Investigating the Effects of Labeled Data on Parameterized Physics-Informed Neural Networks for Surrogate Modeling: Design Optimization for Drag Reduction over a Forward-Facing Ste... by Erik Gustafsson, Magnus Andersson

“…Physics-informed neural networks (PINNs) are gaining traction as surrogate models for fluid dynamics problems, combining machine learning with physics-based constraints. …”
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Development of hybrid robust model based on computational modeling and machine learning for analysis of drug sorption onto porous adsorbents by S. Tasqeeruddin, Shaheen Sultana, Abdulrhman Alsayari

“…The analyses were carried out for separation of drug from a solution by adsorption process where the concentration of drug was obtained in the solution and the adsorbent via computational fluid dynamics (CFD), and the results of concentration distribution were used or machine learning modeling. The model considered mass transfer and fluid flow equations to determine concentration distribution of solute in the system. …”
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Diesel Engine Urea Injection Optimization Based on the Crested Porcupine Optimizer and Genetic Algorithm by Xu Chen, Changhai Ma, Quanli Dou, Shuzhan Bai, Ke Sun, Zhenguo Li

“…The SVM model was optimized using the Crested Porcupine Optimizer (CPO) to improve its prediction accuracy and was made to replace the mathematical model to save computational time. …”
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Integrating Machine Learning into Additive Manufacturing of Metallic Biomaterials: A Comprehensive Review by Shangyan Zhao, Yixuan Shi, Chengcong Huang, Xuan Li, Yuchen Lu, Yuzhi Wu, Yageng Li, Luning Wang

“…Additive manufacturing (AM) has emerged as a transformative technology for producing high-precision metallic biomaterials with customized properties, offering significant advantages over traditional manufacturing methods. The integration of machine learning (ML) with AM has shown great promise in optimizing the fabrication process, enhancing material performance, and predicting long-term behavior, particularly in the development of orthopedic implants and vascular stents. …”
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IT Diagnostics of Parkinson’s Disease Based on the Analysis of Voice Markers and Machine Learning by U. A. Vishniakou, Xia YiWei

“…The Bayesian optimization algorithm and the GridSearch method were used to find the best model hyperparameters. …”
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Evaluation of Cutting Forces and Temperatures in Micro-Milling of NiTi Alloys Using Finite Element Modeling by Nataliia Balytska, Oleh Tomashevskyi, Valentina Shadura

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Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models by Kennedy C. Onyelowe, Viroon Kamchoom, Tammineni Gnananandarao, Krishna P. Arunachalam

“…For developing the SVM_Poly, SVM_RBK models, an extensive number of trials were conducted using various combinations of C and d for polynomial kernels and C and ∂ for radial basis function kernel-based support vector machines (SVMs) utilizing user-defined parameters. …”
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Advances in Machine Learning for Mechanically Ventilated Patients by Xu Y, Xue J, Deng Y, Tu L, Ding Y, Zhang Y, Yuan X, Xu K, Guo L, Gao N

“…The review also examined challenges of integrating machine learning into clinical practice, such as data integration, model interpretability, and real - time performance requirements.Results: Machine learning models have demonstrated significant potential in predicting successful extubation, optimizing oxygenation strategies through non-invasive blood gas prediction, and dynamically adjusting ventilator parameters using reinforcement learning. …”
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Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems by Benedetta Fasciolo, Nicolò Grasso, Giulia Bruno, Paolo Chiabert

“…We integrated a physics-based model with machine learning algorithms to create a dynamic hybrid framework. …”
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Optimization and Modelling of Fracture Height in SECC Cylindrical Cup Deep Drawing Processes by Quy-Huy Trieu, The Thanh Luyen, Duc-Toan Nguyen

“…A precise mathematical equation is developed to estimate fracture height under diverse machining conditions, with a maximum deviation of 4.52% observed between the mathematical model and simulation. …”
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Optimizing Convolutional Neural Network Architectures by Luis Balderas, Miguel Lastra, José M. Benítez

“…Motivated by an interest in optimizing Machine Learning models, in this paper, we propose Optimizing Convolutional Neural Network Architectures (OCNNA). …”
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A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models by Jose Antonio Lagares Rodríguez, Norberto Díaz-Díaz, Carlos David Barranco González

“…Many feature selection methods tend to exclude variables that may not be individually powerful predictors but can collectively provide significant information, thereby constraining a model’s capabilities in learning environments. In contrast, Deep Learning (DL) models paired with Automated Machine Learning techniques can decrease the reliance on manual feature engineering, thereby enabling automatic fine-tuning of numerous model configurations. …”
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A Recommendation Algorithm Based on Restricted Boltzmann Machine by WANG Weibing, ZHANG Lichao, XU Qian

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Fault Diagnosis of Gearboxes Based on AO-VMD and IAO-SVM by Wang Bo, Nan Xinyuan

Subjects: “…Aquila optimizer…”
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Bayesian-optimized ensemble deep learning models for demand forecasting in the volatile situations: A case study of grocery demand during Covid-19 outbreaks by Nader AL Theeb, Hazem Smadi, Naser Al-qaydeh

Subjects: “…demand prediction, machine learning, ensemble model, bayesian optimization, long short-term memory (lstm), gated recurrent unit (gru)…”
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Quality Evaluation Method for Base Baijiu Based on Support Vector Machine Optimized by Genetic and Bootstrap Aggregating Algorithm by PANG Tingting, ZHANG Guiyu, LIU Kecai, LI Xiaoping, TUO Xianguo, PENG Yingjie, ZENG Xianglin

“…In order to improve the accuracy and generalization capacity of the classification model, a method combining genetic algorithm (GA) and bootstrap aggregating (Bagging) was proposed to optimize the support vector machine (SVM) classifier. …”
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Enhancing pediatric distal radius fracture detection: optimizing YOLOv8 with advanced AI and machine learning techniques by Farid Amirouche, Aashik Mathew Prosper, Majd Mzeihem

“…We optimized the model through hyperparameter tuning, implementing data cleaning, augmentation, and normalization techniques using the GRAZPEDWRI-DX dataset. …”
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Prediction of Anthocyanin Content in Purple-Leaf Lettuce Based on Spectral Features and Optimized Extreme Learning Machine Algorithm by Chunhui Liu, Haiye Yu, Yucheng Liu, Lei Zhang, Dawei Li, Junhe Zhang, Xiaokai Li, Yuanyuan Sui

“…Finally, dung beetle optimization (DBO), subtraction-average-based optimization (SABO), and the whale optimization algorithm (WOA) optimized the extreme learning machine (ELM) for modeling. …”
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App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning. by Leila F Dantas, Igor T Peres, Leonardo S L Bastos, Janaina F Marchesi, Guilherme F G de Souza, João Gabriel M Gelli, Fernanda A Baião, Paula Maçaira, Silvio Hamacher, Fernando A Bozza

“…The model was incorporated by the "Dados do Bem" app aiming to prioritize users for testing. …”
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Harnessing machine learning approach for hardness optimization of Al-Si alloy composites reinforced with coconut shell ash by M Poornesh, Shreeranga Bhat, Mithun Kanchan

“…The study used Minitab’s Automated Machine Learning (AutoML), specifically the TreeNet model, to develop a predictive model for hardness. …”
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