Support Vector Machine Learning Algorithm Research Articles

Developing a Dataset of Audio Features to Classify Emotions in Speech

Emotion recognition in speech has gained increasing relevance in recent years, enabling more personalized interactions between users and automated systems. This paper presents the development of a dataset of features obtained from RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) to classify emotions in speech. The paper highlights audio processing techniques such as silence removal and framing to extract features from the recordings. The features are extracted from the audio signals using spectral techniques, time-domain analysis, and the discrete wavelet transform. The resulting dataset is used to train a neural network and the support vector machine learning algorithm. Cross-validation is employed for model training. The developed models were optimized using a software package that performs hyperparameter tuning to improve results. Finally, the emotional classification outcomes were compared. The results showed an emotion classification accuracy of 0.654 for the perceptron neural network and 0.724 for the support vector machine algorithm, demonstrating satisfactory performance in emotion classification.

Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis.

Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presence of synthetic chemical substances in aquatic systems has emerged as a growing concern. Moreover, limited machine-learning (ML) approaches exist for analyzing environmental data, and the increasing complexity of ML models has made it challenging to understand predictor-outcome relationships. In particular, understanding complex interactions among multiple variables remains challenging. This study applies and integrates explainable ML techniques and network analysis to identify the sources of micropollutants in a large watershed and determine the factors affecting micropollutant levels. We assessed the performance of four ML algorithms-support vector machine, random forest, extreme gradient boosting (XGB), and autoencoder-XGB-in predicting micropollutant levels based on the spatial characteristics of the watershed. We applied the synthetic minority oversampling technique to address the data imbalance. The XGB model demonstrated superior predictive performance, particularly for high concentration levels, achieving an accuracy of 87%-99%. Shapley additive explanations (SHAP) analysis identified temperature and rainfall as significant factors. Moreover, agricultural activities contributed to pesticide pollution, whereas urban activities contributed to pharmaceutical contamination. The network analysis corroborated the SHAP findings and revealed event-specific contamination characteristics. This included distinct discharge pathways during a dry summer event and shared pathways during a wet winter event. This approach enhances an understanding of contamination sources and pathways and subsequently aids in developing control measures and making informed policy decisions to preserve water quality in mixed land-use areas.

Improvement of rock surface roughness accuracy by combining object space resolution error and 3D point cloud features

To enhance the accuracy of joint roughness coefficient (JRC) estimation in photogrammetry, this study employed a fixed-camera shooting strategy guided by a Structure-from-Motion-based shooting parameter selection algorithm to reconstruct 3D models of rock samples at 16 different shooting distances. The analysis at profile intervals of 0.25 mm, 0.5 mm, and 1 mm revealed a strong correlation between JRC accuracy and three parameters: object space resolution error, spatial distance between point cloud points, and spatial errors of checkpoints on the orientation board. Using these three parameters as input variables and JRC error as the output variable, five machine learning algorithms—Support Vector Regression, Gaussian Process Regression, Multilayer Perceptron, XGBoost, and CatBoost—were employed to predict JRC errors across different shooting distances. The Multilayer Perceptron model performed best at profile intervals of 0.25 mm and 0.5 mm, while XGBoost was optimal at the 1 mm interval. Under the predictions of these models, JRC accuracy improved by an average of 84.7% across the three intervals. Finally, the applicability and limitations of the proposed method were further discussed.

