Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review by Anass Houdou, Imad El Badisy, Kenza Khomsi, Sammila Andrade Abdala, Fayez Abdulla, Houda Najmi, Majdouline Obtel, Lahcen Belyamani, Azeddine Ibrahimi, Mohamed Khalis

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Effectiveness of a Simplified ESP Curriculum for Adult Learners by Khalid A. Alghamdi

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The attitudes of teachers and students towards ‘one-to-five’ students’ network: Implications for students’ learning by Nigusse Weldemariam Reda, Tsegay Girmany Hagos

Subjects: “…Students' network, attitude, cooperative learning, students' learning…”
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Resilience, Confidence-Building, and Performance: by Audrey Ricke

Subjects: “…adaptive digital learning…”
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STUDENT ENGAGEMENT, CHALLENGES AND COPING MECHANISMS IN HIGH SCHOOL ONLINE DISTANCE LEARNING DURING COVID-19 PANDEMIC by Aurielle Lisa Z. Maypa, Dave E. Marcial, Jade O. Montemayor

Subjects: “…eLearning…”
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A Systematic Review of Self-directed Learning in Medical Education in Undergraduate Medical Students by Dharmendra Kumar Gupta, Arunima Chaudhuri, Dip Gaine

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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…This study employs machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Bayesian Ridge, Decision Tree, Gradient Boosting, XGBoost, and CatBoost, to forecast future trends in under-5 mortality. …”
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Application of machine learning in diabetes prediction based on electronic health record data analysis by Yang Zihan

“…With the application of electronic health records (EHRs) in the medical field, the use of machine learning to predict disease has become one of the important research hotspots in the healthcare industry. …”
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Building occupancy type classification and uncertainty estimation using machine learning and open data by Tom Narock, J. Michael Johnson, Justin Singh-Mohudpur, Arash Modaresi Rad

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LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging by Charilaos Papaioannou, Emmanouil Benetos, Alexandros Potamianos

Subjects: “…Few-shot learning…”
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Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers by Garrett S Bullock, Charles A Thigpen, Gary S Collins, Nigel K Arden, Thomas K Noonan, Michael J Kissenberth, Ellen Shanley

“…While machine learning models demonstrated improved RMSE compared to the regression, the machine learning models displayed poorer calibration compared to regression. …”
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Learning transactions representations for information management in banks: Mastering local, global, and external knowledge by Alexandra Bazarova, Maria Kovaleva, Ilya Kuleshov, Evgenia Romanenkova, Alexander Stepikin, Aleksandr Yugay, Dzhambulat Mollaev, Ivan Kireev, Andrey Savchenko, Alexey Zaytsev

Subjects: “…Representation learning…”
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Preservice Teachers’ Shifting Perspectives of Individuals with Intellectual and Developmental Disabilities in Service-Learning Experiences by Stephanie Devine, Cynthia Massey, Kathryn Haughney, Akintomide Adebile

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The Optimal Machine Learning Model for the Precise Prediction of HighPerformance Concrete Strength Property by Yufeng Qian

“…The present study employs a machine learning-based support vector regression (SVR) method to implement compressive strength prediction. …”
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RISKS REDUCING THROUGH INTELLIGENT HEADLIGHT MANAGEMENT: OPTIMIZING Q-LEARNING FOR ELECTRIC VEHICLES by Pitchaya Jamjuntr, Chanchai Techawatcharapaikul, Pannee Suanpang

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Machine learning techniques for estimating high–temperature mechanical behavior of high strength steels by C. Yazici, F.J. Domínguez-Gutiérrez

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Robust analysis of photovoltaic plants: A framework based on prediction uncertainties by machine learning by Seyyed Shahabaddin Hosseini Dehshiri, Bahar Firoozabadi

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A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration by Victor Barbosa Slivinskis, Isabela Agi Maluli, Joshua Seth Broder

“…Our objective was to evaluate the sensitivity, specificity, and accuracy of a machine learning (ML) algorithm using publicly available data to predict medical device recalls by the FDA. …”
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