Comparison Of Support Vector Machine Research Articles

Comparison of SVM, KNN, and Naïve Bayes Classification Methods in Predicting Student Transfers at BK Palu School

Student transfers are a significant issue in schools and can affect the dynamics of education and student performance. This research aims to predict student transfers using a comparative analysis of three classification methods: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. The study utilizes historical data from BK Palu School, covering the years 2022 to 2024, which includes demographic, academic, socio-economic, and student quality information. The methodology involves data collection, data preparation, algorithm selection, implementation, and evaluation of the three methods. The performance of the classification methods is assessed using metrics such as accuracy, precision, recall, and F1-score. The results indicate that SVM has the highest accuracy in predicting student transfers, followed by KNN and Naïve Bayes. This study contributes to identifying key factors influencing student transfers and offers schools a robust model to develop targeted strategies for reducing transfer rates. Ultimately, this research provides insights into optimizing student retention and improving the overall quality of education.

Supervised Machine Learning to Predict Drilling Temperature of Bone

Surgeons face a significant challenge due to the heat generated during drilling, as excessive temperatures at the bone–tool interface can lead to irreversible damage to the regenerative soft tissue and result in thermal osteonecrosis. While previous studies have explored the use of machine learning to predict the temperature rise during bone drilling, this in vitro study introduces a comprehensive approach by combining the Response Surface Methodology (RSM) with advanced machine learning techniques. The main objective lies in the comprehensive evaluation and comparison of support vector machine (SVM) and random forest (RF) models specifically for the optimization of the bone drilling parameters to prevent thermal bone necrosis. A total of 27 experiments were conducted using a multi-level factorial method, with analysis performed via the Minitab software version 19.1. Performance metrics such as the mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used to assess model accuracy. The RF model emerged as the most effective, with R2 values of 94.2% for testing and 97.3% for training data, significantly outperforming other models in predicting temperature fluctuations. This study demonstrates the superior predictive capabilities of the RF model and offers a robust framework for the optimization of surgical procedures to mitigate the risk of thermal damage.

Performance Comparison of Support Vector Machines, AdaBoost, and Random Forest for Sentiment Text Analysis and Classification

In sentiment analysis, text analysis becomes an important process to derive useful information from the unstructured data. In this work, we study the performance of three advanced machine learning algorithms, Support Vector Machines (SVM), Random Forest, and AdaBoost, for a specific sentiment classification task. Each classifier was trained and evaluated on principal metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1-Score, Precision, and Recall using the Bag of Words model for feature extraction. Those results show that the Random Forest approach beat both SVM and AdaBoost, with an AUC of 0.988, CA = 0.915, and F1-Score = 0.915 SVM demonstrated moderate performance with an AUC of 0.939 and an F1-Score of 0.845, while AdaBoost exhibited the worst performance in all metrics based on that ensemble model-based classifiers for data change predictions. Random Forest may thus be a powerful machine learning technique to implement for sentiment analysis in text.

Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran

Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran

Comparison of SVM & Naïve Bayes Methods in Sentiment Analysis of Electric Vehicle Subsidy Policy Based on X Data

The policy of subsidizing electric vehicles has become a widely discussed issue on social media platform X. The provision of electric vehicle subsidies by the Indonesian government aims to stimulate higher adoption of electric vehicles, with the overarching goal of mitigating air pollution. However, the presence of electric vehicle subsidies continues to elicit both support and opposition among the public. On social media platform X, there is a wealth of data suitable for text mining, particularly concerning the current hot topic of electric vehicle subsidies. This research aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes methods in conducting sentiment analysis on discussions related to the electric vehicle subsidy policy on social media platform X. The testing technique involves using 20% of the total dataset, comprising 5553 data points, and employing 10-fold cross-validation. The results from the 20% test data indicate that the Support Vector Machine (SVM) method's confusion matrix performance is superior, with the highest values achieved using the RBF kernel: accuracy 83.02%, precision 84.61%, and recall 83.02%. In the performance evaluation testing with 10-fold cross-validation, the Support Vector Machine (SVM) method outperforms, especially with the RBF kernel, yielding an average accuracy of 82.88% over 10 iterations.

