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Journal articles on the topic 'Random Forest (RF) and Support Vector Machines (SVM)'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Jane, Eva Aurelia, Rustam Zuherman, Wirasati Ilsya, Hartini Sri, and Stephani Saragih Glori. "Hepatitis classification using support vector machines and random forest." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 446–51. https://doi.org/10.11591/ijai.v10.i2.pp446-451.

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Hepatitis is a medical condition defined by inflammation of the liver. It can be caused by infection of the liver by hepatitis viruses or is of unknown aetiology. There are 5 main hepatitis viruses, such as virus types A, B, C, D and E. The infection may occur with limited or no symptoms, but also may include some symptoms like abdominal pain, dark urine, extreme fatigue, jaundice, nausea or vomiting. Because Indonesia is a large archipelago, the prevalence of viral infections varies greatly by region of acute hepatitis patients. This research uses data of hepatitis examination result with amo

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Aurelia, Jane Eva, Zuherman Rustam, Ilsya Wirasati, Sri Hartini, and Glori Stephani Saragih. "Hepatitis classification using support vector machines and random forest." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 446. http://dx.doi.org/10.11591/ijai.v10.i2.pp446-451.

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<span id="docs-internal-guid-e57881bf-7fff-62db-2c1e-192664c8e8a8"><span>Hepatitis is a medical condition defined by inflammation of the liver. It can be caused by infection of the liver by hepatitis viruses or is of unknown aetiology. There are 5 main hepatitis viruses, such as virus types A, B, C, D and E. The infection may occur with limited or no symptoms, but also may include some symptoms like abdominal pain, dark urine, extreme fatigue, jaundice, nausea or vomiting. Because Indonesia is a large archipelago, the prevalence of viral infections varies greatly by region of acute

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Chelvian, Aroef, Rivan Yuda, and Rustam Zuherman. "Comparing random forest and support vector machines for breast cancer classification." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 815–21. https://doi.org/10.12928/TELKOMNIKA.v18i2.14785.

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There are more than 100 types of cancer around the world with different symptoms and difficulty in predicting its appearance in a person due to its random and sudden attack method. However, the appearance of cancer is generally marked by the growth of some abnormal cell. Someone might be diagnosed early and quickly treated, but the cancerous cell most times hides in the body of its victim and reappear, only to kill its sufferer. One of the most common cancers is breast cancer. According to Ministry of Health, in 2018, breast cancer attacked 42 out of every 100.000 people in Indonesia with appr

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Traoré, Farid, Sié Palé, Aïda Zaré, Moussa Karamoko Traoré, Blaise Ouédraogo, and Joachim Bonkoungou. "A Comparative Analysis of Random Forest and Support Vector Machines for Classifying Irrigated Cropping Areas in The Upper-Comoé Basin, Burkina Faso." Indian Journal Of Science And Technology 17, no. 8 (2024): 713–22. http://dx.doi.org/10.17485/ijst/v17i8.78.

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Objectives: This study investigates the performance of two machine-learning algorithms in classifying land areas across the Upper-Comoé basin in Burkina Faso. Methods: Within the Google Earth Engine data processing environment, Support Vector Machine (SVM) and the Random Forest (RF) algorithms were applied to a Landsat-8 OLI image of March 2019, to discriminate agricultural land areas, with an emphasis on irrigated areas. Findings: The results indicated good to excellent classification performance, with overall accuracies and Kappa coefficients between 71% and 99%, and 0.66 and 0.99, respectiv

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Agjee, Na’eem Hoosen, Onisimo Mutanga, Kabir Peerbhay, and Riyad Ismail. "The Impact of Simulated Spectral Noise on Random Forest and Oblique Random Forest Classification Performance." Journal of Spectroscopy 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/8316918.

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Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable noisy conditions, the extent of this is poorly understood. This study aimed to evaluate the influence of simulated spectral noise (10%, 20%, and 30%) on random forest (RF) and oblique random forest (oRF) classification performance using two node-splitting models (ridge regression (RR) and support vector machines (SVM)) to discriminate healthy an

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Sultana, Salma, and M. Inayathulla. "Precision Land Use and Land Cover Classification Using Google Earth Engine: Integrating Random Forest and Support Vector Machine Algorithms." Geo-Eye 11, no. 2 (2022): 9–14. https://doi.org/10.53989/bu.ge.v11i2.4.

