A Comprehensive Methodology of Field-Oriented Control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives by Tiago Davi Curi Busarello, Abdullah Bubshait, Oruganti V. S. R. Varaprasad, Abdulhakeem Alsaleem, Marcelo Godoy Simoes

“…This paper proposes a comprehensive methodology for Field-Oriented Control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). …”
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Comparison and general law research of multiple machine-learning models for proton exchange membrane electrolytic cell parameters prediction by Yukun Wang, Hai-Wen Li, Wenhan An, Yudong Mao, Kaimin Yang, Jiying Liu

“…Abstract This paper presents a simulation-based framework for predicting the performance of proton exchange membrane electrolytic cells (PEMEC). Machine learning techniques are employed to conduct predictive modeling and comparative analysis, with the aim of identifying the optimal machine learning model for evaluating PEMEC parameters. …”
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Analysis of Machining Parameters in WEDM of Al/SiCp20 MMC Using Taguchi-Based Grey-Fuzzy Approach by Mangesh R. Phate, Shraddha B. Toney, Vikas R. Phate

“…But the difficulties during the machining are the main hurdles to its replacement for other materials. …”
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Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning by Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Štych

“…This study explores the use of SAR data, combined with ancillary data and machine learning (ML), to estimate forest parameters typically derived from optical satellites. …”
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Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters by Gaku FUJIWARA, Yohei OKADA, Eiichi SUEHIRO, Hiroshi YATSUSHIGE, Shin HIROTA, Shu HASEGAWA, Hiroshi KARIBE, Akihiro MIYATA, Kenya KAWAKITA, Kohei HAJI, Hideo AIHARA, Shoji YOKOBORI, Motoki INAJI, Takeshi MAEDA, Takahiro ONUKI, Kotaro OSHIO, Nobukazu KOMORIBAYASHI, Michiyasu SUZUKI, Naoto SHIOMI

“…Patients were divided into 3 groups based on their daily anticoagulant medication (none, direct oral anticoagulant [DOAC], and vitamin K antagonist [VKA]), and coagulation parameters were compared in each group. We then developed a machine-learning model to predict the anticoagulant using coagulation parameters and visualized the pattern using a heat map. …”
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A visualized machine learning model using noninvasive parameters to differentiate men with and without prostatic carcinoma before biopsy by Wenting Zhou, Linhui Wang, Xue Zhang, Xiaohong Zou, Xuemei Du, Liru Luo, Xiaolan Ye, Shujing Li, Hong Lv, Yuanfu Liu, Xiaoyang Huang

“…The XGBOOST model was used to analyze 15 noninvasive prebiopsy parameters. The model performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared with four other machine learning models (decision tree learning, lasso, neural network (NNET), and support vector machine (SVM)) and a logistic model. …”
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Determination and Verification of the Johnson–Cook Constitutive Model Parameters in the Precision Machining of Ti6Al4V Alloy by Piotr Löschner, Munish Kumar Gupta, Piotr Niesłony, Mehmet Erdi Korkmaz, Muhammad Jamil

“…Inherent in finite element analysis (FEA) simulations is the correct description of material behavior during machining. For this purpose, various material models are used to describe the behavior of the material in the range of high deformation, high temperature values, and high strain rates. …”
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Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz, Yacoub Najjar

“…This dataset served to train various models to estimate the UCS from basic soil parameters. The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). …”
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Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms by Antonio Panico, Alberto Corvi, Luca Collini, Corrado Sciancalepore

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Investigating anisotropic effects and optimizing parameters in ultra-short pulsed (USP) laser machining of single crystalline diamond (SCD) by Kiran Michael, Lukas Greiner, Matthias Putzer, Jodok Weixler, Timo Schudeleit, Markus Bambach, Konrad Wegener

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Predicting tensile and fracture parameters in polypropylene-based nanocomposites using machine learning with sensitivity analysis and feature impact evaluation by Pouya Rajaee, Faramarz Ashenai Ghasemi, Amir Hossein Rabiee, Mohammad Fasihi, Behnam Kakeh, Alireza Sadeghi

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The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules by Zuhua Song, Qian Liu, Jie Huang, Dan Zhang, Jiayi Yu, Bi Zhou, Jiang Ma, Ya Zou, Yuwei Chen, Zhuoyue Tang

“…To explore the application value of various machine learning (ML) algorithms based on dual-layer spectral computed tomography (DLCT) quantitative parameters in distinguishing benign from malignant thyroid micro-nodules. …”
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Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution by Mood Mohan, Nikhitha Gugulothu, Sreelekha Guggilam, T. Rajitha Rajeshwar, Michelle K. Kidder, Jeremy C. Smith

Subjects: “…Kamlet-Taft parameter…”
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Integration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron by Yacine Karmi, Haithem Boumediri, Omar Reffas, Yazid Chetbani, Sabbah Ataya, Rashid Khan, Mohamed Athmane Yallese, Aissa Laouissi

Subjects: “…cutting parameters…”
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Investigating effects of cutting parameters on surface roughness machined by turning of C45 steel based on Taguchi methodology and ANOVA by Thanh Thuong Huynh, Hoai Tan Nguyen, Quoc Khanh Huynh, Van Cuong Nguyen, Van Huu Bui

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Investigating effects of cutting parameters on surface roughness machined by turning of C45 steel based on Taguchi methodology and ANOVA by Thanh Thuong Huynh, Hoai Tan Nguyen, Quoc Khanh Huynh, Van Cuong Nguyen, Van Huu Bui

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Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX by I. V. Manoj, Hargovind Soni, S. Narendranath, P. M. Mashinini, Fuat Kara

“…WEDM, a non-contact machining technique, can be employed in the machining of such alloys. …”
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Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture by Vanessa M. Swiatek, Samuel Voss, Florian Sprenger, Igor Fischer, Hafez Kader, Klaus-Peter Stein, Roland Schwab, Sylvia Saalfeld, Ali Rashidi, Daniel Behme, Philipp Berg, I. Erol Sandalcioglu, Belal Neyazi

“…Advanced morphological and hemodynamic parameters were extracted, with clustering applied to address multicollinearity. …”
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Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys by Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang

“…To address this difficulty, a data-driven machine learning (ML) model was established to predict the compressive yield strength (σ0.2) of RHEAs, which provides a design strategy with two pivotal descriptors concerning lattice distortion and thermodynamic parameters. …”
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Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images by Charles Ekene Chika

“…Purpose: The purpose of this study was to accurately estimate proton stopping power ratio (SPR), relative electron density ρe, effective atomic number (Zeff), and mean excitation energy (I) using one simple robust model and design a machine learning algorithm that will lead to automation. …”
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