Relevant bibliographies by topics / Random Forest (RF) and Support Vector Machines (SVM)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Random Forest (RF) and Support Vector Machines (SVM)'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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

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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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Khasan Galuh Ramadhan, Gentur Wahyu Nyipto Wibowo, and Nadia Annisa Maori. "KOMPARASI DETEKSI PENYAKIT GINJAL KRONIS MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST." Jurnal Informatika dan Rekayasa Elektronik 8, no. 1 (2025): 13–21. https://doi.org/10.36595/jire.v8i1.1288.

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Penyakit Ginjal Kronis (PGK) adalah masalah kesehatan global yang signifikan dan seringkali tidak terdeteksi hingga mencapai stadium lanjut. PGK juga menjadi salah satu Penyakit Tidak Menular (PTM) penyebab kematian terbanyak dalam lingkup global. Oleh karena itu, deteksi dini PGK sangat penting untuk mencegah risiko komplikasi. Studi ini membandingkan dua algoritma klasifikasi, yaitu Support Vector Machine (SVM) dan Random Forest (RF). Algoritma SVM dikenal karena tingkat akurasinya yang tinggi, efisien dalam penggunaan memori, dan kemampuannya untuk menangani data dengan distribusi yang tida
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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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Dissertations / Theses on the topic "Random Forest (RF) and Support Vector Machines (SVM)"

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Kinalwa-Nalule, Myra. "Using machine learning to determine fold class and secondary structure content from Raman optical activity and Raman vibrational spectroscopy." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/using-machine-learning-to-determine-fold-class-and-secondary-structure-content-from-raman-optical-activity-and-raman-vibrational-spectroscopy(7382043d-748c-4d29-ba75-67fb35ccdb19).html.

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The objective of this project was to apply machine learning methods to determine protein secondary structure content and protein fold class from ROA and Raman vibrational spectral data. Raman and ROA are sensitive to biomolecular structure with the bands of each spectra corresponding to structural elements in proteins and when combined give a fingerprint of the protein. However, there are many bands of which little is known. There is a need, therefore, to find ways of extrapolating information from spectral bands and investigate which regions of the spectra contain the most useful structural i
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Book chapters on the topic "Random Forest (RF) and Support Vector Machines (SVM)"

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Bartz-Beielstein, Thomas, and Martin Zaefferer. "Models." In Hyperparameter Tuning for Machine and Deep Learning with R. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5170-1_3.

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AbstractThis chapter presents a unique overview and a comprehensive explanation of Machine Learning (ML) and Deep Learning (DL) methods. Frequently used ML and DL methods; their hyperparameter configurations; and their features such as types, their sensitivity, and robustness, as well as heuristics for their determination, constraints, and possible interactions are presented. In particular, we cover the following methods: $$k$$ k -Nearest Neighbor (KNN), Elastic Net (EN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and DL. This cha
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Hojaji, Fazilat, Adam J. Toth, and Mark J. Campbell. "A Machine Learning Approach for Modeling and Analyzing of Driver Performance in Simulated Racing." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_8.

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AbstractThe emerging progress of esports lacks the approaches for ensuring high-quality analytics and training in professional and amateur esports teams. In this paper, we demonstrated the application of Artificial Intelligence (AI) and Machine Learning (ML) approach in the esports domain, particularly in simulated racing. To achieve this, we gathered a variety of feature-rich telemetry data from several web sources that was captured through MoTec telemetry software and the ACC simulated racing game. We performed a number of analyses using ML algorithms to classify the laps into the performanc
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John, Vivek, Ashulekha Gupta, Saurabh Aggarwal, Kawerinder Singh Sidhu, Kapil Joshi, and Omdeep Gupta. "Random Forest (RF) Assisted and Support Vector Machine (SVM) Algorithms for Performance Evaluation of EDM Interpretation." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8129-8_20.

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Xin, Cun, Dangfeng Yang, Xiaodong Liu, Yong Huang, and Xueming Qian. "Research on Dam Crack Identification Method Based on Multi-source Information Fusion." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-9184-2_1.

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AbstractCracks as the main safety concern of dams, high-precision identification of dam cracks is of great application value and scientific significance to ensure the safety of dams. The paper proposes a dam crack identification method based on multi-source information fusion. Specifically, image gray scale and geometric features are extracted based on the image information. And then a single crack identification model based on Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), XGBoost, and BP Neural Network are established based on the features, respectively. Finally, a mul
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Reddy, Nerusupalli Dinesh Kumar, Ashok Kumar Gupta, and Anil Kumar Sahu. "The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSO) to Predict the Soil Liquefaction." In Disaster Risk Reduction. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7707-9_16.

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Zhang, Jingxiang, Siyu Qian, Guoxin Su, Chao Deng, and Ping Yu. "Predicting Readmission Following Hospital Treatment for Patients with Alcohol Related Diagnoses in an Australian Regional Health District." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220273.

