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Rheological behavior of MWCNT-SnO2/SAE50 hybrid nanolubricant: Experimental evaluation and viscosity prediction using optimized machine learning model
Published 2025-08-01“…The proposed MLPNN optimization strategy, through the optimal selection of parameters such as the number of neurons of hidden layers (HLs), transfer functions of HLs, the transfer function of the output layer, and the training algorithm, provided significant efficiency in developing single HL (R2 = 0.99979) and double HLs (R2 = 0.99996) MLPNN models.…”
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Temperature Compensation Method for MEMS Ring Gyroscope Based on PSO-TVFEMD-SE-TFPF and FTTA-LSTM
Published 2025-04-01“…For compensation, the football team training algorithm (FTTA) is used to optimize the parameters of the long short-term memory (LSTM) neural network, forming a novel FTTA-LSTM architecture. …”
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Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path
Published 2020-01-01“…The ANN is a Multilayer Perceptron Network (MLP) that uses the Levenberg-Marquardt training algorithm and cross-validation method. The NFS is an Adaptive Neuro-Fuzzy Inference System (ANFIS) that uses the model Sugeno. …”
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Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data
Published 2025-07-01“…Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. …”
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Artificial Neural Network Prediction on negative and positive activation energy of magnetohydrodynamic nanofluid flow with multiple slips
Published 2025-09-01“…Another important matter to discuss here that we have used feed-forward back-propagation multilayer perceptron artificial neural network with Levenberg-Marquard algorithm as the training algorithm to predict the Sherwood number for both activation energy values 1 and -1. …”
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Evaluating the generalizability of an automated coronary artery calcium segmentation and scoring algorithm using multi-vendor dataset
Published 2025-07-01“…Furthermore, a feasibility study using non-contrast chest CT scans indicates that the performance of our cardiac CT-trained algorithm on chest CT images was acceptable to a certain extent.…”
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