A hybrid model combining environmental analysis and machine learning for predicting AI education quality by Xinyu Ren

“…The results showed that corrective measures in all environments can help the development of AI education in universities. The results of conducting a case study and examining various evaluation indicators showed that the proposed approach in this study has a good accuracy in predicting the target variable (quality of education). …”
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Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Yaqoob Wani

“…Abstract Machine learning has become an essential component across various domains, including the education sector. Accurately predicting students’ academic performance plays a critical role for teachers and school administrators—not only in enhancing the quality of education but also in influencing educational outcomes. …”
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Machine learning-based academic performance prediction with explainability for enhanced decision-making in educational institutions by Wesam Ahmed, Mudasir Ahmad Wani, Pawel Plawiak, Souham Meshoul, Amena Mahmoud, Mohamed Hammad

“…Ten regression models including K-Nearest Neighbors Regressor, Linear Regression, CatBoost, XGBoost, AdaBoost, and ensemble voting regression (VR) algorithm based on the top five heterogeneous regressors as base models are employed to predict academic outcomes. …”
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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

“…Results he RF algorithm showed high performance in both prediction approaches. …”
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Comparative analysis of impact of classification algorithms on security and performance bug reports by Said Maryyam, Bin Faiz Rizwan, Aljaidi Mohammad, Alshammari Muteb

“…The aim of this research is to compare and analyze the prediction accuracy of machine learning algorithms, i.e., Artificial neural network (ANN), Support vector machine (SVM), Naïve Bayes (NB), Decision tree (DT), Logistic regression (LR), and K-nearest neighbor (KNN) to identify security and performance bugs from the bug repository. …”
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Predictive Direct Power Control of Single-phase Power Electronic Transformer Rectifier by SONG Pingang, ZHU Weichang, GE Wang

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Investigating the Predictive Performance of Process Data and Result Data in Complex Problem Solving Using the Conditional Gradient Boosting Algorithm by Fatma Nur Aydin, Kubra Atalay Kabasakal, Ismail Dilek

“…This study aims to examine the predictive performance of process data and result data in complex problem-solving skills using the conditional gradient boosting algorithm. …”
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The Application of Artificial Intelligent Algorithms in Electric Propulsion by Tian Bin, An Bingchen, Xie Kan, Yang Sulan

Subjects: “…|electric propulsion|intelligent learning algorithms|plasma behavior prediction|data-driven|partial differential equation…”
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Harmonized Integration of GWO and J-SLnO for Optimized Asset Management and Predictive Maintenance in Industry 4.0 by A. N. Arularasan, P. Ganeshkumar, Mohammad Alkhatib, Tahani Albalawi

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Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study by Jia-yun Wu, Jing-yu Zhang, Wen-qi Xia, Yue-ning Kang, Ru-yi Liao, Yu-ling Chen, Xiao-min Li, Ya Wen, Fan-xuan Meng, Li-ling Xu, Sheng-hui Wen, Hui-fen Liu, Yuan-qing Li, Jie-ruo Gu, Qing Lv, Yong Ren

“…It is feasible to predict AIT in pSS using machine learning algorithms. …”
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Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China by Luyao Zhou, Weimin Zhang, Min Shao, Cui Wang, Yu Wang

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A recommender algorithm based on SVD ++model under trust network by Peiwu CHEN, Fangxing SHU

Subjects: “…recommender algorithm;latent factor model;trust network;rating prediction…”
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A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools by Rifa Andriani Saputri, Asrianda Asrianda, Lidya Rosnita

“…The rapid advancement of information technology has transformed education by providing tools to accurately predict students' academic performance. …”
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Predicting coastal variations in non-storm conditions with machine learning by Jabari Amir, Adjami Mehdi, Gharechelou Saeid

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Predicting social welfare in Madrid neighbourhoods using machine learning by Carlos Alberto Lastras Rodríguez

“…A comprehensive dataset representing various socioeconomic metrics of Madrid’s neighbourhoods is analysed utilising different linear regression models and the XGBoost machine learning algorithm. The findings indicate that demographic variables play a crucial role in shaping social welfare and inequality in Madrid's neighbourhoods, with the percentage of women, and the percentage of children under 14 years old and adults over 65 years old being the most important variables for predicting social welfare and inequality in the studied neighbourhoods. …”
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Slope deformation prediction based on GA–BP neural networks by Wenhui TAN, Kai LI, Huimin LIU, Meifeng CAI, Qifeng GUO

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Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model by Kerang Cao, Chang Liu, Siqi Yang, Yuxin Zhang, Lili Li, Hoekyung Jung, Shuo Zhang

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Application of Machine Learning Algorithms for Predicting the Dynamic Stiffness of Rail Pads Based on Static Stiffness and Operating Conditions by Isaac Rivas, Jose A. Sainz-Aja, Diego Ferreño, Víctor Calzada, Isidro Carrascal, Jose Casado, Soraya Diego

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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Herein, the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates, with R 2 of 0.9219 and 0.9243, respectively. …”
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Predicting the Compressive Strength of High-Performance Concrete utilizing Radial Basis Function Model integrating with Metaheuristic Algorithms by LiWei Hu

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