Comparison of support vector machine and random forest algorithms for classification of songs for relaxation purposes in individuals with stress disorders

Comparison of support vector machine and random forest algorithms for classification of songs for relaxation purposes in individuals with stress disorders

Predicting hourly electricity consumption of chillers in subway stations: A comparison of support vector machine and different artificial neural networks

It is essential to predicting the electricity consumption of air-conditioning systems. In this study, three models, namely, backpropagation neural network (BPNN), cascade correlation neural network (CCNN), and support vector regression (SVR), were proposed for the prediction of the electricity consumption of chillers in subway stations. For demonstration, historical data from a subway station in Guangzhou were used for training and testing. The simulation results showed that the proposed models could predict the electricity consumption of chillers effectively. In comparison with BPNN, CCNN and SVR showed higher accuracy. For the refrigeration of public spaces (PSs), weak electricity regions (WERs), and comprehensive management regions (CMRs), CCNN exhibited the minimum mean absolute percentage error (MAPE) and root-mean-square error (RMSE) for the test samples, the values of which were 1.56% and 3.15, respectively, followed by those of SVR and BPNN. For the refrigeration of strong electricity regions (SERs), PSs, WERs, and CMRs, SVR presented the smallest error, and its MAPE and RMSE for the test samples were 2.01% and 6.09, respectively, followed by those of CCNN and BPNN. Through the comprehensive consideration of input characteristics, the comparison of different prediction models and the distinction of different operating conditions, the prediction accuracy was improved effectively. Based on the forecasting, the operational parameters of the air-conditioning system can be optimized in advance, which is of great significance for energy saving in subway stations.

Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction

Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are time-consuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the Histogram of Oriented Gradients (HOG) feature extraction method with a dataset of corn leaf images. The classification results achieved an accuracy of 71.44%, AUC of 79.16%, precision of 70.08%, recall of 71.44%, and f1 score of 67.11%. The highest accuracy was obtained by combining HOG feature extraction with the SGD algorithm.

Nutrition Classification In Toddlers at UPTD Puskesmas Tigaraksa Using A Comparison of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) Methods

Toddler are a group of people who are vulnerable to nutritional problems. If the incidence of malnutrition is not addressed, it will have a negative impact on children under five, malnutrition is a condition experienced by a person due to a lackof nutritional intake of the amount of nutrients consumedis below. Health centers are required to improve and organize helath services as well as possible therefore researchers conduct research at the UPTD Tigaraksa Health Center by doing comparison of classification results on toddler nutritional data using the Support Vector Machine and K-Nearest Neighbor methods using WEKA Tools. Based on the result of a comparison between the Support Vector Machine and K-Nearest Negihbor methods using WEKA Tools by carrying out 5 (five) stages of testing namely : Use Training Set, 4 Cross-Validation, 8 Cross-Validation, 50% Percentage Split dan 80% Percentage Split, the results show that the Support Vector Machine method Kernel Radial Basis Function (RBF) is an average accuracy value of 100% higher than the K-Nearest Neighbor Euclidean Distnace algorithm with an average accuracy of 93%.

Comparison of SVM, NBC, and KNN Classification Methods in Determining Students’ Majors at SMK N02 Manokwari

The stages of choosing a major for prospective SMK students are rarely the beginning of the next career determination. The determination of the major aims to make students more directed in receiving lessons based on the abilities and talents of the students, and, of course, when the student graduates, they already have the skills to get a job if they do not continue their education to college. The method used in this study is data mining techniques. But not all data mining algorithms perform well in classifying the selection of interest paths at the SMK level. Therefore, this study will discuss the comparative analysis of the performance level of the Support Vector Machine (SVM) classification algorithm and the Naïve Bayes Classifier (NBC) and K-Nearest Neighbors (KNN). Comparison of NBC, KNN and SVM methods was measured using feeding accuracy for the KNN method to get an accuracy of 54.56%, then for the NBC method to get an accuracy of 74.78%, and the SVM method to get an accuracy of 58.70%. Then it can be concluded that the three methods, based on the attributes used by the NBC method, got high accuracy, which is 74.78%.

