X-ray based radiomics machine learning models for predicting collapse of early-stage osteonecrosis of femoral head by Yaqing He, Yang Chen, Yusen Chen, Pingshi Li, Le Yuan, Maoxiao Ma, Yuhao Liu, Wei He, Wu Zhou, Leilei Chen

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An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation by Christopher N. Mkono, Chuanbo Shen, Alvin K. Mulashani, Patrice Nyangi

“…This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield, Northwestern Uganda. …”
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Flood risk modelling by the synergistic approach of machine learning and best-worst method in Indus Kohistan, Western Himalaya by Ashfaq Ahmad, Jiangang Chen, Xiaoqing Chen, Nitesh Khadka, Muhib Ullah Khan, Chenyuan Wang, Muhammad Tayyab

“…In this study, we propose a novel synergistic approach for flood risk mapping in Indus Kohistan, Pakistan, by integrating machine learning (ML) models and the best-worst method (BWM). …”
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Experimental investigation of solar PVT collector with the dryer on mass and temperature of dried red chili with Machine Learning Models by Miroslav Mahdal, K. Rajathi, Muniyandy Elangovan, Prabhukumar Sellamuthu, Amit Verma

“…The drying process was stop at 11 % moisture content after 6-day testing round of red chilies drying. Machine learning (ML) models, namely the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree (DT), were used to forecast the temperature and mass dryness variables.The RBF model showed the best performance with 0.98, 0.95, and 0.92 for temperature dryness, above MLP and DM. …”
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Model reduction of structural mechanical response in the time domain by Xin Yan, Xinyu Guo, Ningya He, Jinglong Shi, Daquan Zhao

“…Subsequently, road load spectrum signal tests extract vibration acceleration and strain signals from these areas, forming the foundation for model reduction training and validation sets. Comprehensive research into machine learning and model reduction techniques is conducted, with a focus on polynomial order in response surface models and kernel functions in Gaussian process models. …”
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Hybrid FRP strengthening of reinforced concrete deep beams: Experimental, theoretical and machine learning-based study by Phromphat Thansirichaisree, Qudeer Hussain, Mingliang Zhou, Ali Ejaz, Shabbir Ali Talpur, Panumas Saingam

“…To overcome this issue, machine learning approaches were utilized, employing gradient boosting regression and random forest methods. …”
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A machine learning-based clinical predictive tool to identify patients at high risk of medication errors by Ammar Abdo, Lyse Gallay, Thibault Vallecillo, Justine Clarenne, Pauline Quillet, Vincent Vuiblet, Rudy Merieux

“…The data from 7200 patients were used to train four machine learning-based models based on 52 variables in the development dataset. …”
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Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation Using Economic Variables and Indices by Yazan Alzubi

“…This research will undertake a comparative analysis to investigate the efficiency of the different machine learning models, identifying the most effective approach for estimating the sales valuation of construction projects. …”
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Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method by Xinyu Zou, Xinlong Li, Dali Wang, Ju Wang

“…Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O₃) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O₃ concentrations. …”
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Understanding household VMT generation: A comparative analysis with traditional statistical models and a machine-learning approach by Guang Tian, Bob Danton, Bin Li, Vijaya Gopu, Julius A. Codjoe

“…Key thresholds and nonlinear effects of land-use variables on household VMT generation are identified from the BRT model. Both models indicate that land-use patterns that are denser, more diverse, and have increased access to transit result in reductions of vehicular trips and overall VMT, while the BRT model provides effective thresholds for these variables useful for developing planning solutions. …”
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Predicting Tropical Cyclone Extreme Rainfall in Guangxi, China: An Interpretable Machine Learning Framework Addressing Class Imbalance and Feature Optimization by Yuexing Cai, Cuiyin Huang, Fengqin Zheng, Guangtao Li, Sheng Lai, Liyun Zhu, Qiuyu Zhu

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Application of Support Vector Machines in High Power Device Technology by RAO Wei, LI Yong, YAN Ji

“…As a machine learning algorithm, support vector machine(SVM) has the advantages of good nonlinear processing ability, theoretical global optimum and overcoming the curse of dimensionality. …”
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Quantifying and Forecasting Emission Reductions in Urban Mobility: An IoT-Driven Bike-Sharing Analysis by Manuel Uche-Soria, Bernardo Tabuenca, Gonzalo Halcón-Gibert, Yilsy Núñez-Guerrero

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Computational fluid dynamics and machine learning integration for evaluating solar thermal collector efficiency -Based parameter analysis by Xiaoyu Hu, Lanting Guo, Jiyuan Wang, Yang Liu

“…The methodology addresses the fundamental challenge of balancing computational efficiency with prediction accuracy in thermal system design. A validated CFD model generated 935 numerical cases across diverse operational and design parameters, which were used to train and evaluate three machine learning algorithms: linear regression (LR), support vector regression (SVR), and artificial neural networks (ANN). …”
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The Application of Machine Learning on Antibody Discovery and Optimization by Jiayao Zheng, Yu Wang, Qianying Liang, Lun Cui, Liqun Wang

“…These machine learning models enable rapid in silico design of antibody candidates within a few days, achieving approximately a 60% reduction in time and a 50% reduction in cost compared to traditional methods. …”
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Primer on machine learning applications in brain immunology by Niklas Binder, Ashkan Khavaran, Roman Sankowski

“…Traditional statistical techniques, adapted for single-cell omics, have been crucial in categorizing cell types and identifying gene signatures, overcoming challenges posed by increasingly complex datasets. We explore how machine learning, particularly deep learning methods like autoencoders and graph neural networks, is addressing these challenges by enhancing dimensionality reduction, data integration, and feature extraction. …”
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Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks by Zhe Bai, Xishuo Wei, William Tang, Leonid Oliker, Zhihong Lin, Samuel Williams

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Predictive analysis of total organic carbon (TOC) in shale targets: example from the Lower Cretaceous of the Austral Basin (Patagonia, Argentina) using machine learning on outcrop... by Sebastian M. Richiano, Federico Ares

“…Dimensionality reduction improved model performance, with SVC achieving the most robust results. …”
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Developing a Nomogram for Predicting the Probability of Shoulder Redislocation after the First Episode in Young Patients by Chih-Kai Hong, Chin Kuo, Chia-Li Chang, Wei-Ren Su, Ta-Wei Tai

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Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning by Yugal Pande, Jyotismita Chaki

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