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Land Use and Land Cover (LULC) classification plays a pivotal role in understanding and managing environmental resources. This study presents a novel methodology utilizing sentinel satellite data in conjunction with two robust machine learning algorithms: Random Forest (RF) and Support Vector machine (SVM) on Google Earth Engine platform. Sentinel data, renowned for its high- resolution multispectral imagery, provides rich information for classification. Google Earth Engine provides access to vast geospatial datasets and computational resource, enabling effective analysis. RF and SVM, distingu

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Saturi, Swapna, Rafia Adiba, Yeshwanth Reddy I, Arsalan Shareef Md, and Prathyush D. "Advanced MRI-Based Alzheimer’s Disease Classification with Hybrid Convolutional Neural Networks." Journal of Computer Allied Intelligence 3, no. 1 (2025): 31–39. https://doi.org/10.69996/jcai.2025003.

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The key to effective therapy and management of Alzheimer's disease (AD), a progressive neurological condition, is obtaining a prompt and precise diagnosis. Support Vector Machines (SVMs), Random Forest (RFs), and Gradient Boosting Machines (GBMs) are some of the most used traditional machine learning algorithms for Alzheimer's disease (AD) classification, but they struggle to deal with the complexity of medical imaging data. On the other hand, Convolutional Neural Networks (CNNs) are great at image classification and have shown impressive results in analyzing MRI scans for AD detection. Plus,

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Ganjirad, M., and M. R. Delavar. "FLOOD RISK MAPPING USING RANDOM FOREST AND SUPPORT VECTOR MACHINE." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 201–8. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-201-2023.

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Abstract. Floods are among the natural disasters that cause financial and human losses all over the world every year. By production of a flood risk map and determination of potential flood risk areas, the possible damages of this phenomenon can be reduced. To map the flood extend in Calcasieu Parish, Louisiana, US, conditioning factors affecting the flood occurrence including elevation, slope, plan curvature, land use, distance from rivers, density of rivers, rainfall, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference b

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Lachaud, Alix, Marcus Adam, and Ilija Mišković. "Comparative Study of Random Forest and Support Vector Machine Algorithms in Mineral Prospectivity Mapping with Limited Training Data." Minerals 13, no. 8 (2023): 1073. http://dx.doi.org/10.3390/min13081073.

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This paper employs two data-driven methods, Random Forest (RF) and Support Vector Machines (SVM), to develop mineral prospectivity models for an epithermal Au deposit. Four distinct models are presented for comparison: one employing RF and three using SVM with different kernel functions—namely linear, Radial Basis Function (RBF), and polynomial. The analysis leverages a compact training dataset, encompassing just 20 deposits, with deposit and non-deposit locations chosen from known mineral occurrences. Fourteen predictor maps are constructed based on the available data and the exploration mode

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SRISANKAR, M., and Dr K. P. LOCHANAMBAL. "THE SENTIMENTAL ANALYSIS USING DEEP LEARNING MODELS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27151.

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ABSTRACT:The tweets are brief and come in a steady stream. Emotions have a significant impact on feelings. People can express their ideas about anything and anything on social media. Public perception is divided into three categories: positive, negative, and neutral. In this study, Twitter hotel reviews are gathered and pre-processed before being analyzed using Python's Tweepy package. Re-tweets, tags, URLs, hash tag symbols, and duplicate entries are all eliminated as part of a screening procedure to remove any discrepancies in the data. Using Python's scikit-learn module, tweets are up-sampl

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Fauzi, Ariq Ammar, Anik Vega Vitianingsih, Slamet Kacung, Anastasia Lidya Maukar, and Seftin Fiti Ana Wati. "Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods." Teknika 14, no. 1 (2025): 150–56. https://doi.org/10.34148/teknika.v14i1.1198.

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TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application. This research aims to develop an application capable of performing sentiment analysis on TripAdvisor application user reviews on the Google Play Store with negative, positive, and neutral classifications using the Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The RF method was chosen in this study because of its ability to handle large and complex data

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Zhao, Qian, Lu Li, Lihua Zhang, and Man Zhao. "Recognition of Corrosion State of Water Pipe Inner Wall Based on SMA-SVM under RF Feature Selection." Coatings 13, no. 1 (2022): 26. http://dx.doi.org/10.3390/coatings13010026.

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To solve the problem of low detection accuracy of water supply pipeline internal wall damage, a random forest algorithm with simplified features and a slime mold optimization support vector machine detection method was proposed. Firstly, the color statistical characteristics, gray level co-occurrence matrix, and gray level run length matrix features of the pipeline image are extracted for multi-feature fusion. The contribution of the fused features is analyzed using the feature simplified random forest algorithm, and the feature set with the strongest feature expression ability is selected for

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Alkattan, Hussein, Benson Turyasingura, Byamukama Willbroad, and Abd Al Karim Jaafar. "Economic Performance Classification in Iraq (2000–2023): A Statistical Analysis Using Machine Learning with Support Vector Machines and Random Forest." EDRAAK 2025 (February 1, 2025): 29–37. https://doi.org/10.70470/edraak/2025/005.

