Analytical finite-integral-transform and gradient-enhanced machine learning approach for thermoelastic analysis of FGM spherical structures with arbitrary properties by Palash Das, Dipayan Mondal, Md. Ashraful Islam, Md. Abdullah Al Mohotadi, Prokash Chandra Roy

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Research on the Control Method of Force Servo System of Liquid Hydrostatic Guide Oil Film Thickness Test Bench for Machine Tools by Xu Fang, Youmin Wang, Bo Zhu

“…The test bench was designed to solve the problem of measuring the oil film thickness of the liquid hydrostatic guideway of machine tools. And based on the fast overshoot of the classical PID control, introducing the self-antidisturbance control algorithm, this study established a mathematical model of the test bench electro-hydraulic servo control system. …”
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A Machine Learning Dataset of Artificial Inner Ring Damage on Cylindrical Roller Bearings Measured Under Varying Cross-Influences by Christopher Schnur, Payman Goodarzi, Yannick Robin, Julian Schauer, Andreas Schütze

“…In practical machine learning (ML) applications, covariate shifts and dependencies can significantly impact model robustness and prediction quality, leading to performance degradation under distribution shifts. …”
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Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods by Huifang Wang, Min Wang, Pan Jiang, Fanshu Ma, Yanhu Gao, Xinchen Gu, Qingzu Luan

“…A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R²) of 0.93. …”
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Interpretable machine learning excavates a low-alloyed magnesium alloy with strength-ductility synergy based on data augmentation and reconstruction by Qinghang Wang, Xu Qin, Shouxin Xia, Li Wang, Weiqi Wang, Weiying Huang, Yan Song, Weineng Tang, Daolun Chen

“…The application of machine learning in alloy design is increasingly widespread, yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships. …”
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Support Vector Machine for Stratification of Cognitive Impairment Using 3D T1WI in Patients with Type 2 Diabetes Mellitus by Xu Z, Zhao L, Yin L, Cao M, Liu Y, Gu F, Liu X, Zhang G

“…Radiomics features were extracted from the brain tissue except ventricles and sulci in the 3D T1WI images, support vector machine (SVM) model was then established to identify the CI and N groups, and the MCI and DM groups, respectively. …”
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Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning by Smita Sahay, Jingran Wen, Daniel R. Scoles, Anton Simeonov, Thomas S. Dexheimer, Ajit Jadhav, Stephen C. Kales, Hongmao Sun, Stefan M. Pulst, Julio C. Facelli, David E. Jones

“…Compounds were clustered based on structural similarity independently for the three models using the SimpleKMeans algorithm into the optimal number of clusters (<i>n</i> = 26). …”
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Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…However, these solutions introduce the risk of overengineering, in which the complexity of the model outweighs its practical benefits. In contrast, classical machine learning techniques offer a more efficient alternative but are often overlooked due to a lack of focus on data pre-processing, which is critical for achieving optimal performance. …”
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Canopy extraction of mango trees in hilly and plain orchards using UAV images: Performance of machine learning vs deep learning by Yuqi Yang, Tiwei Zeng, Long Li, Jihua Fang, Wei Fu, Yang Gu

“…Subsequently, Mixed Dataset-HR-Net and ETC-CHM (Canopy height model) models were developed based on these optimal models, and their performance was evaluated for canopy segmentation and area extraction in four representative regions. …”
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Installation of pocket parks in mountainous cities: A case study on the nonlinear effect of the built environment on pocket park vitality in Chongqing, China by Zhonghu Zhang, Junyan Yang, Yi Shi, Huiya Yang, Daijun Chen, Chenyang Zhang, Zhihan Zhang, Xun Zhang

