Relevant bibliographies by topics / Random Forest (RF) and Hybrid Scikit algorithms

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Academic literature on the topic 'Random Forest (RF) and Hybrid Scikit algorithms'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Random Forest (RF) and Hybrid Scikit algorithms"

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Mondol, S. I. M. M. Raton, Ryul Kim, and Sangmin Lee. "Hybrid Machine Learning Framework for Multistage Parkinson’s Disease Classification Using Acoustic Features of Sustained Korean Vowels." Bioengineering 10, no. 8 (2023): 984. http://dx.doi.org/10.3390/bioengineering10080984.

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Recent research has achieved a great classification rate for separating healthy people from those with Parkinson’s disease (PD) using speech and the voice. However, these studies have primarily treated early and advanced stages of PD as equal entities, neglecting the distinctive speech impairments and other symptoms that vary across the different stages of the disease. To address this limitation, and improve diagnostic precision, this study assesses the selected acoustic features of dysphonia, as they relate to PD and the Hoehn and Yahr stages, by combining various preprocessing techniques and
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Wang, Junsheng. "The Comparsion of Stock Return Prediction for Random Forest, Ordinary Least Square, and XGBoost." BCP Business & Management 26 (September 19, 2022): 686–95. http://dx.doi.org/10.54691/bcpbm.v26i.2028.

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With the stock market growing larger and the violent fluctuation becoming more frequent after the COVID-19 pandemic broke out, investors and researchers urgently need a method to predict the behavior of the stock market accurately. This research is determined to find out the performance of random forest (RF), XGBoost and ordinary least square (OLS) models in terms of predicting the return of given subjects. This research uses tushare to collect data and Jupyter Notebook to run the models. Libraries such as numpy, pandas, scikit-learn, and stockstats are also used in this paper. According to th
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Aziz, Chya Fatah, and Banan Jamil Awrahman. "Prediction Model based on Iris Dataset Via Some Machine Learning Algorithms." Journal of Kufa for Mathematics and Computer 10, no. 2 (2023): 64–69. http://dx.doi.org/10.31642/jokmc/2018/100210.

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Abstract— Supervised Machine Learning algorithm has an important approach to Classification. We are predicting the deal type of the Iris plant using various algorithms of machine learning. Iris plants are determined by numerous factors such as the size of the length and width of the property. A horticultural skill announces that some of the plants are different in some physical appearances like size, shape, and color. Hence it is difficult to recognize any species. Versicolor, Setosa, and Virginica have three identical subspecies of The Iris flower species. This paper uses machine learning alg
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Yan, Miaomiao, and Yindong Shen. "Traffic Accident Severity Prediction Based on Random Forest." Sustainability 14, no. 3 (2022): 1729. http://dx.doi.org/10.3390/su14031729.

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The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO). In the proposed model, BO-RF, RF is adopted as a basic predictive model and BO is used to tune the parameters of RF. Experimental results show that BO-RF achieves higher accuracy than conventional algorithms. Moreover, BO-RF provides interpretable results by relative importance and a partial dependence plot. We can identify important influen
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Sewpaul, Ronel, Olushina Olawale Awe, Dennis Makafui Dogbey, Machoene Derrick Sekgala, and Natisha Dukhi. "Classification of Obesity among South African Female Adolescents: Comparative Analysis of Logistic Regression and Random Forest Algorithms." International Journal of Environmental Research and Public Health 21, no. 1 (2023): 2. http://dx.doi.org/10.3390/ijerph21010002.

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Background: This study evaluates the performance of logistic regression (LR) and random forest (RF) algorithms to model obesity among female adolescents in South Africa. Methods: Data was analysed on 375 females aged 15–17 from the South African National Health and Nutrition Examination Survey 2011/2012. The primary outcome was obesity, defined as body mass index (BMI) ≥ 30 kg/m2. A total of 31 explanatory variables were included, ranging from socio-economic, demographic, family history, dietary and health behaviour. RF and LR models were run using imbalanced data as well as after oversampling
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Adugna, Tesfaye, Wenbo Xu, and Jinlong Fan. "Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images." Remote Sensing 14, no. 3 (2022): 574. http://dx.doi.org/10.3390/rs14030574.