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality of life. Early identification of at-risk individuals and timely intervention are essential. Nevertheless, current CF risk prediction models fall short in accuracy to adequately fulfill clinical requirements. This study aimed to examine the determinants of CF in elderly patients undergoing MHD and to develop a risk prediction model through machine learning algorithms. The objective is to furnish healthcare professionals with an early prediction tool and to offer insights for personalized CF risk management. A convenience sampling method was employed to select 1,075 elderly MHD patients from various tertiary-level hospitals in Chengdu between October 2023 and March 2024 as the modeling set, and 269 elderly MHD patients from hospitals in Chengdu, Yibin, and Zigong between September 2024 and October 2024 as the external validation set.CF was assessed using the Fried Phenotypic Scale for Frailty (FP) and the Montreal Cognitive Assessment Scale (MOCA). Data on patients’ demographics, sleep, nutrition, depression, and social support were collected. Single-factor and multi-factor logistic regression analyses were conducted to identify the factors influencing CF. Five machine learning algorithms—Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Neural Network (NNET), and Logistic Regression (LR)—were employed to develop risk prediction models. These five models served as base classifiers, and 16 ensemble models were constructed using the Stacking method. The optimal ensemble models were identified and compared with the five individual models, followed by the selection and external validation of the most effective predictive models. Finally, the optimal models were deployed on web platforms utilizing the Streamlit library. CF prevalence was 14.2%. Significant CF risk factors included age, mode of residence, medical payment method, exercise, alcohol consumption, dialysis vascular access, serum albumin classification, serum phosphorus classification, total cholesterol classification, blood urea nitrogen classification, malnutrition score and depression score. The Stacking model showed superior performance (AUC = 0.911), with external validation confirming its accuracy (AUC = 0.832). Machine learning models, particularly Stacking, effectively predict CF risk in elderly MHD patients, providing a valuable tool for clinical intervention.

Machine Learning and the GR2M Model for Monthly Runoff Forecasting

This article presents the results of an analysis of monthly rainfall into monthly runoff using Machine Learning algorithms, including Multiple Linear Regression, Multilayer Perceptron, and Support Vector Machine, which were compared with the GR2M hydrologic model to identify the most suitable approach for rainfall-runoff analysis in watersheds in the lower southern region of Thailand. This region is characterized by its unique geographic location at the border between Thailand and Malaysia. It faces challenges due to uncertainty in rainfall data, measured only on the Thai side, leading to a lack of corresponding data from Malaysia. The analysis found that the Machine Learning Support Vector Machine algorithm consistently provided the most accurate results across all sub-basins. Sub-basin TU02 achieved an MAE of 2.63 mm/month, while sub-basin X.119A had an MAE of 68.10 mm/month, sub-basin X.184 had an MAE of 145.05 mm/month, and sub-basin X.274 had an MAE of 66.08 mm/month. This research demonstrated the utility of advanced algorithms in rainfall-runoff analysis for areas with partial or incomplete data coverage. The findings confirm that the Machine Learning Support Vector Machine algorithm outperformed the Hydrologic Model (GR2M) in terms of accuracy and reliability. Therefore, this study concludes that applying the Machine Learning Support Vector Machine algorithm is an optimal approach for runoff prediction in the southern region of Thailand and provides a framework for potential applications in other areas with similar data and geographic challenges. Doi: 10.28991/CEJ-2025-011-01-022 Full Text: PDF

Support vector machine learning algorithm for the prediction of atmospheric air temperature using historical weather data

Support vector machine learning algorithm for the prediction of atmospheric air temperature using historical weather data

Predicting bond strength between steel reinforcement and concrete materials using machine learning with Bayesian optimization techniques

ABSTRACT Predicting the adhesive force between steel reinforcement and concrete is crucial as it influences stress distribution and the overall mechanical behavior of reinforced concrete. This study proposes a novel approach to enhance bond strength prediction using machine learning (ML) models optimized through Bayesian optimization (BO). A dataset comprising 401 beam tests with six key factors was used to train three distinct ML algorithms—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The prediction models were first trained on the full dataset, with BO applied to fine-tune hyperparameters and improve accuracy. Among these models, the BO-XGBoost achieved the best performance, with an R2 of 0.74, MAE of 1.412 MPa, and RMSE of 1.516 MPa on the test set, and R2 = 0.80, MAE = 0.950 MPa, RMSE = 1.200 MPa on the training set. In addition, a simplified model was developed, incorporating only three critical variables—rebar thickness, reinforcement tensile strength, and concrete compressive capacity—to make the model more applicable in real-world engineering scenarios. To further interpret the model’s predictions, Shapley additive explanations (SHAP) were employed, revealing the specific influence of each variable on bond strength. This study demonstrates that the integration of ML with Bayesian optimization can significantly improve the accuracy of bond strength predictions, offering valuable insights for structural design optimization.