“…It utilizes location-based service data to measure pocket park vitality. An interpretable machine learning model that integrates eXtreme Gradient Boosting (XGBoost) and Shapley Additive exPlanations (SHAP) is employed to explore the nonlinear effects and interactions of built environment factors on pocket park vitality. …”
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Prediction and optimization of acoustic absorption performance of quasi-Helmholtz acoustic metamaterials based on LightGBM algorithm by Jianxin Xu, Weikang Mao, Xiaoqian Yu, Bingfei Liu

“…In this paper, by analyzing the model interpretation and optimization results, the optimal values of each parameter are obtained to satisfy the optimization requirements. …”
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How digital therapeutic alliances influence the perceived helpfulness of online mental health Q&A: An explainable machine learning approach by Yinghui Huang, Hui Liu, Maomao Chi, Sujie Meng, Weijun Wang

“…Results The machine learning-based model for predicting perceived helpfulness demonstrated strong performance, achieving an root mean square error of 0.8234 and a mean absolute percentage error of 22.7288%. …”
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Circadian rhythm related genes signature in glioma for drug resistance prediction: a comprehensive analysis integrating transcriptomics and machine learning by Junbo Liao, Yingxing Duan, Xiangwang Xu, Yaxue Liu, Chaohong Zhan, Gelei Xiao

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A study on factors influencing digital sports participation among Chinese secondary school students based on explainable machine learning by XiaoTao Cai, Yi Xian, TongYi Liu, YuXin Zhou, Qing Chen, HaoNan Cui

“…Multilevel logistic regression identified five significant influencing factors (p < 0.05): academic performance, weekly physical education class days, household ICT resources, school ICT resources, and ICT social perception, which were incorporated as features in machine learning models. Through grid search with 5-fold cross-validation, we constructed and optimized four basic machine learning models (GNB, GBDT, KNN, and LR), then developed an ensemble stacking model using base models with AUC values exceeding 0.70. …”
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Inversion of SPAD Values of Pear Leaves at Different Growth Stages Based on Machine Learning and Sentinel-2 Remote Sensing Data by Ning Yan, Qu Xie, Yasen Qin, Qi Wang, Sumin Lv, Xuedong Zhang, Xu Li

“…First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. …”
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Machine Learning (AutoML)-Driven Wheat Yield Prediction for European Varieties: Enhanced Accuracy Using Multispectral UAV Data by Krstan Kešelj, Zoran Stamenković, Marko Kostić, Vladimir Aćin, Dragana Tekić, Tihomir Novaković, Mladen Ivanišević, Aleksandar Ivezić, Nenad Magazin

“…Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Aerial Vehicle (UAV) multispectral (MS) and Red-Green-Blue (RGB) imagery. …”
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Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process by A. Bolève, R. Eddies, M. Staring, Y. Benboudiaf, H. Pournaki, M. Nepveaux

“…By coupling the localised measurements from CPTs with more global 3D measurements derived from geophysical methods, a higher fidelity 3D overview of the subsurface can be obtained. Machine Learning (ML) may offer an effective means to capture all types of geophysical information associated with CPT data at a site scale to build a 2D or 3D ground model. …”
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Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics by Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira

“…We evaluate the significance of body composition radiomics in predicting outcomes for resectable gastric cancer (GC) patients, as these parameters can enhance optimal surveillance strategies and risk-stratification models. …”
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Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning by Danhe Wang, Chunxia Yao, Yangyang Lu, Di Huang, Yameng Li, Xugan Wu, Weiguo Song, Qinxiong Rao

“…Interpretation of the optimal Random Forest model, using SHAP (SHapley Additive exPlanations) analysis, identified <i>δ</i>²H in male muscle, <i>δ</i>¹⁵N in female hepatopancreas, and <i>δ</i>¹³C in female hepatopancreas as the most influential features for discriminating geographic origin. …”
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Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases by Zhiyuan Ning, Xuanfei Jiang, Huan Huang, Honggang Ma, Ji Luo, Xiangyan Yang, Bing Zhang, Ying Liu

“…The optimal features predicting NT-proBNP levels were identified using univariate and step-forward multivariate models. …”
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