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This research investigates the classification of Iraq's economic performance from 2000 to 2023 using machine learning methodologies, specifically Support Vector Machines (SVM) and Random Forest (RF). The study aims to leverage macroeconomic indicators to develop predictive models for economic state classification. The dataset comprises six key variables gross domestic product, inflation rate, unemployment rate, exchange rate, trade volume, and government spending selected for their economic relevance. The methodology employs a composite Economic Performance Index for binary categorization ("Go

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De Dieu Hagenimana, Jean, and Djuma Sumbiri. "Benchmarking Machine Learning Models for Landslide Susceptibility: A Study in the Ngororero Sector." Journal of Information and Technology 5, no. 1 (2025): 16–24. https://doi.org/10.70619/vol5iss1pp16-24.

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This study evaluates the performance of six machine learning algorithms: Decision Trees (DT), Neural Networks (NN), Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting Machines (GBM), and k-nearest Neighbors (k-NN) for landslide prediction in the Ngororero sector, Rwanda. Using Sentinel-2 satellite imagery, meteorological data, and topographical datasets from 2015, 2019, and 2023, the study incorporates critical features such as slope, rainfall, soil type, and vegetation cover. The findings indicate significant temporal and algorithmic variations in prediction performance. K-

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Balakrishnan, Charumathi, and Mangaiyarkarasi Thiagarajan. "CREDIT RISK MODELLING FOR INDIAN DEBT SECURITIES USING MACHINE LEARNING." Buletin Ekonomi Moneter dan Perbankan 24 (March 8, 2021): 107–28. http://dx.doi.org/10.21098/bemp.v24i0.1401.

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We develop a new credit risk model for Indian debt securities rated by major credit rating agencies in India using the ordinal logistic regression (OLR). The robustness of the model is tested by comparing it with classical models available for ratings prediction. We improved the model’s accuracy by using machine learning techniques, such as the artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). We found that the accuracy of our model has improved from 68% using OLR to 82% when using ANN and above 90% when using SVM and RF.

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Farid, Traoré, Palé Sié, Zaré Aïda, Karamoko Traoré Moussa, Ouédraogo Blaise, and Bonkoungou Joachim. "A Comparative Analysis of Random Forest and Support Vector Machines for Classifying Irrigated Cropping Areas in The Upper-Comoé Basin, Burkina Faso." Indian Journal of Science and Technology 17, no. 8 (2024): 713–22. https://doi.org/10.17485/IJST/v17i8.78.

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Abstract <strong>Objectives:</strong> This study investigates the performance of two machine-learning algorithms in classifying land areas across the Upper-Comoé basin in Burkina Faso. <strong>Methods:</strong> Within the Google Earth Engine data processing environment, Support Vector Machine (SVM) and the Random Forest (RF) algorithms were applied to a Landsat-8 OLI image of March 2019, to discriminate agricultural land areas, with an emphasis on irrigated areas. <strong>Findings:</strong> The results indicated good to excellent classification performance, with

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Chen, Sijun. "Breast Cancer Prediction Based on RF-SVM." Applied and Computational Engineering 8, no. 1 (2023): 675–84. http://dx.doi.org/10.54254/2755-2721/8/20230293.

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Breast cancer prediction is crucial in identifying women who may be at risk for developing the disease. By doing the prediction, doctors can make the rapid diagnosis. Additionally, breast cancer prediction can also help guide research efforts and inform public health policies aimed at reducing the incidence and mortality of breast cancer. SVM (Support Vector Machine)is a classic method in machine learning, Random Forest is also widely used but they all have some shortcomings. Random Forest dont have high accuracy. So RF-SVM(Random Forest and Random Forest) is be chosen to do the prediction. Th

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Yao, Jian-Rong, and Jia-Rui Chen. "A New Hybrid Support Vector Machine Ensemble Classification Model for Credit Scoring." Journal of Information Technology Research 12, no. 1 (2019): 77–88. http://dx.doi.org/10.4018/jitr.2019010106.