Predicting the Effect of RSW Parameters on the Shear Force and Nugget Diameter of Similar and Dissimilar Joints Using Machine Learning Algorithms and Multilayer Perceptron.

Resistance spot-welded joints are crucial parts in contemporary manufacturing technology due to their ubiquitous use in the automobile industry. The necessity of improving manufacturing efficiency and quality at an affordable cost requires deep knowledge of the resistance spot welding (RSW) process and the development of artificial neural network (ANN)- and machine learning (ML)-based modelling techniques, apt for providing essential tools for design, planning, and incorporation in the welding process. Tensile shear force and nugget diameter are the most crucial outputs for evaluating the quality of a resistance spot-welded specimen. This study uses ML and ANN models to predict shear force and nugget diameter responses to RSW parameters. The RSW analysis was executed on similar and dissimilar AISI 304 and grade 2 titanium alloy joints with equal and unequal thicknesses. The input parameters included welding current, pressure, welding duration, squeezing time, holding time, pulse welding, and sheet thickness. Linear regression, Decision tree, Support vector machine (SVM), Random forest (RF), Gradient-boosting, CatBoost, K-Nearest Neighbour (KNN), Ridge, Lasso, and ElasticNet machine learning algorithms, along with two different structures of Multilayer Perceptron, were utilized for studying the impact of the RSW parameters on the shear force and nugget diameter. Different validation metrics were applied to assess each model's quality. Two equations were developed to determine the shear force and nugget diameter based on the investigation parameters. The current research also presents a prediction of the Relative Importance (RI) of RSW factors. Shear force and nugget diameter predictions were examined using SHapley (SHAP) Additive Explanations for the first time in the RSW field. Trainbr as the training function and Logsig as the transfer function delivered the best ANN model for predicting shear force in a one-output structure. Trainrp with Tansig made the most accurate predictions for nugget diameter in a one-output structure and for shear force and diameter in a two-output structure. Depending on validation metrics, the Random forest model outperformed the other ML algorithms in predicting shear force or nugget diameter in a one-output model, while the Decision tree model gave the best prediction using a two-output structure. Linear regression made the worst ML predictions for shear force, while ElasticNet made the worst nugget diameter forecasts in a one-output model. However, in two-output models, Lasso made the worst predictions.

Detecting Wheat Leaf Diseases: A Deep Feature-Based Approach with Machine Learning Classification

Wheat is a rich storehouse of nutrients with many different vitamins and minerals. Wheat is one of the main cereals that meet the nutritional needs of humans and other living things and is used in the production of other foods. It can be grown in almost all regions of the world. It requires less irrigation compared to other plants. One of the most important problems in wheat cultivation is the fight against diseases. Wheat diseases cause yield losses and quality decreases as in other agricultural products. Timely and accurate diagnosis of these diseases; It is clear that it will lead to an increase in yield and quality. Detection of these diseases with the naked eye can be difficult and laborious. In this study, diseases on wheat leaves were detected using image processing techniques. The features of septoria and stripe rust diseases on wheat leaves were extracted using pre-trained networks VGG-16, VGG-19 and then classified with machine learning algorithms support vector machines (SVM), multi-layer perceptron (MLP), k-nearest neighbor (KNN). The results obtained were evaluated with performance criteria such as accuracy, sensitivity, specificity, precision and F1-Score. In the analysis, the features extracted with VGG-19 were classified with SVM method and the highest classification accuracy of 98.63% was achieved.

Grassland biome fragmentation analysis using sentinal-2 images and support vector machine learning model in South Africa

