Enhancing augmented reality with machine learning for hands-on origami training by Mikołaj Łysakowski, Jakub Gapsa, Chenxu Lyu, Thomas Bohné, Sławomir Konrad Tadeja, Piotr Skrzypczyński, Piotr Skrzypczyński

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Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma by Abdul Basit Ahanger, Syed Wajid Aalam, Tariq Ahmad Masoodi, Asma Shah, Meraj Alam Khan, Ajaz A. Bhat, Assif Assad, Muzafar Ahmad Macha, Muzafar Rasool Bhat

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Using Factor Decomposition Machine Learning Method to Music Recommendation by Dapeng Sun

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Prediction of Mg Alloy Corrosion Based on Machine Learning Models by Zhenxin Lu, Shujing Si, Keying He, Yang Ren, Shuo Li, Shuman Zhang, Yi Fu, Qi Jia, Heng Bo Jiang, Haiying Song, Mailing Hao

“…The RF algorithm offered the most accurate predictions than the other three machine learning algorithms. The input effects on corrosion potential have been investigated. …”
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Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy by Jing-Mei Yin, Yang Li, Jun-Tang Xue, Guo-Wei Zong, Zhong-Ze Fang, Lang Zou

“…We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. …”
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Machine Learning Model for Processing Aerospace Images of the Earthʼs Surface by T. F. Starovoitova, I. A. Starovoitov

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Predicting the thickness of shallow landslides in Switzerland using machine learning by C. Schaller, C. Schaller, L. Dorren, M. Schwarz, C. Moos, A. C. Seijmonsbergen, E. E. van Loon

“…We tested three machine learning (ML) models based on random forest (RF) models, generalised additive models (GAMs), and linear regression models (LMs). …”
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Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies by Alireza Maleki, Mohammad Mahdi Mirza Ali Mohammadi, Shahab Gholizadeh, Behnaz Dalvandi, Mohammad Rahimi, Aidin Tarokhian

Subjects: “…Machine learning…”
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Exploring the Landscape of Programming Language Identification With Machine Learning Approaches by Amandeep Verma, Rahul Saha, Gulshan Kumar, Alessandro Brighente, Mauro Conti, Tai-Hoon Kim

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Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning by Hosna Tavakoli, Reza Rostami, Reza Shalbaf, Mohammad‐Reza Nazem‐Zadeh

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Machine Learning Techniques for Quantification of Knee Segmentation from MRI by Sujeet More, Jimmy Singla, Ahed Abugabah, Ahmad Ali AlZubi

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Machine learning configuration-dependent friction tensors in Langevin heatbaths by Matthias Sachs, Wojciech G Stark, Reinhard J Maurer, Christoph Ortner

“…Machine Learning: Science and Technology…”
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Risk Assessment of Government Debt Based on Machine Learning Algorithm by Dan Chen

“…This paper builds an effective government debt risk assessment system based on machine learning algorithm. According to forming the performance of local government debt risk and its internal and external influencing factors, this study applies the analytic hierarchy process, entropy method, and BP neural network method to construct the local government risk assessment index system, which includes the primary and secondary indexes including the explicit debt risk, the contingent implicit debt risk, and the financial and economic operation risk. …”
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Bio-primed machine learning to enhance discovery of relevant biomarkers by David M. Henke, Alexander Renwick, Joseph R. Zoeller, Jitendra K. Meena, Nicholas J. Neill, Elizabeth A. Bowling, Kristen L. Meerbrey, Thomas F. Westbrook, Lukas M. Simon

“…Here, we introduce a novel machine learning approach extending the Least Absolute Shrinkage and Selection Operator (LASSO) regression framework to incorporate biological knowledge, such as protein-protein interaction databases, into the regularization process. …”
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Interpretable Machine Learning Techniques for an Advanced Crop Recommendation Model by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…Our research addresses this critical imperative by introducing an innovative predictive model that refines crop recommendation systems through advanced machine learning techniques, specifically random forest and SHapley Additive exPlanations (SHAP). …”
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Machine learning-powered, high-affinity modification strategies for aptamers by Gubu Amu, Xin Yang, Hang Luo, Sifan Yu, Huarui Zhang, Yuan Tian, Yuanyuan Yu, Shijian Ding, Yufei Pan, Zefeng Chen, Yixin He, Yuan Ma, Baoting Zhang, Ge Zhang

“…This approach harnessed the power of machine learning to predict the most promising high-affinity modification strategy for aptamers.…”
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An efficient modified HS conjugate gradient algorithm in machine learning by Gonglin Yuan, Minjie Huang

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Machine learning-based analyzing earthquake-induced slope displacement. by Jiyu Wang, Niaz Muhammad Shahani, Xigui Zheng, Jiang Hongwei, Xin Wei

“…This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. …”
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Using Machine Learning for Aerostructure Surface Damage Digital Reconstruction by Yijia Wu, Hon Ping Tang, Anthony Mannion, Robert Voyle, Ying Xin

Subjects: “…machine learning…”
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Perbandingan Algoritme Machine Learning untuk Memprediksi Pengambil Matakuliah by Fitra A. Bachtiar, Indra K. Syahputra, Satrio A. Wicaksono

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