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Credit scoring plays important role in the financial industry. There are different ways employed in the field of credit scoring, such as the traditional logistic regression, discriminant analysis, and linear regression; methods used in the field of machine learning include neural network, k-nearest neighbors, genetic algorithm, support vector machines (SVM), decision tree, and so on. SVM has been demonstrated with good performance in classification. This paper proposes a new hybrid RF-SVM ensemble model, which uses random forest to select important variables, and employs ensemble methods (bagg

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Dong, Longjun, Xibing Li, and Gongnan Xie. "Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification." Abstract and Applied Analysis 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/459137.

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The discrimination of seismic event and nuclear explosion is a complex and nonlinear system. The nonlinear methodologies including Random Forests (RF), Support Vector Machines (SVM), and Naïve Bayes Classifier (NBC) were applied to discriminant seismic events. Twenty earthquakes and twenty-seven explosions with nine ratios of the energies contained within predetermined “velocity windows” and calculated distance are used in discriminators. Based on the one out cross-validation, ROC curve, calculated accuracy of training and test samples, and discriminating performances of RF, SVM, and NBC were

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Saputra, Dimas Chaerul Ekty, Alfian Ma'arif, and Khamron Sunat. "Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification." Journal of Robotics and Control (JRC) 4, no. 6 (2024): 896–904. http://dx.doi.org/10.18196/jrc.v4i6.20898.

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This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Fore

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Sharma, Ram, and Keitarou Hara. "Characterization of Vegetation Physiognomic Types Using Bidirectional Reflectance Data." Geosciences 8, no. 11 (2018): 394. http://dx.doi.org/10.3390/geosciences8110394.

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This paper presents an assessment of the bidirectional reflectance features for the classification and characterization of vegetation physiognomic types at a national scale. The bidirectional reflectance data at multiple illumination and viewing geometries were generated by simulating the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters with Ross-Thick Li-Sparse-Reciprocal (RT-LSR) kernel weights. This research dealt with the classification and characterization of six vegetation physiognomic types—evergreen coniferous

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Prasanna, S. T. P., and T. Veeramani. "Comparing the Efficiency of Heart Disease Prediction using Novel Random Forest, Logistic Regression and Decision Tree And SVM Algorithms." CARDIOMETRY, no. 25 (February 14, 2023): 1491–99. http://dx.doi.org/10.18137/cardiometry.2022.25.14911499.

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Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calcu

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Darmawan, Aang, Ivana Yudhisari, Anwari Anwari, and Masdukil Makruf. "Pola Prediksi Kelulusan Siswa Madrasah Aliyah Swasta dengan Support Vector Machine dan Random Forest." Jurnal Minfo Polgan 12, no. 1 (2023): 387–400. http://dx.doi.org/10.33395/jmp.v12i1.12388.

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Kelulusan Siswa adalah salah satu indikator penting bagi kinerja keberhasilan sekolah. Prediksi kelulusan siswa penting bagi sekolah untuk mengidentifikasi siswa yang beresiko putus sekolah dan memberi mereka intervensi dini untuk meningkatkan kinerja akademik mereka. Ini juga dapat membantu pemangku kebijakan mengembangkan kebijakan dan program untuk meningkatkan tingkat kelulusan sekolah dan mengurangi tingkat putus sekolah. Akan tetapi berdasarkan penelusuran pustaka terdapat berbagai permasalahan krusial terkait prediksi kelulusan siswa yaitu sulitnya memprediksi secara akurat tingkat kelu

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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (2022): 5774. http://dx.doi.org/10.3390/rs14225774.

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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for

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Su, Chih-Jen, I.-Fei Chen, Tzong-Ru Tsai, Tzu-Hsuan Wang, and Yuhlong Lio. "Support Vector Machines and Model Selection for Control Chart Pattern Recognition." Mathematics 13, no. 4 (2025): 592. https://doi.org/10.3390/math13040592.

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Resource-intensiveness often occurs in modern industrial settings; meanwhile, common issues and irregular patterns in production can lead to defects and variations in work-piece dimensions, negatively impacting products and increasing costs. Utilizing traditional process control charts to monitor the process and identify potential anomalies is expensive when intensive resources are needed. To conquer these downsides, algorithms for control chart pattern recognition (CCPR) leverage machine learning models to detect non-normality or normality and ensure product quality is established, and novel

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Teja, P. P. S., and T. Veeramani. "Improving the Efficiency of Heart Disease Prediction Using Novel Random Forest Classifier Over Support Vector Machine Algorithm." CARDIOMETRY, no. 25 (February 14, 2023): 1468–76. http://dx.doi.org/10.18137/cardiometry.2022.25.14681476.