The grassland biome, which is classified as a terrestrial ecosystem, contributes significantly to carbon sequestration. About one third of this ecosystem covers the land surface in south Africa and faces the danger of being eradicated. Much of the pressure is attributed to by synthetic initiatives that seeks to expand the economy of the country thereby meeting the demands of cumulative population. Mining, agriculture, and human settlement are the main characters. Given the paucity of research on the threatened ecosystem, the support vector machine learning algorithm (SVM) is employed to investigate fragmentation from 2016 to 2023. We used Sentinel-2A/B satellite images to learn more about spatial and temporal patterns, as well as the distribution of fragmentation in the grassland biome, using the Google Earth Engine platform. The findings revealed that grassland occupied 66% of the area in 2016, decreased to 52% in 2019, and then increased to 59% by 2023. The inconsistency in the pattern or trend of the grassland class is likely attributable to the expansion of the other classes. The SVM model indicated an overall classification accuracy of 97.62%, 97.66% and 98.58% in 2016, 2019, and 2023, respectively. In contrast, the models developed to relate LAI to NDVI, MSAVI2, OSAVI, and NDRE produced R2 values of 0.6396, 0.6325, 0.6387, and 0.6344, respectively. An in-depth understanding of the fragmentation patterns observed in grasslands yields valuable information for the formulation of conservation strategies, sustainable land-use planning, and climate-resilient management approaches aimed at safeguarding South Africa's distinctive grassland ecosystems.

Machine learning assisted buckling analysis of cantilever bio-inspired helicoidal laminated composite cylindrical shells with cutouts

Buckling behavior of laminated composite shells changes drastically with introduction of cut outs. The present work aims to predict the buckling behavior of bio-inspired helicoidal laminated composite cantilever cylindrical shells using finite element-assisted multi-output Support Vector Machine (SVM) learning algorithm. The model is trained to predict the value for the non-dimensional critical buckling load with the number of layers, geometry of shell, orientation and thickness of each layer, and dimension of circular cut out are adopted as input parameters with critical buckling load for perfect, shell containing one circular or square hole at the center of the length, and shell containing two circular or square hole at the center of the length of the shell are predicted. Thus, single surrogate can predict critical buckling load for five cases in a single rum. Two different types of cut outs i.e., circular and square shaped cut outs are studied. Influence of helicoidal scheme, number of layers, and dimension and number of holes on the buckling behavior of shell is studied. For shell without cut outs, conventional layup schemes such as cross-ply and quasi-isotropic are found to give highest values of the non-dimensional critical buckling load compared to the helicoidal schemes. For shells with cut outs, Fibonacci helicoidal and helicoidal semi-circular (HS3) schemes gives maximum values for the non-dimensional critical buckling loads.

Machine learning-driven fluorescent sensor array using aqueous CsPbBr3 perovskite quantum dots for rapid detection and sterilization of foodborne pathogens

With the growing global concern over food safety, the rapid detection and disinfection of foodborne pathogens have become critical in public health. This study presents a novel machine learning-driven fluorescent sensor array utilizing aqueous CsPbBr3 perovskite quantum dots (PQDs) for the rapid identification and eradication of foodborne pathogens. The relative signal intensity changes (ΔRGB) generated by the sensor array were analyzed using the machine learning algorithm—Support Vector Machine (SVM). The study achieved the identification and recognition of five pathogens and their mixtures within a concentration range of 1.0 × 103 to 1.0 × 107 CFU/mL with an accuracy rate of 100 %, and the limits of detection (LOD) for the pathogens were found to be low. Additionally, the array also showed excellent performance in the identification of pathogens in tap water, achieving an accuracy rate of 100 %. Furthermore, the fluorescent sensor array was capable of inactivating the pathogens with an efficiency of over 99 % within 30 min post-detection. This development provides an efficient and reliable tool for the field of food safety detection.

Individualized post-operative prediction of cochlear implantation outcomes in children with prelingual deafness using functional near-infrared spectroscopy.