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This study aims to investigate the prediction of hospital readmission of alcohol use disorder patients within 28 days of discharge and compare the performance of six machine learning methods i.e., random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM.
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Phiri, Moses, Lebotsa Daniel Metsileng, and Mogari Ishmael Rapoo. "The Implications of Missing Data on the Stability of Random Forest and Support Vector Machine Model's Output." In Advances in Finance, Accounting, and Economics. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-8507-4.ch016.

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The study utilized two machine learning (ML) techniques namely Random Forest (RF) and Support Vector Machines (SVM) with the aim of understanding the implications of missing data on RF and SVM's ability in forecasting accuracy. The study employed a time series data sourced from South African Reserve Bank website from January 1960 to June 2021 with a total of 738 observations for variables of government expenditure as a dependent variable and government revenue as independent variable. The study employed RMSE, MSE, MAE and MAPE for measuring forecasting accuracy of these two machine learning mo
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Alleema, N. Noor, Amar Choudhary, Siddhi Nath Rajan, Rakesh Kancharla, Rakshit Kothari, and Rakesh Kumar. "A Machine Learning-Based Predictive Model for Drug Sensitivity in Breast Cancer Using Gene Expression Data." In Advances in Healthcare Information Systems and Administration. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1662-7.ch008.

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Through the combination of tool learning patterns, this study offers a novel strategy for personalised treatment for the majority of breast malignancies. The authors used a carefully assembled dataset that included 3444 cases of drug management data, affected person profiles, diagnostic scans, and scientific reviews to train artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and random forests (RF) for drug sensitivity prediction modelling. While SVM demonstrated its capacity to handle high-dimensional statistics with an accuracy of 96.5%, the artificial neur
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Ishankulov, Timur, Fyodor Grebenev, Uliya Strunina, Oleg Shekhtman, Shalva Eliava, and Gleb Danilov. "The Prediction of Functional Outcome After Microsurgical Treatment of Unruptured Intracranial Aneurysm Based on Machine Learning." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220503.

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Our study aimed to create a machine learning model to predict patients’ functional outcomes after microsurgical treatment of unruptured intracranial aneurysms (UIA). Data on 615 microsurgically treated patients with UIA were collected retrospectively from the Electronic Health Records at N.N. Burdenko Neurosurgery Center (Moscow, Russia). The dichotomized modified Rankin Scale (mRS) at the discharge was used as a target variable. Several machine learning models were utilized: a random forest upon decision trees (RF), logistic regression (LR), support vector machine (SVM). The best result with
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Al-Sabur, Raheem, Akshansh Mishra, and Hassanein I. Khalaf. "Mastering Friction Stir Welding (FSW) With Machine Learning (ML)." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7974-5.ch017.

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This chapter introduces machine learning (ML) in friction stir welding (FSW), a solid-state welding process that has gained significant attention in research and application. The chapter discusses five primary ML methods: artificial neural networks (ANNs), support vector machines (SVM), random forests (RF), particle swarm optimisation (PSO), and convolutional neural networks (CNNs). The chapter emphasizes the successful application of ANNs in optimizing FSW process parameters and predicting tool wear, tensile failure, and fracture positions. CNNs are shown to be effective for microstructure st
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Conference papers on the topic "Random Forest (RF) and Support Vector Machines (SVM)"

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Seto, Emily, Meifeng Li, and Jing Liu. "Predicting Corrosion Severity of Pipeline Steels in Supercritical CO2 Environments Using Supervised Machine Learning." In CONFERENCE 2024. AMPP, 2024. https://doi.org/10.5006/c2024-20803.

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Abstract The importance of effective corrosion management in carbon capture, utilization, and storage (CCUS) networks has significantly increased. Captured CO2 is often transported in the supercritical state (s-CO2) and can contain impurities like H2O, O2, SOx, or NOx. While repurposing existing oil and gas pipelines for s-CO2 transport has been suggested, further testing and risk assessment is required to validate this strategy and its associated risks. Given the substantial amount of corrosion data available from recent corrosion studies, machine learning (ML) has emerged as a promising tool
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Vieira, Ronald E., Farzin Darihaki, Jamie Li, and Siamack A. Shirazi. "Application of Machine Learning Techniques for Sand Erosion Prediction for Elbows in Multiphase Flow." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18995.

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Abstract The aim of this work is to define, implement, test, and validate an AI methodology using existing machine learning (ML) algorithms to predict sand erosion in 90° elbows for a broad range of multiphase operating conditions. Based on information obtained from the experimental UT wall thickness loss data collected for different flow regimes (gas-sand, liquid-sand, dispersed-bubble, churn, annular, and low liquid loading multiphase flows), the methodology has been developed to predict the maximum erosion magnitudes in standard metallic elbows. In order to expand the range of application o
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Agarwal, Abhishek, Pradeep Rathore, Vinay Jain, and Beena Rai. "In-silico Model for Predicting the Corrosion Inhibition Efficiency of Steel Inhibitors." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13329.