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Aim: The Aim of the research study is to see how accurate Novel Random Forest (RF) and Support Vector Machine (SVM) classification algorithms were in predicting heart disease.Materials and Methods: The RF Classifier is used to a 304-Record dataset with heart disease.A paradigm for heart disease prediction in the medical field has been presented and developed, comparing Novel Random Forest with SVM classifiers. The total number of images in the sample was 42, with 21 in each test group. Result:-The classifiers were evaluated, predictions and accuracy were supplied. Based on the information prov

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Tuğaç, Murat Güven, Fatih Fehmi Şimşek, and Harun Torunlar. "Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images." International Journal of Environment and Geoinformatics 11, no. 3 (2024): 106–18. http://dx.doi.org/10.30897/ijegeo.1479116.

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Monitoring crop development and mapping cultivated areas are important for reducing risks to food security due to climate change. Remote sensing techniques contribute significantly to the efficient and effective management of agricultural production. In this study, agricultural fields (sunflower, wheat, maize, oat, chickpea, sugar beet, alfalfa, onion, fallow) and other fields (non-agricultural, pasture, lake) were identified by using Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms with Sentinel-2 and Landsat-8 images in the area covering Polatlı, Haymana and G

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Agustinus, Bimo Gumelar, Yogatama Astri, Pramono Adi Derry, Frismanda, and Sugiarto Indar. "Forward feature selection for toxic speech classification using support vector machine and random forest." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 717–26. https://doi.org/10.11591/ijai.v11.i2.pp717-726.

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This study describes the methods for eliminating irrelevant features in speech data to enhance toxic speech classification accuracy and reduce the complexity of the learning process. Therefore, the wrapper method is introduced to estimate the forward selection technique based on support vector machine (SVM) and random forest (RF) classifier algorithms. Eight main speech features were then extracted with derivatives consisting of 9 statistical sub-features from 72 features in the extraction process. Furthermore, Python is used to implement the classifier algorithm of 2,000 toxic data collected

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Rahmah, Astriana, Nurhafiza Sepriyanti, Muhammad Hafis Zikri, Isnani Ambarani, and Muhammad Yusuf bin Shahar. "Implementation of Support Vector Machine and Random Forest for Heart Failure Disease Classification." Public Research Journal of Engineering, Data Technology and Computer Science 1, no. 1 (2023): 34–40. http://dx.doi.org/10.57152/predatecs.v1i1.816.

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Heart failure is a life-threatening disease and its management should be considered a global public health priority. The use of data mining in data processing operations to identify existing patterns and identify the information stored in them. In this study, researchers classify using two algorithms for comparison of algorithms, namely Random Forest (RF) and Support Vector Machine (SVM). The purpose of this study is to find patterns in finding the best accuracy for the 2 algorithms. The results of this study obtained an accuracy of 81.51%. with a Hold Out of 60 : 40% on the SVM algorithm, whi

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Meshram, Pritam, Kishan Singh Rawat, and Vinod Kumar Tripathi. "Sentinel-1A Data Analysis for Rice Classification Utilizing Random Forests and Support Vector Machine." Environment and Ecology 42, no. 4C (2024): 2037–43. https://doi.org/10.60151/envec/hyxu4637.

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Rice is known to be one of the most important crops in India and many other nations, particularly in Asia, therefore accurate rice area estimation has an important role in many activities, ranging from human nutrition to environmental concerns. As a result, the determination of cultivation area remains a hot topic among researchers from numerous disciplines, planners, and decision makers. Using Sentinel-1A SAR (Synthetic Aperture Radar) satellite data, this study attempts to evaluate the effectiveness of random forest (RF) and support vector machines (SVM) algorithms for rice crop classificati

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Gumelar, Agustinus Bimo, Astri Yogatama, Derry Pramono Adi, Frismanda Frismanda, and Indar Sugiarto. "Forward feature selection for toxic speech classification using support vector machine and random forest." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 717. http://dx.doi.org/10.11591/ijai.v11.i2.pp717-726.

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<span lang="EN-US">This study describes the methods for eliminating irrelevant features in speech data to enhance toxic speech classification accuracy and reduce the complexity of the learning process. Therefore, the wrapper method is introduced to estimate the forward selection technique based on support vector machine (SVM) and random forest (RF) classifier algorithms. Eight main speech features were then extracted with derivatives consisting of 9 statistical sub-features from 72 features in the extraction process. Furthermore, Python is used to implement the classifier algorithm of 2,

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Nti, Isaac Kofi, Adebayo Felix Adekoya, and Benjamin Asubam Weyori. "Efficient Stock-Market Prediction Using Ensemble Support Vector Machine." Open Computer Science 10, no. 1 (2020): 153–63. http://dx.doi.org/10.1515/comp-2020-0199.