The goal of this study was to develop an objective measure and predictor of cochlear implantation (CI) outcomes using functional near-infrared spectroscopy (fNIRS) for young children with prelingual deafness. Sound-evoked hemodynamic responses were recorded from auditory and language-related cortical regions of 47 child CI recipients (35.47 ± 17.24 months of age) using fNIRS shortly after CI activation (0.26 ± 0.30 months). There were four sound conditions (natural speech, instrumental music, multi-speaker babble noise, and speech-in-noise). Post-CI auditory and verbal communication performance was evaluated using clinical questionnaires with caretakers. Both classification and individualized regression models were constructed to predict post-CI behavioral improvement from fNIRS data using support vector machine (SVM) learning algorithms. Auditory cortical responses shortly after CI hearing onset yielded highly accurate prediction of behavioral development in young CI children. For classification models, optimal prediction was achieved using cortical responses to two or more sound conditions, with the highest accuracy of 98.20% (precision = 98.17%, sensitivity = 98.96%, area under the curve of the receiver operating characteristic curve = 99.61%) obtained with the combination of speech, noise, and music stimuli. Similarly, for regression models, best prediction of individual development was achieved using three (highest r = 0.919) or four (r = 0.966) sound conditions. The predictability of cortical responses far outperformed (Cohen's d: 18.56) that of the collection of audiological and demographic parameters (classification accuracy: 0.62) under the same SVM algorithms and could not benefit from the inclusion of the latter. Machine learning models using auditory cortical hemodynamic responses shortly after CI activation were able to predict individualized post-CI behavioral improvement in children with prelingual deafness. Level 5.

A machine learning-based approach for predicting the level of palm oil adulteration in coconut oil

Coconut oil, prized for its health benefits, faces quality threats from adulteration, particularly with cheaper palm oil. This not only degrades the quality but also poses health risks. Traditional detection methods are often labor-intensive, destructive, and time-consuming. This study addresses the issue by applying multispectral imaging technology combined with machine learning to detect palm oil adulteration in coconut oil. We selected four machine learning algorithms—Support Vector Machines (SVM), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), and Bagging—due to their robustness in handling complex datasets. These models achieved classification accuracies of up to 100 %, far surpassing traditional chemical tests, which are slower and dependent on tester expertise. To further enhance detection accuracy, we employed both hard- and soft-voting mechanisms, integrating the strengths of individual models to improve overall reliability. This research marks a significant advancement in detecting coconut oil adulteration, offering a faster, more efficient solution to ensure product quality and consumer health.

Mapping forest tree species and its biodiversity using EnMAP hyperspectral data along with Sentinel-2 temporal data: An approach of tree species classification and diversity indices

Forests play a crucial role in maintaining ecological balance and biodiversity, making the accurate mapping of tree species and assessment of biodiversity indices essential for informed management decisions. This study introduces an innovative methodology that integrates EnMAP (Environmental Mapping and Analysis Program) hyperspectral data with Sentinel-2 multitemporal data to classify tree species in the biodiverse landscapes of Kampinos National Park and its surrounding regions in Poland.We extract essential vegetation indices such as NDVI, NDMI, SAVI, and EVI from Sentinel-2 data to assess forest health and dynamics. The Sentinel-2 data is upscaled from 10 m to 30 m to align with EnMAP’s spatial resolution, followed by precise co-registration of the images using QGIS. Utilizing a rich dataset from the National Forest Inventory, we extract spectral signatures of nine distinct tree species from both data sources. We employ five machine learning algorithms—Support Vector Machines (SVM), Random Forest (RF), CatBoost (CAT), Gradient Boosting Classifier (GBC), and XGBoost (XGB)—to enhance classification accuracy.Through iterative experimentation with data reduction techniques and algorithm tuning, we achieve optimal performance across needle-leaved and broad-leaved species. The resulting tree species maps are validated through quantitative accuracy assessments against mixed-species polygons from the National Forest Inventory and ground truthing in the Kampinos National Park. Achieving an overall accuracy of 85% to 93%, our study demonstrates the efficacy of this integrated approach in tree species mapping. Furthermore, the tree species maps serve as a foundation for deriving key biodiversity indices—species richness, Shannon-Wiener Diversity Index, Simpson’s Diversity Index, and a composite Biodiversity Index—providing insights into spatial biodiversity patterns and informing targeted conservation strategies. This study exemplifies the potential of combining advanced remote sensing techniques with field validation to enhance our understanding of forest ecosystems and guide sustainable management practices.

