Optimizing Learning: Predicting Research Competency via Statistical Proficiency by Tarid Wongvorachan, Siwachoat Srisuttiyakorn, Kanit Sriklaub

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A Proposal for a New Python Library Implementing Stepwise Procedure by Luiz Paulo Fávero, Helder Prado Santos, Patrícia Belfiore, Alexandre Duarte, Igor Pinheiro de Araújo Costa, Adilson Vilarinho Terra, Miguel Ângelo Lellis Moreira, Wilson Tarantin Junior, Marcos dos Santos

Subjects: “…supervised machine learning…”
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Devising a Breast Cancer Diagnosis Protocol through Machine Learning by Tooba Mujtaba, Saif Ullah Hashmi, Usama Bin Imtiaz, Sheikh Jameel Fathima Nusra

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A Survey on Android Malware Detection Techniques Using Supervised Machine Learning by Safa J. Altaha, Ahmed Aljughaiman, Sonia Gul

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Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study by Amanda de Carvalho Dutra, Lincoln Luis Silva, Isadora Martins Borba, Amanda Gubert Alves dos Santos, Diogo Pinetti Marquezoni, Matheus Henrique Arruda Beltrame, Rogério do Lago Franco, Ualid Saleh Hatoum, Juliana Harumi Miyoshi, Gustavo Cezar Wagner Leandro, Marcos Rogério Bitencourt, Oscar Kenji Nihei, João Ricardo Nickenig Vissoci, Luciano de Andrade

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Exploring a cost-effective way for nutrient management with machine learning for container plants by Ping Yu, Kuan Qin

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Exploring machine learning algorithms to predict short birth intervals and identify its determinants among reproductive-age women in East Africa by Tirualem Zeleke Yehuala, Bezawit Melak Fente, Sisay Maru Wubante

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Password strength verification based on machine learning algorithms and LSTM recurrent neural networks by V. V. Belikov, I. A. Prokuronov

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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

Subjects: “…Supervised machine learning…”
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A Systematic Literature Review of Supervised Machine Learning Techniques for Predictive Maintenance in Industry 4.0 by Dario Guidotti, Laura Pandolfo, Luca Pulina

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Supervised Machine Learning Models for Predicting SS304H Welding Properties Using TIG, Autogenous TIG, and A-TIG by Subhodwip Saha, Barun Haldar, Hillol Joardar, Santanu Das, Subrata Mondal, Srinivas Tadepalli

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Semi-automated data collection from electronic health records in a stroke unit in Brazil by Raquel Franco Zambom Valêncio, Juli Thomaz de Souza, Fernanda Cristina Winckler, Gabriel Pinheiro Modolo, Natalia Cristina Ferreira, Silmeia Garcia Zanati Bazan, Marcos Christiano Lange, Carlos Clayton Macedo de Freitas, Sergio Alberto Rupp de Paiva, Rogério Carvalho de Oliveira, Gustavo José Luvizutto, Rodrigo Bazan

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Active learning model used for android malware detection by Md․Habibullah Shakib, Md. Yeasin, Kh. Mustafizur Rahman, Fabiha Faiz Mahi, Md. Mahsin-Ul-Islam, Saddam Hossain

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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning by Zakwan AlArnaout, Chamseddine Zaki, Yehia Kotb, Mouhammad AlAkkoumi, Nour Mostafa

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Comparison of Dimensionality Reduction Approaches and Logistic Regression for ECG Classification by Simeon Lappa Tchoffo, Éloïse Soucy, Ismaila Baldé, Jalila Jbilou, Salah El Adlouni

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Non-Invasive Painting Pigment Classification Through Supervised Machine Learning by Michal Piotr Markowski, Solongo Gansukh, Mateusz Madry, Robert Borowiec, Jaroslaw Rogoz, Boguslaw Szczupak

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The influence of partisanship on the roots of anti-vaccine attitudes in the context of the COVID-19 pandemic by Sylvia Iasulaitis, Alan Demetrius Baria Valejo, Leonardo Ribeiro dos Santos

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Large dataset on Fourier transform near infrared (FT-NIR) spectroscopy of green and roasted specialty coffee: Preprocessed infrared spectra and sensory scores for machine learning-... by Andrés F. Bahamón-Monje, Ever M. Morales-Angulo, Gentil A. Collazos-Escobar, Nelson Gutiérrez-Guzmán

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A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa by Maria Magdalena Van Zyl-Cillié, Jacoba H. Bührmann, Alwiena J. Blignaut, Derya Demirtas, Siedine K. Coetzee

Subjects: “…Supervised machine learning model…”
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Prediction of depressive disorder using machine learning approaches: findings from the NHANES by Thien Vu, Research Dawadi, Masaki Yamamoto, Jie Ting Tay, Naoki Watanabe, Yuki Kuriya, Ai Oya, Phap Ngoc Hoang Tran, Michihiro Araki

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