Finding the original mass: A machine learning model and its deployment for lithic scrapers. by Guillermo Bustos-Pérez

“…This allows for the wide spread implementation of a highly precise machine learning model for predicting initial mass of flake blanks successively retouched into scrapers.…”
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Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning by Jiazheng Zhu, Xize Huang, Xiaoyu Liang, Meng Wang, Yu Zhang

“…By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. …”
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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

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A Kp‐Driven Machine Learning Model Predicting the Ultraviolet Emission Auroral Oval by Huiting Feng, Dedong Wang, Yuri Y. Shprits, Artem Smirnov, Deyu Guo, Yoshizumi Miyoshi, Stefano Bianco, Shangchun Teng, Run Shi, Su Zhou, Yongliang Zhang

“…Based on the data spanning from 2005 to 2016 obtained from DMSP/SSUSI, we explore several machine learning algorithms, such as KNN, RF, and XGBoost, to construct an auroral oval prediction model. …”
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An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis by Wei Xiao, Yingying Shen, Jiao Zhao, Luogeng Lv, Jiangtao Chen, Wei Zhao

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Seismic Shear Strength Prediction of Reinforced Concrete Shear Walls by Stacking Multiple Machine Learning Models by Siming Tian, Xiangyong Ni, Yang Wang

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Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, W. Kent Tobiska

“…We utilize derived temperature data and optimize a nonlinear machine‐learned (ML) regression model to improve upon the performance of the linear EXospheric TEMPeratures on a PoLyhedrAl gRid (EXTEMPLAR) model. …”
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Autonomous Detection of Mineral Phases in a Rock Sample Using a Space-prototype LIMS Instrument and Unsupervised Machine Learning by Salome Gruchola, Peter Keresztes Schmidt, Marek Tulej, Andreas Riedo, Klaus Mezger, Peter Wurz

“…In situ mineralogical and chemical analyses of rock samples using a space-prototype laser ablation ionization mass spectrometer along with unsupervised machine learning are powerful tools for the study of surface samples on planetary bodies. …”
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Fairness in focus: quantitative insights into bias within machine learning risk evaluations and established credit models by Jacob Ford

“…Notably, our findings indicate that for low-income customers, the variance across all threshold scenarios was over seven times lower when using a machine learning model compared to traditional FICO scores, signifying a significant reduction in bias. …”
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Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

Subjects: “…Machine learning…”
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PHYSICS-DRIVEN FEATURE CREATION TO IMPROVE MACHINE LEARNING MODELS PERFORMANCE FOR OIL PRODUCTION RATE PREDICTION by Eghbal Motaei, Seyed Mehdi Tabatabai, Tarek Ganat, Ahmad Khanifar, Sulaiman Dzaiy, Timur Chis

“…This paper aims to develop a machine learning-based model for oil production rate prediction. …”
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Machine learning-enhanced fully coupled fluid–solid interaction models for proppant dynamics in hydraulic fractures by Dennis Delali Kwesi Wayo, Sonny Irawan, Lei Wang, Leonardo Goliatt

“…Abstract This study presents a hybrid modeling framework for predicting proppant settling rate (PSR) in hydraulic fracturing by integrating symbolic physics-based derivations, parametric simulations, and ensemble machine learning. …”
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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

“…These features are then subjected to Principal Component Analysis (PCA) for dimensionality reduction. Subsequently, three machine learning models—Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Gaussian Naive Bayes (GNB)—are employed for classification. …”
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Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting by Andrey K. Gorshenin, Anton L. Vilyaev

“…This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. Model performance was evaluated using a range of classification metrics, including measures of predictive accuracy and diagnostic reliability, with 95% confidence intervals provided to enhance reliability. …”
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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

“…Stress by unfavorable environmental conditions, including temperature, light intensity, and photoperiod, significantly impact early-stage growth in crops, such as cucumber seedlings, often resulting in yield reduction and quality degradation. Advanced machine learning (ML) models combined with image-based analysis offer promising solutions for precise, non-invasive stress monitoring. …”
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Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis by Feng Pang, Lijiao Wu, Jianping Qiu, Yu Guo, Liangen Xie, Shimin Zhuang, Mengya Du, Danni Liu, Chenyue Tan, Tianrun Liu

“…Results This study analyzed 693 lymph nodes (302 positive and 391 negative) and identified 35 significant radiomic features through dimensionality reduction and selection. The three machine learning models, including the Lasso regression, Support Vector Machine (SVM), and RF radiomics models, showed.…”
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Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning by Masashige Shiga, Masao Sorai, Tetsuya Morishita, Masaatsu Aichi, Naoki Nishiyama, Takashi Fujii

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Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis by Dongliang Hu, Manman Cui, Xueke Zhang, Yuanyuan Wu, Yan Liu, Duchang Zhai, Wanliang Guo, Shenghong Ju, Guohua Fan, Wu Cai

“…Abstract Objective To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients. …”
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Comparative evaluation of feature reduction methods for drug response prediction by Farzaneh Firoozbakht, Behnam Yousefi, Olga Tsoy, Jan Baumbach, Benno Schwikowski

“…Our analysis employs six distinct machine learning models, with a total of more than 6,000 runs to ensure a robust evaluation. …”
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