A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo

A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo

Wearable self-powered motion sensor based on biomass carbon-dots/polyvinyl alcohol film

The functionalized polymer with scalable properties presents a promising option for flexible triboelectric nanogenerators designed for detecting human motion. In this study, a novel method to fabricate tribopositive composite film was proposed by incorporating high permittivity N-doped orange peel-based carbon dots (O-CDs) into the highly cross-linked polyvinyl alcohol (PVA). The O-CDs/PVA sensor showed elevated electrical output with the raised content of O-CDs until 1.5 wt. % with a peak voltage of 3.5 V. The response characteristics under various external force conditions were tested, and the maximum peak voltage and current reached 3.92 V and 28.6 A under the force of 50 N. Moreover, O-CDs/PVA sensors present superior high-output stability, durability, and accurate distinction to different movement states, even the vocal cord vibration. By introducing the support vector machine learning algorithm, intelligent gesture recognition was achieved. This work paves a facile, feasible, and scalable pathway to the self-powered human motion sensor device.

Predicting non-suicidal self-injury among Chinese adolescents: The application of ten algorithms of machine learning

Background and aimsHigh non-suicidal self-injury (NSSI) prevalence among adolescents is a global health issue. However, current prediction models for adolescent NSSI rely on a limited set of algorithms, resulting in biased predictions. Therefore, the aim of this study is to develop multiple machine learning models to enhance prediction accuracy and mitigate biases among Chinese adolescents. MethodsA total of 4487 junior and senior high school students in China were recruited. Multiple algorithms were included, such as logistic regression, decision tree, support vector machine, Naive Bayes, multi-layer perceptron, K-nearest neighbors, and ensemble learning algorithm like random forest, bagging, AdaBoost, and stacking to build predictive models. Data processing techniques, including standardization and the synthetic minority oversampling technique, were employed to optimize the predictive model. The model was trained on 70 % of the data, reserving 30 % for testing. ResultsThe ten prediction models achieved a good performance, with area under the receiver operating characteristic curve (AUC) scores above 0.700 in the test set. The stacking and random forest models achieved AUC scores of 0.904 and 0.898, respectively. The prediction performance of the Naive Bayes model was relatively poor. The top five important variables were resilience, bully, suicidal ideation, internet addiction, and depression. ConclusionsThe ensemble machine learning algorithm showed promising results predicting NSSI among adolescents. Such algorithms should be recommended for future NSSI research to enhance predictive accuracy. Identification of important features in NSSI prediction can help develop screening protocols and lay a foundation for clinical diagnosis and intervention in adolescent populations.

EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE

This study evaluates several machine learning algorithms—Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (C4.5), and k-Nearest Neighbors (KNN)—for breast cancer detection using the Breast Cancer Wisconsin Diagnostic dataset. We implemented comprehensive pre-processing and model evaluation with Scikit-learn in Python. Our findings show that SVM achieved the highest accuracy, with 99.9% on the training set and 98.50% on the testing set, indicating superior performance in handling high-dimensional data. Random Forest also performed well, with accuracies of 98.5% and 98.20%, respectively. Logistic Regression and Decision Tree models provided reliable predictions when tuned, while KNN was less effective. SVM and Random Forest are recommended for clinical decision support systems due to their high accuracy and robustness.

Comparison of classical Machine Learning-based algorithms to predict Triplex Forming Oligonucleotides

Machine learning algorithms can be utilized to tackle genomic challenges in biological systems, such as identifying therapeutic targets for drug development and searching for effective treatments for chronic diseases like cancer. Triplex-forming oligonucleotides (TFOs) can recognize specific sites in the major groove of duplex DNA sequences, aiding in the identification of therapeutic targets via computational methods. This study aims to compare the performance of five classical machine learning algorithms—Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—in classifying single-stranded DNA into TFOs. Three datasets were created from an oligo library targeting the supF gene, developed by Kaufmann et al., and used to train the algorithms. The classifiers were optimized through hyperparameter tuning and their performance was evaluated using accuracy, precision, recall, and F1-score metrics. SVM and kNN achieved less than 90 % accuracy, while DT, RF, and XGBoost, which used tree-based methods, attained over 90 % accuracy. The XGBoost model showed the best performance with over 96 % accuracy in classifying single-stranded DNA into TFOs. This study demonstrates that supervised machine learning techniques are effective for accurately classifying single-stranded DNA into TFOs.