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AbstractPredicting stock-price remains an important subject of discussion among financial analysts and researchers. However, the advancement in technologies such as artificial intelligence and machine learning techniques has paved the way for better and accurate prediction of stock-price in recent years. Of late, Support Vector Machines (SVM) have earned popularity among Machine Learning (ML) algorithms used for predicting stock price. However, a high percentage of studies in algorithmic investments based on SVM overlooked the overfitting nature of SVM when the input dataset is of high-noise a

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Dabija, Anca, Marcin Kluczek, Bogdan Zagajewski, et al. "Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping." Remote Sensing 13, no. 4 (2021): 777. http://dx.doi.org/10.3390/rs13040777.

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Land cover information is essential in European Union spatial management, particularly that of invasive species, natural habitats, urbanization, and deforestation; therefore, the need for accurate and objective data and tools is critical. For this purpose, the European Union’s flagship program, the Corine Land Cover (CLC), was created. Intensive works are currently being carried out to prepare a new version of CLC+ by 2024. The geographical, climatic, and economic diversity of the European Union raises the challenge to verify various test areas’ methods and algorithms. Based on the Corine prog

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Yen, Chih-Feng, He-Yen Hsieh, Kuan-Wu Su, Min-Chieh Yu, and Jenq-Shiou Leu. "Solar Power Prediction via Support Vector Machine and Random Forest." E3S Web of Conferences 69 (2018): 01004. http://dx.doi.org/10.1051/e3sconf/20186901004.

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Due to the variability and instability of photovoltaic (PV) output, the accurate prediction of PV output power plays a major role in energy market for PV operators to optimize their profits in energy market. In order to predict PV output, environmental parameters such as temperature, humidity, rainfall and win speed are gathered as indicators and different machine learning models are built for each solar panel inverters. In this paper, we propose two different kinds of solar prediction schemes for one-hour ahead forecasting of solar output using Support Vector Machine (SVM) and Random Forest (

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Matso, Nover M., Heherson B. Ong, and Emerson V. Barcellano. "Mapping and Estimating Forest Stand Volume using Machine Learning Methods and Multi-Spectral Sentinel 2 Data." European Journal of Theoretical and Applied Sciences 2, no. 2 (2024): 635–47. http://dx.doi.org/10.59324/ejtas.2024.2(2).55.

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Sustainable forest management necessitates the mapping and estimation of forest stand attributes such as density, volume, basal area, and aboveground biomass. This study was conducted to explore the potential of geographic information systems (GIS), remote sensing, machine learning, and field inventories to estimate the forest stand volume of natural and plantation forests within watersheds in the Abra River Basin. The common machine learning regression techniques, which are random forest (RF), k-nearest neighbors (KNN), and support vector machines (SVM), were used to model and predict forest

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Nover, M. Matso, B. Ong Heherson, and V. Barcellano Emerson. "Mapping and Estimating Forest Stand Volume using Machine Learning Methods and Multi-Spectral Sentinel 2 Data." European Journal of Theoretical and Applied Sciences 2, no. 2 (2024): 635–47. https://doi.org/10.59324/ejtas.2024.2(2).55.

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Sustainable forest management necessitates the mapping and estimation of forest stand attributes such as density, volume, basal area, and aboveground biomass. This study was conducted to explore the potential of geographic information systems (GIS), remote sensing, machine learning, and field inventories to estimate the forest stand volume of natural and plantation forests within watersheds in the Abra River Basin. The common machine learning regression techniques, which are random forest (RF), k-nearest neighbors (KNN), and support vector machines (SVM), were used to model and predict forest

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Muneer, Amgad, Rao Faizan Ali, Amal Alghamdi, Shakirah Mohd Taib, Ahmed Almaghthawi, and Ebrahim Abdulwasea Abdullah Ghaleb. "Predicting customers churning in banking industry: A machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 539–49. https://doi.org/10.11591/ijeecs.v26.i1.pp539-549.

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In this era, machines can understand human activities and their meanings. We can utilize this ability of machines in various fields or applications. One specific field of interest is a prediction of churning customers in any industry. Prediction of churning customers is the state of art approach which predicts which customer is near to leave the services of the specific bank. We can use this approach in any big organization that is very conscious about their customers. However, this study aims to develop a model that offers a meaningful churn prediction for the banking industry. For this purpo

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Suman Kumar Swarnkar. "Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images." Journal of Electrical Systems 20, no. 3s (2024): 645–53. http://dx.doi.org/10.52783/jes.1346.

