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1 |
Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussian process regression
Enthalten in Frontiers in energy 20.11.2023: 1-16
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2 |
Predicting the rehydration process of mushroom powder by multiple linear regression (MLR) and artificial neural network (ANN) in different rehydration medium
Enthalten in Journal of food measurement and characterization 19.12.2022: 1-12
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3 |
Predicting the material removal rate during electrical discharge diamond grinding using the Gaussian process regression: a comparison with the artificial neural network and response surface methodology
Enthalten in The international journal of advanced manufacturing technology 8.2.2021: 1-7
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4 |
Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems
Enthalten in Hydrology and earth system sciences Bd. 22, 2018, Nr. 5: 2987-3006
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5 |
Predictive Modelling of Ball Burnishing Process Using Regression Analysis and Neural Network
Enthalten in Materials testing Bd. 55, 2013, Nr. 3: 187-192. 6 S.
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6 |
Comparison between regression and artificial neural network for prediction model of flexibly reconfigurable roll forming process
Enthalten in The international journal of advanced manufacturing technology Bd. 101, 15.12.2018, Nr. 9-12, date:4.2019: 3081-3091
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7 |
Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall
Enthalten in Water resources management Bd. 37, 8.5.2023, Nr. 9, date:7.2023: 3631-3655
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8 |
Gaussian process regression based on deep neural network for reliability analysis in high dimensions
Enthalten in Structural and multidisciplinary optimization Bd. 66, 23.5.2023, Nr. 6, date:6.2023: 1-17
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9 |
Mathematical Regression and Artificial Neural Network for Prediction of Corrosion Inhibition Process of Steel in Acidic Media
Enthalten in Journal of bio- and tribo-corrosion Bd. 6, 29.6.2020, Nr. 3, date:9.2020: 1-10
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10 |
Predicting of acid red 14 removals from synthetic wastewater in the advanced oxidation process using artificial neural networks and fuzzy regression
Enthalten in Rendiconti lincei / Scienze fisiche e naturali Bd. 33, 7.1.2022, Nr. 1, date:3.2022: 115-126
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