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The type of algorithm employed to classify remote sensing imageries plays a great role in affecting the accuracy. In recent decades, machine learning (ML) has received great attention due to its robustness in remote sensing image classification. In this regard, random forest (RF) and support vector machine (SVM) are two of the most widely used ML algorithms to generate land cover (LC) maps from satellite imageries. Although several comparisons have been conducted between these two algorithms, the findings are contradicting. Moreover, the comparisons were made on local-scale LC map generation e
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Nagpal, Arpita, and Vijendra Singh. "Coupling Multivariate Adaptive Regression Spline (MARS) and Random Forest (RF)." International Journal of Healthcare Information Systems and Informatics 14, no. 1 (2019): 1–18. http://dx.doi.org/10.4018/ijhisi.2019010101.

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In this article, a new algorithm to select the relevant features is proposed for handling microarray data with the specific aim of increasing classification accuracy. In particular, the optimal genes are extracted using filter and wrapper feature selection algorithms. Here, the use of non-parametric regression algorithm called Multivariate Adaptive Regression Spline (MARS) followed by proposed Random Forest Statistical Test (RFST) algorithm are being studied. The study evaluates the comparative performance of the results of RFST and MARS with existing algorithms on ten standard microarray data
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., Mehvish, and Ravinder Pal Singh. "Random Forest and Extreme Learning Machine Algorithms for High Accuracy Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 892–95. http://dx.doi.org/10.22214/ijraset.2023.55752.

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Abstract: The banking sector is facing a huge issue with credit card fraud, and research has shown that machine learning algorithms are a useful tool for identifying fraudulent actions of this kind. In this investigation, we offer a method for detecting fraudulent use of credit cards that makes use of a hybrid of two machine learning algorithms known as Random Forest (RF) and Extreme Learning Machine (ELM). We compiled a dataset using information obtained from a wide variety of sources, and then we preprocessed it to eliminate any inconsistencies and errors. Following this, the RF and ELM algo
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Melesse, Assefa M., Khabat Khosravi, John P. Tiefenbacher, et al. "River Water Salinity Prediction Using Hybrid Machine Learning Models." Water 12, no. 10 (2020): 2951. http://dx.doi.org/10.3390/w12102951.

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Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% fro
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Md Afendi, Muhamad Amirul Sadikin, and Marina Yusoff. "A sound event detection based on hybrid convolution neural network and random forest." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (2022): 121. http://dx.doi.org/10.11591/ijai.v11.i1.pp121-128.

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Sound event detection (SED) assists in the detainment of intruders. In recent decades, several SED methods such as support vector machine (SVM), K-Means clustering, principal component analysis, and convolution neural network (CNN) on urban sound have been developed. Advanced work on SED in a rare sound event is challenging because it has limited exploration, especially for surveillance in a forest environment. This research provides an alternative method that uses informative features of sound event data from a natural forest environment and evaluates the CNN capabilities of the detection per
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Book chapters on the topic "Random Forest (RF) and Hybrid Scikit algorithms"

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Verma, Jyoti, Isha Kansal, Renu Popli, et al. "A Hybrid Images Deep Trained Feature Extraction and Ensemble Learning Models for Classification of Multi Disease in Fundus Images." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_14.

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AbstractRetinal disorders, including diabetic retinopathy and macular degeneration due to aging, can lead to preventable blindness in diabetics. Vision loss caused by diseases that affect the retinal fundus cannot be reversed if not diagnosed and treated on time. This paper employs deep-learned feature extraction with ensemble learning models to improve the multi-disease classification of fundus images. This research presents a novel approach to the multi-classification of fundus images, utilizing deep-learned feature extraction techniques and ensemble learning to diagnose retinal disorders an
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Muthurajkumar, S., G. Kajeeth Kumar, and S. T. P. Mohana Priya. "Crayfish-Optimized CNN and Random Forest for Effective Plant Disease Detection." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8019-2.ch007.

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Plant disease detection is crucial for ensuring agricultural productivity and food security. This work presents a new hybrid model that combines a Convolutional Neural Network (CNN) with a Random Forest (RF) to increase the accuracy and efficiency of plant disease identification. Additionally, the Crayfish Optimization Algorithm (COA) is utilized to fine-tune the hyperparameters of the CNN, improving its ability to extract relevant features from crop images. Then, these features are fed into a RF leverages its robustness and interpretability to classify the presence of diseases. Extensive expe
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Agiwal, Yamini, Anurag Bhatnagar, and Nikhar Bhatnagar. "PYTHON ENSEMBLE LEARNING FOR EARLY CARDIOVASCULAR DISEASE DIAGNOSIS." In Futuristic Trends in Information Technology Volume 3 Book 2. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bfit2p8ch1.