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This study investigates the application of convolutional neural networks (CNNs) and traditional machine learning algorithms for the early detection and classification of Alzheimer's disease (AD) using brain Magnetic Resonance Imaging (MRI) data. We compare the performance of CNNs with Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) on a dataset comprising MRI images from AD patients and healthy controls. Results show that CNNs achieved the highest accuracy (90.2%) and area under the receiver operating characteristic curve (AUC-ROC) of 0.95, outperforming

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Sujaini, Herry. "Klasifikasi Citra Alat Musik Tradisional dengan Metode k-Nearest Neighbor, Random Forest, dan Support Vector Machine." JURNAL SISTEM INFORMASI BISNIS 9, no. 2 (2019): 185. http://dx.doi.org/10.21456/vol9iss2pp185-191.

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Dalam dekade terakhir, metode non-parametrik (algoritma berbasis pembelajaran mesin) semakin banyak dipergunakan dari berbagai aplikasi berbasis pengolahan citra digital. Penelitian ini bertujuan untuk membandingkan tiga metode non-parametrik yaitu Metode k-Nearest Neighbor (kNN), Random Forest (RF), dan Support Vector Machine (SVM) terhadap klasifikasi citra alat musik tradisional di Indonesia yang populer di kalangan masyarakat yaitu : angklung, djembe, gamelan, gong, gordang, kendang, kolintang, rebana, sasando, dan serunai. Dari hasil eksperimen pengklasifikasian dengan metode kNN, RF dan

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Gigović, Ljubomir, Hamid Reza Pourghasemi, Siniša Drobnjak, and Shibiao Bai. "Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park." Forests 10, no. 5 (2019): 408. http://dx.doi.org/10.3390/f10050408.

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The main objectives of this paper are to demonstrate the results of an ensemble learning method based on prediction results of support vector machine and random forest methods using Bayesian average. In this study, we generated susceptibility maps of forest fire using supervised machine learning method (support vector machine—SVM) and its comparison with a versatile machine learning algorithm (random forest—RF) and their ensembles. In order to achieve this, first of all, a forest fire inventory map was constructed using Serbian historical forest fire database, Moderate Resolution Imaging Spect

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Kong, Chunfang, Yiping Tian, Xiaogang Ma, Zhengping Weng, Zhiting Zhang, and Kai Xu. "Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi." Remote Sensing 13, no. 18 (2021): 3573. http://dx.doi.org/10.3390/rs13183573.

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Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide di

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Garzón Barrero, Julián, Nancy Estela Sánchez Pineda, and Darío Fernando Londoño Pinilla. "Evaluación comparativa de los algoritmos de aprendizaje automático Support Vector Machine y Random Forest." Ciencia e Ingeniería Neogranadina 33, no. 2 (2023): 131–48. http://dx.doi.org/10.18359/rcin.6996.

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En el presente estudio se examinó el rendimiento de los algoritmos Support Vector Machine (SVM) y Random Forest (RF) utilizando un modelo de segmentación de imágenes basado en objetos (OBIA) en la zona metropolitana de Barranquilla, Colombia. El propósito fue investigar de qué manera los cambios en el tamaño de los conjuntos de entrenamiento y el desequilibrio en las clases de cobertura terrestre influyen en la precisión de los modelos clasificadores. Los valores del coeficiente Kappa y la precisión general revelaron que svm superó consistentemente a RF. Además, la imposibilidad de calibrar ci

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Xu, Zhanghua, Qi Zhang, Songyang Xiang, et al. "Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection." Forests 13, no. 3 (2022): 418. http://dx.doi.org/10.3390/f13030418.

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In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a detection model to study the damage caused to Moso bamboo forests by Pantana phyllostachysae Chao (PPC), a major leaf-eating pest, at 5 cm resolution. Damage sensitive features were extracted from multispectral images acquired by UAVs and used to train detection models based on support vector machines (SVM), random forests (RF), and extreme gradient

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Prodromou, Maria, Ioannis Gitas, Christodoulos Mettas, Marios Tzouvaras, Chris Danezis, and Diofantos Hadjimitsis. "Comparative Analysis of Supervised Machine Learning Algorithms for Forest Habitat Mapping in Cyprus." Sustainability 17, no. 13 (2025): 6021. https://doi.org/10.3390/su17136021.

