A Self-Adaptive Contractive Algorithm for Enhanced Dynamic Phasor Estimation

To overcome challenges with in vivo digestibility assessment, in vitro digestibility techniques have been created. The biobased additive in concentrate can effectively promote the in vitro digestibility performance. The primary goal of this study was to evaluate the propolis effect on produced gas by an in vitro procedure in 25−75% proportion of concentrate. Then, machine learning (ML) models such as nonlinear regression techniques and multilayer perceptron neural networks (MLP-NNs) were applied to assess the prediction performance of gas generation during in vitro digestion using propolis treatments with diverse diet components. The MLP-NN was created using 11 nonlinear regression (NLR) and 12 training algorithms. The findings revealed that the logistic−exponential without lag time (LE 0 ) model was chosen as the best nonlinear model for eight diet interventions. Also, the achievements of the MLP-NN model evaluation revealed that the trainbr training procedure with six neurons in the hidden layer can accurately predict the gas output. The prediction errors of the MLP-NN and NLR approaches were not significantly different (R 2 ∼ 0.99). Three-dimensional response surface graphs were drawn with the help of a neural network, and the optimal value was calculated based on it in a simple and intuitive way. This route displayed the optimum propolis treatment, in which the application of 75% propolis ethanol extract in concentrate could significantly increase gas production between 1 and 4 mL/h. The MLP-NN model has more capabilities than NLR in such studies.

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Section: Methodsmentioning

confidence: 99%

Toward Modeling the In Vitro Gas Production Process by Using Propolis Extract Oil Treatment: Machine Learning and Kinetic Models

Vakili

Ehtesham

Mesgaran

et al. 2022

Ind. Eng. Chem. Res.

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Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning

Rahimi

Abbaspour‐Fard

Rohani

2022

Journal of Power Sources

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Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques

Pourramezan,

Rohani

2024

SAE Int. J. Fuels Lubr.

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<div>This study explores the effectiveness of two machine learning models, namely multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), in advancing maintenance management based on engine oil analysis. Data obtained from a Mercedes Benz 2628 diesel engine were utilized to both train and assess the MLP-NN and ANFIS models. Six indices—Fe, Pb, Al, Cr, Si, and PQ—were employed as inputs to predict and classify engine conditions. Remarkably, both models exhibited high accuracy, achieving an average precision of 94%. While the radial basis function (RBF) model, as presented in a referenced article, surpassed ANFIS, this comparison underscored the transformative potential of artificial intelligence (AI) tools in the realm of maintenance management. Serving as a proof-of-concept for AI applications in maintenance management, this study encourages industry stakeholders to explore analogous methodologies.</div> <section> <h2>Highlights</h2> <div> <ul> <li> <div>Two machine learning models, multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), were employed to predict and classify the performance condition of diesel engines.</div> </li> <li> <div>Among various training algorithms, Levenberg–Marquardt and the Bayesian regularization demonstrated superior classification accuracy, achieving a 95%–96% range.</div> </li> <li> <div>To assess the generalizability of MLP-NN and ANFIS, the training set size was varied from 90% to 10%.</div> </li> <li> <div>The ANFIS model exhibited greater stability than MLP-NN, with a 50% higher performance.</div> </li> </ul> </div> </section> <section> <h2>Graphical Abstract</h2> <div> <img/> </div> </section>

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A Self-Adaptive Contractive Algorithm for Enhanced Dynamic Phasor Estimation

Cited by 3 publications

References 25 publications

Toward Modeling the In Vitro Gas Production Process by Using Propolis Extract Oil Treatment: Machine Learning and Kinetic Models

Toward Modeling the In Vitro Gas Production Process by Using Propolis Extract Oil Treatment: Machine Learning and Kinetic Models

Synergetic effect of N/O functional groups and microstructures of activated carbon on supercapacitor performance by machine learning

Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques

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