A Planar-Dimensions Machine Vision Measurement Method Based on Lens Distortion Correction by Qiucheng Sun, Yueqian Hou, Qingchang Tan, Guannan Li

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Detection of mite infested saffron plants using aerial imaging and machine learning classifier by Hossein Sahabi, Jalal Baradaran-Motie

“…In order to detect affected plants, two support vector machine (SVM) classifiers with radial basis function (RBF) kernels were used separately for NIR and RGB images. …”
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Industrial data-driven machine learning soft sensing for optimal operation of etching tools by Feiyang Ou, Henrik Wang, Chao Zhang, Matthew Tom, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…For example, artificial intelligence (AI) machine learning-based soft sensors can increase operational productivity and machine tool performance while still ensuring that critical product specifications are met. …”
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Progress and prospect of surface self-cleaning technology of machine vision system in underground mining by Shengli YANG, Yongsheng CHEN, Jiachen WANG, Lianghui LI, Fengqi LIU, Shixiong SONG, Dingheng HUI

“…The technical challenges faced by the underground mining machine vision system are pointed out from three aspects: comprehensive self-cleaning strategy, optimization of explosion-proof design and multi-functionalization of lens film materials. …”
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LINEAR GENERATOR PROTOTYPE WITH VERTICAL CONFIGURATION OF SEA WAVE POWER PLANT by Ane Prasetyowati, Wisnu Broto, Noor Suryaningsih

“…The application of the ocean wave energy conversion technology, a linear generator system is an electrical machine that functions to convert the mechanical energy of linear motion into electrical energy using the principle of electromagnetic induction. …”
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A State-of-the-Art Survey on Advanced Electromagnetic Design: A Machine-Learning Perspective by Masoud Salmani Arani, Reza Shahidi, Lihong Zhang

“…Research on electromagnetic (EM) components is essential to enabling the design and optimization of such devices as antennas and filters, leading to improved functionality, reduced costs, and enhanced overall performance. …”
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Investigation of Engine Lubrication Oil Quality Using a Support Vector Machine and Electronic Nose by Ali Adelkhani, Ehsan Daneshkhah

“…Oil properties such as viscosity, density, flash point, and freezing point were measured at each level. …”
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Efficient Approximation Procedure for Magnetization Characteristics Used in Performance Analysis of Highly Saturated Electrical Machines by Miralem Hadžiselimović, Tine Marčič, Ivan Zagradišnik

“…In practice, professional software may use many points of the magnetizing curve (sometimes 50 or more points). …”
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Elucidating the role of KLRD1 in coronary atherosclerosis: harnessing bioinformatics and machine learning to advance understanding by Huan Liang, Xiao Liang, Man Zheng, Shoudong Wang

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Integrating Inverse Data Envelopment Analysis and Machine Learning for Enhanced Road Transport Safety in Iran by Seyyed Mojtaba Tabatabai, Fatemeh Al-Sadat Tabatabai

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Online Fault Tolerant RUL Prediction Strategy for Lithium-Ion Batteries Using Machine Learning by Brahim Zraibi, Mohamed Mansouri, Chafik Okar, Hicham Chaoui

“…The model’s ability to maintain accuracy is influenced by the point at which predictions begin, as earlier predictions introduce higher levels of uncertainty due to accumulating errors over time. …”
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Learning Online MEMS Calibration with Time-Varying and Memory-Efficient Gaussian Neural Topologies by Danilo Pietro Pau, Simone Tognocchi, Marco Marcon

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Research on cultivating students’ creative thinking ability in art design teaching based on machine learning by Ke Xue

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Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…Three supervised regression models of Kernel Ridge Regression (KRR), Decision Tree Regression (DT), and Radial Basis Function Support Vector Machine (RBF-SVM) were developed to map spatial coordinates to solute concentrations. …”
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Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…Using a comprehensive database of 1404 experimental data points spanning temperature (−10 to 450 °C), pressure (0.098 to 140 MPa), and salinity (0.017 to 6.5 mol/kg), the research evaluates the predictive capabilities of five ML algorithms: Decision Tree, Random Forest, XGBoost, Multilayer Perceptron, and Support Vector Regression with a radial basis function kernel. …”
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Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding by Xiaomei Hu, Fanqi Liang, Man Zheng, Juying Xie, Shanxi Wang

“…Advanced analytical tools, including Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to refine our gene selection. …”
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Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models by Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok

“…The comparison of the control methods of the running platform based on machine learning models showed the advantage of the combined method (linear control function combined with the speed prediction model), which provides an average absolute error value of 0.116 m/s. …”
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Multi-Objective Automated Machine Learning for Inversion of Mesoscopic Parameters in Discrete Element Contact Models by Xu Ao, Shengpeng Hao, Yuyu Zhang, Wenyu Xu

“…From each stress–strain curve, eight characteristic points were extracted as inputs to a multi-objective Automated Machine Learning (AutoML) model designed to invert three key mesoscopic parameters, i.e., the elastic modulus (<i>E</i>), stiffness ratio (<i>k_s</i>/<i>k_n</i>), and degraded elastic modulus (<i>E_d</i>). …”
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Validation of a Machine Learning Model for Certification Using Symbolic Regression and a Behaviour Envelope by Moritz Neumaier, Marcel Anselment, Stephan Rudolph

“…In case the model stays within the behaviour envelope, which can be mathematically evaluated, it can be ensured that the behaviour between the test points is always physically meaningful. Since the effort for the evaluation increases with the complexity, it is proposed to use symbolic regression, a method where a search procedure combines elementary functions to create a compact symbolic model. …”
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Machine Learning-Enhanced 3D GIS Urban Noise Mapping with Multi-Modal Factors by Jianping Pan, Yuzhe He, Wei Ma, Shengwang An, Lu Li, Dan Huang, Dunxin Jia

“…As a result, there are often discrepancies between the actual noise measurements at monitoring points and the predicted values generated by these models. …”
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