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Mapping dominant forest habitats is essential for guiding reforestation practices, especially in areas affected by fires. This study focuses on identifying dominant forest habitats in selected forested areas in Cyprus using supervised, pixel-based classification algorithms to support the planning of post-fire reforestation actions. For this study, three classifiers were provided by the Google Earth Engine (GEE) platform. Specifically, the Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART) were implemented utilizing Sentinel-1 and Sentinel-2 data as

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Reddy, Patlolla Varshini, Mr Y. Manohar Reddy, Rathod Praveen, and Mohammad Asif. "INNOVATIVE APPROACHES TO MALICIOUS URL DETECTION: USING MACHINE LEARNING UNLEASHED." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39918.

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The proliferation of malicious URLs presents significant challenges to cyber security, necessitating the development of advanced detection techniques. Using the capabilities of Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) models, this study investigates novel machine learning techniques for identifying dangerous URLs. The effectiveness of each model in differentiating between benign and malicious URLs is assessed, taking into account a range of performance indicators including accuracy, precision, recall, and F1-score. The integration of feature extraction technique

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Harish, P., and Dr R. Sabitha. "Improving the Efficiency of Heart Disease Prediction Using SVM and a Novel Tree Specific Random Forest Classifier (NTSRF)." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 616–22. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21087.

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Aim: The objective of the work is to evaluate the accuracy and precision in predicting the heart disease using Support Vector Machine (SVM) and Random Forest (RF) classification algorithms. Materials and Methods: Random Forest Classifier is applied on a Health dataset that consists of 304 records. A framework for heart disease prediction in the medical sector comparing Random Forest and SVM classifiers has been proposed and developed. The sample size was measured as 21 per group. The accuracy and the precision of the classifiers was evaluated and recorded. Results: The SVM classifier produces

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Zuhria, Lailatuz, and Azwar Riza Habibi. "Comparative Analysis of Random Forest and SVM Performance in Asthma Prediction." sinkron 9, no. 1 (2025): 347–56. https://doi.org/10.33395/sinkron.v9i1.14346.

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This study evaluates the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms in predicting asthma risk to identify the most suitable method for medical datasets. Key metrics include training time, testing time, forecasting time, error rate, and accuracy. The datasets involve attributes such as age and clinical factors, analyzed in three stages: training, testing, and forecasting. During training, SVM demonstrated faster processing, requiring a maximum of 0.5323 seconds compared to RF's 3.7736 seconds on 90% training data. In testing, RF excelled in speed, achieving a

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Patil, Abhijit, and Sachin Panhalkar. "comparative analysis of machine learning algorithms for land use and land cover classification using google earth engine platform." Journal of Geomatics 17, no. 2 (2023): 111–18. http://dx.doi.org/10.58825/jog.2023.17.2.96.

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This study evaluates different machine learning algorithms for land use and land cover classification using Sentinel-2 Level-1C data with 10-meter spatial resolution. The algorithms include Random Forest (RF), Classification and Regression Trees (CART), Support Vector Machines (SVM), Naive Bayes (NB), and Gradient Boosting (GTB). The classification was performed on the Google Earth Engine (GEE) platform. Results highlight variations in land cover classification among algorithms, with RF and CART identifying cropland as dominant, SVM indicating fallow land presence, NB revealing significant for

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Uppalapati, Harshitha, Bhuvitha Vallamkondu, and Poornima Dr. "Deep Learning-Driven MRI Image Segmentation and Classification for Brain Tumors Using RF, SVM, YOLOv5, and U-Net Architectures." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 2 (2025): 1478–83. https://doi.org/10.5281/zenodo.14964479.

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This work uses deep learning and machine learning approaches to identify and categorize brain cancers from MRI scans. U-Net is utilized for precise tumor segmentation, YOLOv5 is employed for real-time detection, and Random Forest (RF) and Support Vector Machines (SVM) are employed for tumor type classification. In order to assist doctors, in diagnosing brain tumors more rapidly, the system aims to automate segmentation, detection, and classification, improve diagnosis accuracy, and reduce analysis time. By developing early intervention strategies for brain tumor treatment, this study enhances

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Ahmad, Yahya Dawod, and Ali Sharafuddin Mohammed. "Assessing mangrove deforestation using pixel-based image: a machine learning approach." Bulletin of Electrical Engineering and Informatics 10, no. 6 (2021): 3178–90. https://doi.org/10.11591/eei.v10i6.3199.

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Mangrove is one of the most productive global forest ecosystems and unique in linking terrestrial and marine environment. This study aims to clarify and understand artificial intelligence (AI) adoption in remote sensing mangrove forests. The performance of machine learning algorithms such as random forest (RF), support vector machine (SVM), decision tree (DT), and objectbased nearest neighbors (NN) algorithms were used in this study to automatically classify mangrove forests using orthophotography and applying an object-based approach to examine three features (tree cover loss, aboveground car

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