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Abstract Quantitative Structure-Activity Relationships (QSAR) based models have been widely used for predicting corrosion inhibition performance of metals. However, one of the major limitations in these studies is that the authors have restricted themselves to use only a single class of molecules having similar molecular structure. In this study, a computational end-to-end framework was developed to investigate the properties of organic corrosion inhibitors which are responsible for inhibition of steel in acidic solution. The framework consists of modules like data preprocessing, descriptor se
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Pirić, David, and Romana Masnikosa. "PERFORMANCE OF RANDOM FORESTS, EXTREME GRADIENT BOOSTING AND SUPPORT VECTOR MACHINES EMPLOYED IN LIPIDOMICS." In 17th International Conference on Fundamental and Applied Aspects of Physical Chemistry. Society of Physical Chemists of Serbia, 2024. https://doi.org/10.46793/phys.chem24i.223p.

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Herein we present the performance of three supervised machine learning (ML) algorithms: random forests (RF), extreme gradient boosting (XGB) and support vector machines (SVM) in classification of human serum samples into pancreatic cancer or control group, using a lipidomic dataset retrieved from the research article „Lipidomic profiling of human serum enables detection of pancreatic cancer“ by Wolrab et al. [1]. Our main objective was to assess and compare, for the three ML techniques, the performance metrics, that is accuracy, precision, sensitivity, F1 score and ROC- AUC, with those compute
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Irawansyah, Adiwijaya, and Widi Astuti. "Comparative Analysis of Support Vector Machine (SVM) and Random Forest (RF) Classification for Cancer Detection using Microarray." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527458.

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Seixas, Jordana, Ailton Leite, Rodrigo de Paula, and Sérgio Murilo Maciel Fernandes. "Reconhecimento de comandos de voz com e sem disartria usando extração de características da fala MFCC e algoritmos de aprendizagem de máquina." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbcas.2023.229708.

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A fala disártrica está entre os problemas para articular e pronunciar bem as palavras devido aos danos no sistema neurológico responsável pela fala. Este estudo investiga se os classificadores de aprendizagem de máquina reconhecem quais palavras as pessoas com e sem disartria falam, aplicando uma técnica de extração de características da fala chamada MFCC (Mel Frequency Cepstral Coefficients). Os classificadores Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) e KNearest Neighbor (KNN) foram testados. O conjunto de dados UASpeech foi usado nos modelos, contendo
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Sousa, Ronieri Nogueira de, Roney Nogueira de Sousa, Rhyan Ximenes de Brito, and Janaide Nogueira de Sousa Ximenes. "Utilização de Modelos Computacionais Baseados em Classificadores Para Predição da Dislexia em Crianças." In Encontro Unificado de Computação do Piauí. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/enucompi.2021.17761.

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A dislexia é uma das dificuldades de aprendizagem mais comum nas salas de aula. Dessa forma o estudo teve como finalidade a classificação de crianças com ou sem dislexia através da aplicação de técnicas de Inteligência Computacional (IC). Para a metodologia utilizou-se de uma base de dados pública e da aplicação das arquiteturas neurais, Multilayer Perceptron (MLP), Radial Basis Function (RBF) e Extreme Learning Machine (ELM) e dos classificadores estatísticos, Support Vector Machine (SVM), Random Forest (RF) e K-Nearest Neighbors (K-NN), assim como das técnicas k-fold, SMOTE e normalização z-
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Pirić, David, and Romana Masnikosa. "Assessment of different machine learning tools employed in lipidomics." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.330p.

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Herein we present the potential of four machine learning (ML) algorithms: Partial Least Squares – Discriminant Analysis (PLS-DA), Random Forests (RF), Support Vector Machines (SVM) and Decision Trees (DT) to classify human plasma samples into cancer or control group, using a lipidomic dataset retrieved from the research article „Lipidomic profiling of human serum enables detection of pancreatic cancer“ by Wolrab et al. [1]. Our main objective was to assess and compare, for the four ML techniques, the performance metrics, that is accuracy, precision, sensitivity, F1 score and ROC-AUC, with thos
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Sousa, Roney Nogueira de, Ronieri Nogueira de Sousa, Rhyan Ximenes de Brito, and Janaide Nogueira de Sousa Ximenes. "Modelo de Previsão com Regressão Polinomial Para Casos de COVID-19 na Cidade de Tianguá-CE Através dos Classificadores Support Vector Machine e Random Forest." In Escola Regional de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/ercas.2021.17427.

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A COVID-19 é um dos maiores problemas de saúde pública enfrentados no Brasil e no mundo atualmente. Para essa pesquisa utilizou-se de dados disponibilizados pela plataforma Brasil.io. Dessa forma usou-se de algoritmos com regressão polinomial para predizer os casos de COVID-19, com base no treinamento e teste a partir dos dados extraidos dos boletins diários que são fornecidos pela Secretaria de Saúde do Município. Os resultados obtidos foram satisfatórios visto que foram próximos aos observados na realidade. Assim, o classificador Random Forest (RF) obteve os melhores resultados com 83,70% de
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Costa, Bernardo S., Aiko C. S. Bernardes, Julia V. A. Pereira, et al. "Artificial Intelligence in Automated Sorting in Trash Recycling." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4416.

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A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy around 93%.
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Reports on the topic "Random Forest (RF) and Support Vector Machines (SVM)"

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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