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A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island by Mohd Shafri, Helmi Zulhaidi, Ramle, F. S. H.

“…The study indicates that the classification accuracy of SVM algorithm was better than DT algorithm. The overall accuracy of the SVM using four kernel types was above 73% and the overall accuracy of the DT method was 69%. …”
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Improving Support Vector Machine Performance using Modified Similarity Distance Plotting-Data Reduction by Abdul Muqtasid, Rushdi, Mohammad, Hossin, Norita, Norwawi

“…Our evaluation, conducted on 30 diverse datasets, compares the performance of SDP-DR with standard SVM, Edited Nearest Neighbour (ENN) + SVM, and Condensed Nearest Neighbour (CNN) + SVM models. …”
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Financial time series predicting using machine learning algorithms by Tiong, Leslie Ching Ow *

“…Thereafter, Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms are implemented separately to train with the trend patterns for predicting the movement direction of financial trends. …”
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Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

“…However, the imbalanced LR-based methods are not extensively developed such as imbalanced SVM-based methods. Hence, it is required to develop effective imbalanced LR-based methods to be widely used in data mining applications. …”
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The identification of high potential archers based on relative psychological coping skills variables: a support vector machine approach by Taha, Z., Musa, R.M., Majeed, A.P.P.A, Abdullah, M.R., Zakaria, M.A., Alim, M.M., Jizat, J.A.M., Ibrahim, M.F.

“…Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. …”
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Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill by Abunama, Taher, Othman, Faridah, Ansari, Mozafar, El-Shafie, Ahmed

“…These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. …”
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Performance comparison of different machine learning algorithms on a time-series of covid-19 data: A case study for Saudi Arabia by Ahmad, M.T., Qaiyum, S., Alamri, A., Islam, S.

“…Several machine learning models and related algorithms were developed for prediction of total cases and total deaths. …”
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Performance comparison of different machine learning algorithms on a time-series of covid-19 data: A case study for Saudi Arabia by Ahmad, M.T., Qaiyum, S., Alamri, A., Islam, S.

“…Several machine learning models and related algorithms were developed for prediction of total cases and total deaths. …”
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The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach by Zahari, Taha, Rabiu Muazu, Musa, Anwar, P. P. Abdul Majeed, Mohamad Razali, Abdullah, Muhammad Aizzat, Zakaria, Muhammad Muaz, Alim, Jessnor Arif, Mat Jizat, Mohamad Fauzi, Ibrahim

“…Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. …”
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SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors by Muhammad Raza, Ul Mustafa, Abdulkadir, Taofeeq Sholagber, Khamaruzaman, Wan Yusof, Ahmad Mustafa, Hashim, M., Waris, Muhammad, Shahbaz

“…The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. …”
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Identification Of Flow Blockage Levels In Centrifugal Pump By Machine Learning by Ng, Woon Li

“…Besides, the result of the Linear SVM model is validated by using the classification packages in Google Colaboratory. …”
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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar M.A.A.A., Ker P.J., Tang S.G.H., Baharuddin M.Z., Lee H.J., Omar A.R.

“…Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chicken�s comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. …”

A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani by Mahmoud Reza, Saybani

“…The FSR-AIRS2 is a new hybrid algorithm that incorporates the FRA, RRC, and the SVM into AIRS2 in order to produce a stronger classifier. …”
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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar, Mohd Anif A. A., Ker, Pin Jern, Tang, Shirley G. H., Baharuddin, Mohd Zafri, Lee, Hui Jing, Omar, Abdul Rahman

“…Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chickens comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95 accuracy, followed by SVM-RBF kernel, and KNN with 93 accuracy, Decision Tree with 90 accuracy, and lastly, SVM-Sigmoidal kernel with 83 accuracy. …”
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A novel approach based on machine learning and public engagement to predict water-scarcity risk in urban areas by Hanoon, Sadeq Khaleefah, Abdullah, Ahmad Fikri, M. Shafri, Helmi Z., Wayayok, Aimrun

“…The approach was used to detect (WSR) in two ways, namely, prediction using ML models directly and using the weighted linear combination (WLC) function in GIS. Five types of ML algorithm, namely, support vector machine (SVM), multilayer perceptron, K-nearest neighbour, random forest and naïve Bayes, were incorporated for this purpose. …”
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Durian (Durio zibethinus) ripeness detection using thermal imaging with multivariate analysis by Mohd Ali, Maimunah, Hashim, Norhashila, Shahamshah, Muhammad Ikmal

“…Principal component analysis (PCA) showed successful clustering ability of three different ripeness levels of durians. Linear discriminant analysis (LDA), k-nearest neighbour (kNN), and support vector machine (SVM) were applied for the establishment of the optimal classification modelling algorithms. …”
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Job position prediction based on skills and experience using machine learning algorithm / Ezaryf Hamdan by Hamdan, Ezaryf

“…The Machine Learning algorithm, comprising Random Forest, Linear Regression, XGBoost, SVM, and Stacking Ensemble, is embedded in the system for job position predictions based on the analysed data. …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

“…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
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An intra-severity classification and adaptation technique to improve dysarthric speech recognition accuracy / Bassam Ali Qasem Al-Qatab by Bassam Ali Qasem, Al-Qatab

“…The algorithms include Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM), Naive Bayes (NB), Classification And Regression Tree (CART), Random Forest (RF). …”
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