Drivers of academic achievement in high school: Assessing the impact of COVID-19 using machine learning techniques by Ana Beatriz-Afonso, Frederico Cruz-Jesus, Catarina Nunes, Mauro Castelli, Tiago Oliveira, Luísa Canto e Castro

“…This study contributes to AA literature by utilizing extensive data and machine learning models to reveal enduring and emerging factors affecting educational outcomes during challenging times.…”
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Analyzing click data with AI: implications for student performance prediction and learning assessment by Mahdi-Reza Borna, Hanan Saadat, Aref Tavassoli Hojjati, Elham Akbari

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Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic by Eka Miranda, Mediana Aryuni, Mia Ika Rahmawati, Siti Elda Hiererra, Albert Verasius Dian Sano

“…Objective: This study aims to develop machine learning (ML) model-agnostic interpretability that could predict students' academic performance in VCAOL. …”
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Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity by Nasser Shoeibi, Majid Abrishami, Seyedeh Maryam Hosseini, Mohammad-Reza Ansari-Astaneh, Razieh Farrahi, Bahareh Gharib, Fatemeh Neghabi, Mojtaba Abrishami, Mehdi Sakhaee, Mehrdad Motamed Shariati

“…Abstract Background This study aims to design and evaluate various supervised machine-learning models for identifying premature infants who require treatment based on demographic data and clinical findings from screening examinations. …”
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Mapping soil drainage classes: Comparing expert knowledge and machine learning strategies by Danilo César de Mello, Nélida E.Q. Silvero, Bradley A. Miller, Nicolas Augusto Rosin, Jorge Tadeu Fim Rosas, Bruno dos Anjos Bartsch, Gustavo Vieira Veloso, Jean Jesus Macedo Novais, Renan Falcioni, Marcos Rafael Nanni, Marcelo Rodrigo Alves, Elpídio Inácio Fernandes-Filho, Uemeson José dos Santos, José Alexandre Melo Demattê

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Beyond Performance: Explaining and Ensuring Fairness in Student Academic Performance Prediction with Machine Learning by Kadir Kesgin, Salih Kiraz, Selahattin Kosunalp, Bozhana Stoycheva

“…This study addresses fairness in machine learning for student academic performance prediction using the UCI Student Performance dataset. …”
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A real-time AI tool for hybrid learning recommendation in education: Preliminary results by Chaman Verma

“…This study created an innovative AI tool utilizing the Support Vector Machine (SVM) algorithm on primary samples of Hungarian informatics students to assess their suitability for adopting hybrid learning in their studies. …”
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Modeling and forecasting of the high-dimensional time series data with functional data analysis and machine learning approaches by Yousuf Alkhezi, Hajar M. Alkhezi, Ahmad Shafee

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Prediction of Student Academic Performance Utilizing a Multi-Model Fusion Approach in the Realm of Machine Learning by Wei Zou, Wei Zhong, Junzhen Du, Lingyun Yuan

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Real Estate Market Forecasting for Enterprises in First-Tier Cities: Based on Explainable Machine Learning Models by Dechun Song, Guohui Hu, Hanxi Li, Hong Zhao, Zongshui Wang, Yang Liu

“…This study comprehensively measures the evolution trends of the real estate markets in Beijing, Shanghai, Guangzhou, and Shenzhen, China, from 2003 to 2022 through three dimensions. Then, various machine learning methods and interpretability methods like SHAP values are used to explore the impact of supply, demand, policies, and expectations on the real estate market of China’s first-tier cities. …”
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Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning by Yanzi Li, Cai Chen, Fuqiang Liu, Jian Wang

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Improving brain tumor classification: An approach integrating pre-trained CNN models and machine learning algorithms by Mohamed R. Shoaib, Jun Zhao, Heba M. Emara, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel

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Association of risk factors with mental illness in a rural community: insights from machine learning models by Firoj Al-Mamun, Mohammed A. Mamun, Md Emran Hasan, Moneerah Mohammad ALmerab, Johurul Islam, Mohammad Muhit

“…Aims This study aims to examine the prevalence and associated risk factors of common mental illnesses collectively (depression and anxiety) in a rural Bangladeshi community using machine learning models. Method This cross-sectional study surveyed 490 adults aged 18–59 in a rural Bangladeshi community. …”
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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…The dataset was split into training and testing sets (7:3 ratio), and seven machine learning models, including the ensemble Super Learner, were trained. …”
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Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning by Deyu WANG, Defu ZHU, Biaobiao YU, Chen WANG

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Predicting Cardiovascular Aging Risk Based on Clinical Data Through the Integration of Mathematical Modeling and Machine Learning by Kuat Abzaliyev, Madina Suleimenova, Siming Chen, Madina Mansurova, Symbat Abzaliyeva, Ainur Manapova, Almagul Kurmanova, Akbota Bugibayeva, Diana Sundetova, Raushan Bitemirova, Nazipa Baizhigitova, Merey Abdykassymova, Ulzhas Sagalbayeva

“…A Random Forest classifier was trained to distinguish between high-risk and low-risk individuals using the same feature set. These machine learning approaches were used as complementary tools to enhance the robustness and interpretability of the modeling results. …”
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Machine learning models coupled with ionic fragment σ-profiles to predict ammonia solubility in ionic liquids by Kaikai Li, Yuesong Zhu, Sensen Shi, Yongzheng Song, Haiyan Jiang, Xiaochun Zhang, Shaojuan Zeng, Xiangping Zhang

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The role of EU cohesion funds in Romanian labour productivity: Insights from machine learning and econometric modelling by Davidescu Adriana AnaMaria, Maer Matei Monica Mihaela, Agafiței Marina-Diana, Bolboașă Maria Bianca

“…This research aims to assess how ESIF investments influence productivity imbalance while also identifying key regional determinants of economic performance, including socioeconomic structure, institutional quality, and educational attainment. Utilising a hybrid methodology integrating machine learning for variable selection and econometric modelling for effect estimation, the analysis leverages Least Absolute Shrinkage and Selection Operator to pinpoint the most influential factors and fixed effects panel regression models to quantify regional impacts. …”
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Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care by Holly Tibble, Aziz Sheikh, Athanasios Tsanas

“…Longitudinal Scottish primary care data for 21,250 asthma patients were used to predict the risk of asthma attacks in the following year. A selection of machine learning algorithms (i.e., Naïve Bayes Classifier, Logistic Regression, Random Forests, and Extreme Gradient Boosting), hyperparameters, training data enrichment methods were explored, and validated in a random unseen data partition. …”
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A customized ensemble machine learning approach: predicting students’ exam performance by Rasel Ahmed, Nafiz Fahad, Md Saef Ullah Miah, Kah Ong Michael Goh, Mufti Mahmud, M. Mostafizur Rahman

“…Using a dataset of 500 students sourced from Kaggle, we introduce a novel customized ensemble machine learning model, combining Random Forest (RF) and AdaBoost classifiers with a custom-weighted soft voting method (weights of 0.2 for RF and 0.8 for AdaBoost). …”
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