A mixed-methods approach to the psychological predictors of boredom in second language learning: mindfulness, grit, and self-regulation by Jingjing Lyu

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Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle. by Muhammad Usman Riaz, Luke O'Grady, Conor G McAloon, Finnian Logan, Isobel Claire Gormley

“…Using data containing 20 thousand recordings from 383 dairy cows in 11 dairy herds, this study evaluated the effectiveness of ML methods in detecting foot lesions in dairy cows using accelerometer data, with a focus on dimensionality reduction approaches and cross-validation strategies. …”
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Prediction of Hydrogen Production from Solid Oxide Electrolytic Cells Based on ANN and SVM Machine Learning Methods by Ke Chen, Youran Li, Jie Chen, Minyang Li, Qing Song, Yushui Huang, Xiaolong Wu, Yuanwu Xu, Xi Li

Subjects: “…machine learning methods…”
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The effect of differential and traditional training methods on electromyographic changes of lower body muscles in performing and learning crawl swimming by Raha Nikravesh, Seyed Kazem Mousavi Sadati, Jaleh Bagherli, Mohammad Ali Aslankhani

“…The aim of the present study was to investigate the effect of differential and traditional training methods on electromyographic changes of lower body muscles in performing and learning crawl swimming.Methods: In this study, 36 swimmers aged 20 to 25 years who had no experience in swimming training were selected as a sample and randomly divided into three groups of control, traditional exercises and differential exercises. …”
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Prediction of BRAF and TERT status in PTCs by machine learning-based ultrasound radiomics methods: A multicenter study by Hui Shi, Ke Ding, Xue Ting Yang, Ting Fan Wu, Jia Yi Zheng, Li Fan Wang, Bo Yang Zhou, Li Ping Sun, Yi Feng Zhang, Chong Ke Zhao, Hui Xiong Xu

“…Conclusion: The machine learning-based US radiomics methods, integrated with clinical characteristics, demonstrated effectiveness in predicting the BRAF V600E and TERT promoter mutations in PTCs.…”
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Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes by Martin Hitziger, Mareike Ließ

“…The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. …”
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Comparison of Trivariate Copula-Based Conditional Quantile Regression Versus Machine Learning Methods for Estimating Copper Recovery by Heber Hernández, Martín Alberto Díaz-Viera, Elisabete Alberdi, Aitor Goti

“…This approach is compared with six supervised machine learning regression methods, namely, Decision Tree, Extra Tree, Support Vector Regression (linear and epsilon), Multilayer Perceptron, and Random Forest. …”
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Predicting Early Employability of Vietnamese Graduates: Insights from Data-Driven Analysis Through Machine Learning Methods by Long-Sheng Chen, Thao-Trang Huynh-Cam, Van-Canh Nguyen, Tzu-Chuen Lu, Dang-Khoa Le-Huynh

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A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data by Siqi He, Lihong Zhu, Yiman Li, Qing Xia, Qiong Zheng, Zheng Wang, Xinyu Zou

“…This study employs the random forest (RF) and extreme gradient boosting algorithm to rank the significance of features, which include sentinel-1 polarization and sentinel-2 spectral information, texture features, and vegetation indices, and analyze the contribution of each feature using the SHAP method. The change analysis of the impervious layer in Changsha County from 2016 to 2024 was conducted based on the better machine learning algorithm and indicators for extracting the impervious layer. …”
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Constructing a predictive model of negative academic emotions in high school students based on machine learning methods by Shumeng Ma, Ning Jia, Xiuchao Wei, Wanyi Zhang

“…Subsequently, the importance of variables was determined using the forward feature selection method. We concluded that the most important factors for predicting high school students’ negative academic emotions are affect control, followed by ability attribution, luck attribution, background attribution, self-efficacy for learning behaviors, and self-efficacy for learning abilities. …”
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A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods by Fatima Rashed Alzaabi, Abid Mehmood

“…Furthermore, the survey explores the utilization of modern deep learning and natural language processing (NLP) based methods as promising alternatives, shedding light on their advantages over traditional methods. …”
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Advancing Scalable Methods for Surface Water Monitoring: A Novel Integration of Satellite Observations and Machine Learning Techniques by Megan Renshaw, Lori A. Magruder

“…Notably, the high correlation (r > 0.8) between our surface water estimates and the GRACE-FO signal in the Manaus region highlights our method’s ability to resolve key hydrological dynamics. …”
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The relative data hungriness of unpenalized and penalized logistic regression and ensemble-based machine learning methods: the case of calibration by Peter C. Austin, Douglas S. Lee, Bo Wang

Subjects: “…Machine learning…”
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Integrating sequencing methods with machine learning for antimicrobial susceptibility testing in pediatric infections: current advances and future insights by Zhuan Zou, Zhuan Zou, Fajuan Tang, Fajuan Tang, Lina Qiao, Lina Qiao, Sisi Wang, Sisi Wang, Haiyang Zhang, Haiyang Zhang

“…Recent technological advances in sequencing methods, including metagenomic next-generation sequencing (mNGS), Oxford Nanopore Technologies (ONT), and targeted sequencing (TS), have significantly enhanced the detection of both pathogens and their associated resistance genes. …”
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On the Development of an Acoustic Image Dataset for Unexploded Ordnance Classification Using Front-Looking Sonar and Transfer Learning Methods by Piotr Ściegienka, Marcin Blachnik

“…The obtained dataset was then evaluated by state-of-the-art image classification methods using off-the-shelf models and transfer learning techniques. …”
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Comparative study on Functional Machine learning and Statistical Methods in Disease detection and Weed Removal for Enhanced Agricultural Yield by Sudha D., Menaga D.

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Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures by Srinivasagan N. Subhashree, C. Igathinathane, John Hendrickson, David Archer, Mark Liebig, Jonathan Halvorson, Scott Kronberg, David Toledo, Kevin Sedivec

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Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…This study utilizes fractal dimension to characterize the geometric characteristics of rock mass discontinuity and develops a data-driven surrounding rock classification (SRC) model integrating machine learning algorithms. Initially, the box-counting method was introduced to calculate the fractal dimension of discontinuity from the excavation face image. …”
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An Empirical Comparison of Urban Road Travel Time Prediction Methods—Deep Learning, Ensemble Strategies and Performance Evaluation by Yizhe Wang, Yangdong Liu, Xiaoguang Yang

“…This study compares the predictive performance of deep learning methods with traditional shallow learning methods for urban road travel time prediction, and explores the potential for improving prediction effectiveness through multi-run training strategies for hyperparameter optimization and ensemble learning. …”
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Analysis of seismicity in the Haicheng-Xiuyan region based on dense array data and deep learning methodsKey points by Zemin Liu, Weitao Wang, Lu Li, Zihao Li, Ziye Yu, Songyong Yuan, Lanshu Bai

“…In this study, we selected 15 permanent stations and 37 ChinArray-III stations within 150 km of the epicenter of the Haicheng Earthquake. Next, we used deep learning methods to pick P- and S-wave phases from continuous waveforms recorded at these stations from January 2018 to July 2020. …”
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