleoperation framework that modulates the generated trajectory from demonstrations to response varying obstacles in complex environment. Design/methodology/approach First, a single trajectory is learned from demonstration using dynamic movement primitives (DMP). Then, the classic DMP is improved by integrating adaptive terms and updating the radial basis function kernel weights, allowing the single-skill trajectory to alter to respond to obstacles dynamically. Finally, composite virtual forces are generated on the haptic device to enhance operational stability and prevents unintentional operations that could bring the robot into close proximity with obstacles. Findings The VF method can accomplish online obstacle avoidance and operation guidance. The experimental results show that the operation trajectory based on VFs is smoother compared to the operation without assistance. Moreover, the operational speed has increased by up to 44.37% compared to the demonstrations. Originality/value The proposed composite VF-based protection framework solves the problem that classical DMP method cannot dynamically avoid obstacles, and effectively improves the operational safety and efficiency.

The Effect of Physical Activity on Combined Cadmium, Lead, and Mercury Exposure.

Background/Objective: Environmental exposures, such as heavy metals, can significantly affect physical activity, an important determinant of health. This study explores the effect of physical activity on combined exposure to cadmium, lead, and mercury (metals), using data from the 2013-2014 National Health and Nutrition Examination Survey (NHANES). Methods: Physical activity was measured with ActiGraph GT3X+ devices worn continuously for 7 days, while blood samples were analyzed for metal content using inductively coupled plasma mass spectrometry. Descriptive statistics and multivariable linear regression were used to assess the impact of multi-metal exposure on physical activity. Additionally, Bayesian Kernel Machine Regression (BKMR) was applied to explore nonlinear and interactive effects of metal exposures on physical activity. Using a Gaussian process with a radial basis function kernel, BKMR estimates posterior distributions via Markov Chain Monte Carlo (MCMC) sampling, allowing for robust evaluation of individual and combined exposure-response relationships. Posterior Inclusion Probabilities (PIPs) were calculated to quantify the relative importance of each metal. Results: The linear regression analysis revealed positive associations between cadmium and lead exposure and physical activity. BKMR analysis, particularly the PIP, identified lead as the most influential metal in predicting physical activity, followed by cadmium and mercury. These PIP values provide a probabilistic measure of each metal's importance, offering deeper insights into their relative contributions to the overall exposure effect. The study also uncovered complex relationships between metal exposures and physical activity. In univariate BKMR exposure-response analysis, lead and cadmium generally showed positive associations with physical activity, while mercury exhibited a slightly negative relationship. Bivariate exposure-response analysis further illustrated how the impact of one metal could be influenced by the presence and levels of another, confirming the trends observed in univariate analyses while also demonstrating the complexity varying doses of two metals can have on either increased or decreased physical activity. Additionally, the overall exposure effect analysis across different quantiles revealed that higher levels of combined metal exposures were associated with increased physical activity, though there was greater uncertainty at higher exposure levels as the 95% credible intervals were wider. Conclusions: Overall, this study fills a critical gap by investigating the interactive and combined effects of multiple metals on physical activity. The findings underscore the necessity of using advanced methods such as BKMR to capture the complex dynamics of environmental exposures and their impact on human behavior and health outcomes.

Machine learning model for dissolved gas analysis: methodological review with a case in Hong Kong

Dissolved gas analysis is a valuable diagnostic tool used to monitor transformer health by analysing the gases dissolved in insulation oil. However, its practical application is hindered by the absence of a universal standard, leading to varied interpretations and implementations across different contexts. Scholars have turned to machine learning to advance DGA anomaly detection, but the existing literature prioritises model development over methodological rigour; issues such as dataset imbalance, appropriate evaluation metrics, and testing and validation procedures are often overlooked. This study addresses the existing gaps by critically reviewing the methodological steps involved in machine learning modelling with DGA datasets. The proposed design considerations are justified, and the analysis of the IEC TC 10 dataset and a dataset from a Hong Kong company is enhanced. Using Python’s scikit-learn, over 18 combinations of datasets, data preprocessing techniques, and model architectures were assessed, and the random forest model (F1 = 0.88) and the support vector classifier with an RBF kernel (F1 = 0.87) were identified as the top-performing models, after applying Yeo-Johnson transformation. The models’ source code is available on the author’s GitHub repository.