Comparison of Support Vector Machines and K-Nearest Neighbor Algorithm Analysis of Spam Comments on Youtube Covid Omicron

Every time a new variant of Coronavirus (Covid-19) appears, themedia or news platforms review it to find out whether the new variantis more dangerous or contagious than before. One of the media orplatforms that is fast in presenting news in videos is YouTube.YouTube is a social media that can upload videos, watch videos, andcomment on the video. The comment field on YouTube videos cannotbe separated from spam comments that annoy other users who want tofollow or participate in the comment column. Indication of spamcomments is still done by observing one by one; this is very inefficientand time-consuming. This study aims to create a model that canclassify spam on YouTube comments. The classification method uses the SVM (Support Vector Machines) algorithm and the KNN (K-Nearest Neighbor) algorithm to identify spam comments or not with comment data taken from Omicron's Covid-19 news video on national news channels. The classification results show that the SVM method is superior inaccuracy with the Linear SVC algorithm of 75.12%, SVC of 76.11%, and Nu-SVC of 77.11%. While the KNN algorithm with k=2 is 65.67%, k=4 is 64.51%, k=6 is 62.35%.

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

The Comparison of SVM and ANN Classifier for COVID-19 Prediction

Coronavirus 2 (SARS-CoV-2) is the cause of an acute respiratory infectious disease that can cause death, popularly known as Covid-19. Several methods have been used to detect COVID-19-positive patients, such as rapid antigen and PCR. Another method as an alternative to confirming a positive patient for COVID-19 is through a lung examination using a chest X-ray image. Our previous research used the ANN method to distinguish COVID-19 suspect, pneumonia, or expected by using a Haar filter on Discrete Wavelet Transform (DWT) combined with seven Hu Moment Invariants. This work adopted the ANN method's feature sets for the Support Vector Machine (SVM), which aim to find the best SVM model appropriate for DWT and Hu moment-based features. Both approaches demonstrate promising results, but the SVM approach has slightly better results. The SVM's performances improve accuracy to 87.84% compared to the ANN approach with 86% accuracy.
 

Comparison of Support Vector Machine and K-Nearest Neighbors in Breast Cancer Classification

Cancer is one of the leading causes of death, and breast cancer is the second leading cause of cancer death in women. One method to realize the level of malignancy of breast cancer from an early age is by classifying the cancer malignancy using data mining. One of the widely used data mining methods with a good level of accuracy is the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). Evaluation techniques of percentage split and cross-validation were used to evaluate and compare the SVM and KNN classification models. The result was that the accuracy level of the SVM classification method was better than the KNN classification method when using the cross-validation technique, which is 95,7081%. Meanwhile, the KNN classification method was better than the SVM classification method when using the percentage split technique, which is 95,4220%. From the comparison results, it can be seen that the KNN and SVM methods work well in the classification of breast cancer.

Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination

The corona virus has become a global pandemic and has spread almost all over the world, including Indonesia. There are many negative impacts caused by the spread of COVID-19 in Indonesia, so the government takes vaccination measures in order to suppress the spread of COVID-19. The public's response to vaccination was quite diverse on Twitter, some were supportive and some were not. The data used in this study came from Twitter which was taken using the drone emprit portal, using the keyword, namely "vaccination". The classification will be carried out using the SVM and hybrid methods between SVM and XGBoost or what is commonly called XGBSVM. The purpose of this study is to provide an overview to the public whether the Covid-19 vaccination actions carried out tend to be positive, neutral or negative opinions. The results of the sentiment evaluation that have been carried out can be seen that SVM has the highest accuracy of 83% with 90:10 data splitting, then XGBSVM produces 79% accuracy with 90:10 data splitting.

Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks

Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE. Using a collated database of drugs licensed from 2016-2020, drugs were classified into three groups; positive, negative or no FE. Greater than 250 drug properties were predicted for each drug which were used to train predictive models using Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. When compared, ANN outperformed SVM for FE classification upon training (82%, 72%) and testing (72%, 69%). Both models demonstrated higher FE prediction accuracy than the Biopharmaceutics Classification System (BCS) (46%). This exploratory work provided new insights into the connection between FE and drug properties as the Octanol Water Partition Coefficient (S+logP), Number of Hydrogen Bond Donors (HBD), Topological Polar Surface Area (T_PSA) and Dose (mg) were all significant for prediction. Overall, this study demonstrated the utility of ML to facilitate early anticipation of likely FE in pre-clinical development using four well-known drug properties.

Performance comparison of support vector machine and gaussian naive bayes classifier for youtube spam comment detection

Youtube is a video sharing site that was begun back in 2005. Youtube produces over 400 hours of substance each moment and more than 1 billion hours of substance are devoured by clients every day. In this work, we present a new approach by comparing the analysis results using a support vector machine and the Gaussian Naive Bayes classificatio. Our proposed methodology We used the  dataset from UCI especially Youtube-Shakira for training and testing. The transformed dataset is split into training and testing subsets and fed into Naive Bayes and Support Vector Machin. In all cases, the F1 score was used to evaluate the classifier's performance. The results of the experiment are displayed in Gaussian Naive Bayes with an F1 score of 84.38% and a Support Vector Machine (SVM) with an F1 score of 88.00%. Naive Bayes is consistently the worst performer than SVM.

Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter

Sentiment analysis is a technique to extract information of one’s perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify society’s response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.

RETRACTED: Identifying non-contact defects in fault bearing

Early diagnosis of bearing failures can save time, effort, and money on rotating machine maintenance. On this test, a non-touch type vibration pickup was designed and refined to capture vibration data for bearing fitness tracking under tight load and pace variations, avoiding the bodily connection of vibration pickup to the system tool. The signal was denoised and fault analysis was performed using a Hilbert rework. Principal Component Analysis (PCA) was used to reduce the dimensionality of the extracted capabilities, and then the chosen capabilities for lowering the amount of enter capabilities and discovering the maximum premier function set, the Sequential Floating Forward Selection (SFFS) method was used to rank them in order of significance. Finally, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used to determine and classify the numerous faults in bearings. A comparison of SVM and ANN efficacy was carried out. The results reveal that vibration signatures from advanced non-touch sensors (NCS) correspond well with accelerometer data collected under the same conditions. The classification accuracy obtained by combining the advanced NCS with various sensors mentioned in the literature is comparable to that obtained by using the advanced NCS alone. The proposed method could be utilised to detect automated popularity system faults and issue early warnings, preventing unwelcome and unplanned device shutdowns due to bearing failure.

Comparison of Support Vector Machine and K-Nearest Neighbor for Baby Foot Identification based on Image Geometric Characteristics

Biometric recognition of infant identification systems is critical in security access for identification and verification systems. However, until now, hospitals or health centres in Indonesia still use conventional biometric identification, such as stamping or inking on the soles of babies' feet affixed to paper and are very vulnerable to the risk of damage or loss of data. To resolve this problem, computer vision technology can accurately identify the baby's feet' soles with the final result in the form of digital data. This study compares the classification method of baby feet using the SVM (Support Vector Machine) algorithm with the K-Nearest Neighbor algorithm. The baby's feet understudy image was taken using a cellphone camera with sample data of 3 months old babies. Comparing the SVM and KNN classification methods obtained high accuracy, precision and recall values, namely 98.80% accuracy, 89.51% precision and 88.00% recall. (for the SVM Gaussian kernel classification), with an accuracy of 99.08%, 92.65% precision and 90.75% recall (for the KNN Ecluidean Distance classification), it can be concluded that the KNN classification method using Euclidean distance is the best for applied in the baby palm identification system using the geometric image feature.