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The early detection of heart disease based on symptoms is a major challenge in today's world, particularly in developing countries where access to specialized heart doctors is limited in remote and rural areas. To tackle this issue, researchers have proposed a hybrid decision support system that aids in the early detection of heart disease using clinical parameters of patients. In recent years, there has been a growing emphasis on predicting cardiovascular disease using data-driven techniques and machine learning algorithms. Early detection of cardiovascular disease poses a significant challen
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Parvathala, Balakesava Reddy, A. Manikandan, P. Vijayalakshmi, M. Muzammil Parvez, S. Harihara Gopalan, and S. Ramalingam. "Bio-Inspired Metaheuristic Algorithm for Network Intrusion Detection System of Architecture." In Bio-Inspired Intelligence for Smart Decision-Making. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5276-2.ch004.

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By identifying different kinds of attacks and application misuse that firewalls normally aren't able to identify, network intrusion detection systems (IDS) are intended to keep computer networks safe. When creating a network intrusion detection system, feature selection techniques are crucial. Several bionic meta-heuristic algorithms are used to quickly categorize network traffic as problematic or normal, then decrease features to demonstrate higher accuracy. Thus, in order to detect frequent attacks, this research proposes a hybrid model of network intrusion detection system (IDS) based on an
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Conference papers on the topic "Random Forest (RF) and Hybrid Scikit algorithms"

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Bashir, Ahmed, Ahmed Kasha, Shirish Patil, Murtada Saleh Aljawad, and Muhammad Shahzad Kamal. "Extensive Study on the Influencing Parameters of Sc CO2 Foam Viscosity for Enhanced Oil Recovery and Carbon Sequestration: A Machine Learning Approach." In GOTECH. SPE, 2024. http://dx.doi.org/10.2118/219163-ms.

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Abstract Foam flooding has been used to control gas mobility during oil displacement and CO2 sequestration processes in subsurface porous media, mitigating the negative impacts of low gas viscosity, reservoir heterogeneity, and gravity override. In this research, we study the application of machine learning (ML) to develop a data-driven prediction of the effective viscosity of supercritical CO2 foam (Sc-CO2) for enhanced oil recovery (EOR) and CO2 sequestration. The ML approach is used to overcome the challenge of using physical correlations to account for the effect of key experimental parame
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Ma, Zhengchao, Maoya Hsu, Hao Hu, et al. "Hybrid Strategies for Interpretability of Rate of Penetration Prediction: Automated Machine Learning and SHAP Interpretation." In 58th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2024. http://dx.doi.org/10.56952/arma-2024-0315.

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ABSTRACT: Accurate prediction of rate of penetration (ROP) during petroleum drilling is crucial to optimize and guide field operations. However, due to the complex nonlinear relationship between drilling parameters and ROP, traditional empirical models often struggle to accurately predict ROP. This study introduces an automated machine learning (AutoML) for ROP prediction and utilizes SHAP (SHapley Additive exPlanations) to interpret the prediction results. The workflow framework based on this collaborative prediction strategy enables automated processing of data and automatic stacking ensembl
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Al-Ballam, S., H. Karami, and D. Devegowda. "A Hybrid Physical and Machine Learning Model to Diagnose Failures in Electrical Submersible Pumps." In SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214632-ms.

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Abstract Electrical submersible pumps (ESPs) are among the most common artificial lift techniques in highly productive oil wells. The ESP failures are extremely costly to the producers and must be minimized. This study proposes a hybrid approach utilizing multi-class classification machine learning (ML) models to identify various specific failure modes (SFMs) of an ESP. A comprehensive dataset and various ML algorithms are utilized, considering the physics of fluid flow through the ESP. The ML models are based on field data gathered from the surface and downhole ESP monitoring equipment over f
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Rathnayake, O., V. Adikariwattage, and C. Senanayake. "Using a machine learning approach to develop a macroscopic passenger flow model for departure passengers at an airport terminal." In Transport Research Forum 2025. Transportation Engineering Group, Department of Civil Engineering, University of Moratuwa, 2025. https://doi.org/10.31705/trf.2025.14.

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Effective passenger flow management is a cornerstone of airport terminal operations, directly impacting service quality, resource allocation, and traveller satisfaction. As global air traffic demand continues to grow, airport systems must contend with increasingly complex, dynamic, and high-volume environments. Traditional modelling methods—such as queuing theory, regression models, and discrete event simulation (DES)—have provided valuable frameworks for analysing terminal processes. However, these methods often struggle to deliver scalable, flexible, and real-time solutions required for mode
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