Data analysis methods in astronomic objects classification (Sloan Digital Sky Survey DR14) by V. A. Golov, D. A. Petrusevich

“…The large quantity of statistical information leads to the demand for analytical algorithms and systems capable of making classification. Such information is marked up well enough to build machine learning classification systems. …”
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Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data by Parama Bagchi, Barbara Sawicka, Zoran Stamenkovic, Dušan Marković, Debotosh Bhattacharjee

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Random Generalized Additive Logistic Forest: A Novel Ensemble Method for Robust Binary Classification by Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani, Nada MohammedSaeed Alharbi, Asma Ahmad Alzahrani

“…We introduce a novel ensemble approach, the Random Generalized Additive Logistic Forest (RGALF), which integrates generalized additive models (GAMs) within a random forest framework to improve binary classification tasks. Unlike traditional random forests, which rely on piecewise constant predictions in terminal nodes, RGALF fits GAM logistic regression (LR) models to the data in each terminal node, enabling it to capture complex nonlinear relationships and interactions among predictors. …”
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Deep Learning-Based Detection of Honey Storage Areas in <i>Apis mellifera</i> Colonies for Predicting Physical Parameters of Honey via Linear Regression by Watit Khokthong, Panpakorn Kritangkoon, Chainarong Sinpoo, Phuwasit Takioawong, Patcharin Phokasem, Terd Disayathanoowat

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Machine learning-aided hybrid technique for dynamics of rail transit stations classification: a case study by Ahad Amini Pishro, Shiquan Zhang, Alain L’Hostis, Yuetong Liu, Qixiao Hu, Farzad Hejazi, Maryam Shahpasand, Ali Rahman, Abdelbacet Oueslati, Zhengrui Zhang

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Machine Learning with Tunicate Swarm Optimization for Improved Disc Herniation Prediction by Theofilos Gofas, Argyros Maris

“…The study employs Extra Trees Classification (ETC), Naïve Bayes Classification (NBC), and Logistic Regression Classification (LRC) to predict disc herniation. …”
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PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS: DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAH... by Reni Pratiwi, Memi Nor Hayati, Surya Prangga

“…Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. …”
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Factors Affecting Length of Stay in the Pediatric Emergency Department by Sung-Tse Li, Nan-Chang Chiu, Wen-Chuan Kung, Juei-Chao Chen

“…Two-thirds of the database was randomized for the classification and regression tree (CART) model-building dataset, and one-third was used for the validation dataset. …”
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Deep Learning-Assisted Compound Bioactivity Estimation Framework by Yasmine Eid Mahmoud Yousef, Ayman El-Kilany, Farid Ali, Yassin M. Nissan, Ehab E. Hassanein

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AN ADVANCED MACHINE LEARNING (ML) ARCHITECTURE FOR HEART DISEASE DETECTION, PREDICTION AND CLASSIFICATION USING MACHINE LEARNING by Muhammad Anas, Muhammad Atif Imtiaz, Saad Khan, Arshad Ali, Noor Fatima Naghman, Hamayun Khan, Sami Albouq

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Variability of three‐dimensional knee morphology cannot be effectively assessed using a coronal plane knee alignment classification in total knee arthroplasty patients by Ishaan Jagota, Joshua Twiggs, Brad Miles, Jonathan V. Baré

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Research on the Classification of Traditional Building Materials in Southern Fujian Using the Reflection Intensity Values of Ground-Based LiDAR by Tsung-Chiang Wu, Neng-Gang Kuan, Wei-Cheng Lu

“…This study addresses these challenges by using the laser reflectance intensity (I-value) for automated classification. Boxplot theory is applied to calibrate the data, remove noise, and establish polynomial regression equations correlating intensity with scanning distances. …”
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Enhancing heart disease prediction accuracy by comparing classification models employing varied feature selection techniques by Balliu Lorena, Zanaj Blerina, Basha Gledis, Zanaj Elma, Meçe Elinda Kajo

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Post-surgical fall risk prediction: a machine learning approach for spine and lower extremity procedures by Ya-Huei Chen, Xing-Yu Luo, Chia-Hui Chang, Chen-Tsung Kuo, Sou-Jen Shih, Mei-Yu Chang, Mei-Rong Weng, I-Chieh Chen, Ying-Lin Hsu, Ying-Lin Hsu, Jia-Lang Xu

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Diet Quality and Weight Status are Predicted by Federal Nutrition Assistance Program Participation, Health, and Demographics by Lauren E Au, Charles D Arnold, Christabel Domfe, Lorrene D Ritchie, Shannon E Whaley, Marianne Bitler, Edward A Frongillo

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A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements by Suduan Chen, Yeong-Jia James Goo, Zone-De Shen

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Review and practical excursus on the comparison between traditional statics methods and Classification And Regression Tree (CART) in real-life data: Low protein diet compared to Me... by Vincenzo Calabrese, Daniela Metro, Angela Alibrandi, Domenico Maviglia, Valeria Cernaro, Veronica Maressa, Elisa Longhitano, Guido Gembillo, Domenico Santoro

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Assessment of textile sludge pyrolysis behaviour through advance predictive models for bioenergy production by Arslan Khan, Asif Hussain Khoja, Salman Raza Naqvi, Waheed Miran, Imtiaz Ali

“…Machine learning models like Artificial Neural Networks (ANN), Classification and Regression Trees (C&RT), Boosted regression trees (BRT), and K Nearest Neighbors (KNN) employed to predict the activation energy (Ea) with ANN emerging as superior predicting capabilities (R2 = 0.999) over other models. …”
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Landscape dynamics and its related factors in the Citarum River Basin: a comparison of three algorithms with multivariate analysis by Moh. Dede, Sunardi Sunardi, Kuok-Choy Lam, Susanti Withaningsih, Hendarmawan Hendarmawan, Teguh Husodo

“…Data was acquired from Landsat-series imageries from 1993 to 2023, and LULC analyses were conducted using classification and regression trees (CART), random forest (RF), and support vector machine (SVM). …”
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Evaluation of gene expression classification studies: factors associated with classification performance. by Putri W Novianti, Kit C B Roes, Marinus J C Eijkemans

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