Plant-based protein extrusion optimization: Comparison between machine learning and conventional experimental design by Yingfen Jiang, Noor Irsyad Bin Noor Azlee, Wing Shan Ko, Kaiqi Chen, Bee Gim Lim, Arif Z. Nelson

“…In contrast, Bayesian Optimization (BO), a machine learning technique, uses probabilistic surrogate models to efficiently explore parameter spaces and optimize black-box functions with fewer experiments. …”
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Optimization of Hydronic Heating System in a Commercial Building: Application of Predictive Control with Limited Data by Rana Loubani, Didier Defer, Ola Alhaj-Hasan, Julien Chamoin

Subjects: “…model predictive control…”
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Frequency Response Function-Based Finite Element Model Updating Using Extreme Learning Machine Model by Yu Zhao, Zhenrui Peng

“…Extreme learning machine (ELM) is introduced as the surrogate model of the finite element model (FEM) to construct the relationship between updating parameters and structural responses. …”
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AI-Driven Meat Food Drying Time Prediction for Resource Optimization and Production Planning in Smart Manufacturing by Rajnish Rakholia, Andres L. Suarez-Cetrulo, Manokamna Singh, Ricardo Simon Carbajo

“…Therefore, implementing an automation solution by developing a predictive model for drying times in meat manufacturing is essential for optimizing the production lifecycle. …”
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin by Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun

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Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study by Mahmoud B Almadhoun, MA Burhanuddin

“…MethodsMultiple ML models are evaluated in this study, including random forest, extreme gradient boosting (XGBoost), support vector machine (SVM), and k. …”
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Forecasting of heat and mass transfer in Casson nanofluid flow with entropy optimization: Machine learning approach by P. Kavya, K. Loganathan, K. Chellapriya, S. Eswaramoorthi, S. Divya, Krishna Prakash Arunachalam, P. Senthilkumar

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Logistics demand prediction using fuzzy support vector regression machine based on Adam optimization by Jing Quan, Yiwen Peng, Liyun Su

“…In this study, we conduct the Fuzzy Support Vector Regression Machine approach based on Adam optimization (FSVR-AD). …”
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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Hyperparameter tuning via Grid Search (GS) and K-fold cross-validation further optimized the models. Among those analyzed, XGBoost and GBR achieved the highest predictive accuracy, with R2 values of 93.49% and 92.09% respectively, coupled with lower mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). …”
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Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes by Stephen R. Spellman, Rodney Sparapani, Martin Maiers, Bronwen E. Shaw, Purushottam Laud, Caitrin Bupp, Meilun He, Steven M. Devine, Brent R. Logan

“…The NFT BART models were adjusted based on recipient, disease, and transplant variables. …”
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Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization by Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal

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A Survey of Machine Learning Techniques for Optimal Capacitor Placement and Sizing in Smart Distribution Networks by Kwabena Addo, Katleho Moloi, Musasa Kabeya, Evans Eshiemogie Ojo

“…This paper presents a comprehensive review of machine learning (ML)-based methodologies for optimal capacitor placement and sizing, focusing on their ability to enhance voltage stability, minimize power losses, and improve overall grid efficiency in smart distribution networks. …”
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Prediction and optimization of stretch flangeability of advanced high strength steels utilizing machine learning approaches by Tianyang Li, Zheng Yang, Junyi Cui, Wenjie Chen, Rami Almatani, Yingjie Wu

“…Support vector machine, symbolic regression, and extreme gradient boosting models accurately predicted hole expansion ratio (HER), ultimate tensile strength (UTS), and total elongation (TE). …”
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Design, Testing, and Optimization of a Filling-Type Silage Crushing, Shredding, and Baling Integrated Machine by Tong Dai, Wei Sun, Danzhu Zhang, Petru A. Simionescu

“…A three-factor, three-level experiment was conducted to evaluate the effects of the hammer blade quantity, blade length, and hammer angle on machine productivity and straw shredding rate. Performance data were analyzed using Design-Expert 10.0.7 software to develop regression models and assess the significance of each factor. …”
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Optimization of Flavor Quality of Lactic Acid Bacteria Fermented Pomegranate Juice Based on Machine Learning by Wenhui ZOU, Fei PAN, Junjie YI, Linyan ZHOU

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Development of Machine Learning Models for Sandface Pressure Prediction in Oil Well by Lorraine P. Oliveira, Raul M. Foronda, Alexandre V. Grillo, Brunno F. dos Santos

“…The optimal DT model configuration was determined through cross-validation, utilizing Scikit-learn’s GridSearchCV for hyperparameter optimization. …”
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Optimal Scheduling Strategy for Micro Energy Internet Under Electric Vehicles Aggregation by Jiakun AN, Shuqiang YANG, Tao WANG, Chunguang HE, Jing ZHANG, Chao YUAN, Chunxia DOU

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Passivity-Based Model-Predictive Control for the Permanent Magnet Synchronous Machine by Alejandro Garcés-Ruiz, Walter Julián Gil González

“…Method: A passivity-based model predictive control (MPC) is proposed, integrating port-Hamiltonian representation with optimization. …”
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Machine learning‐guided plasticity model in refractory high‐entropy alloys by Shang Zhao, Jinshan Li, Weijie Liao, Ruihao Yuan

“…Traditional experimental methods for characterizing this property are time‐consuming and resource‐intensive, necessitating the development of efficient predictive models. In this study, we propose a machine learning approach to predict the fracture strain of RHEAs. …”
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Machine Learning Models and Mathematical Approaches for Predictive IoT Smart Parking by Vesna Knights, Olivera Petrovska, Jasmina Bunevska-Talevska, Marija Prchkovska

“…The lagged features were able to capture the temporal dependencies more effectively than the other models, resulting in lower RMSE values. The LightGBM model with lagged data produced an R² of 0.9742 and an RMSE of 0.1580, making it the best model for time series prediction. …”
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