Novel mathematical and polypharmacology predictions of salicylsalicylic acid: Solubility enhancement through SCCO2 system

Zhang, Peijun; Albaghdadi, Mustafa Fahem; AbdulAmeer, Sabah Auda; Altamimi, Abdulmalik Saleh Alfawaz; Abdulrazzaq, Ali Zeinulabdeen; chailibi, Hayder; Hadrawi, Salema K.; Hamdan, Hassan Falih; Altalbawy, Farag M. A.; Alsubaiyel, Amal M.

doi:10.1016/j.molliq.2022.121195

Cited by 2 publications

(5 citation statements)

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“…A variety of neural networks can be formed based on the connections, neuron model, and weight modification methods [10,21]. Methods such as the Multilayer Perceptron (MLP) of artificial neural networks (ANN) could be employed to mimic the possible hidden correlations between the in and out data of processes [8,10,22].…”

Section: Multilayer Perceptron and Grnnmentioning

confidence: 99%

“…Updates and optimizations based on work complexity enable a variable approach to hidden layer size. The MLP system's artificial neurons are structured in a three-layered network [8,10,23].…”

Section: Multilayer Perceptron and Grnnmentioning

confidence: 99%

“…The activation function, f(z), can be calculated using a number of continuously differentiable functions, including the more modern ReLU, which is widely employed in the method of deep learning [8,24,25].…”

Section: Multilayer Perceptron and Grnnmentioning

confidence: 99%

“…Using a wide variety of methods, these models determine if there is a connection between variables [5][6][7]. In this research, three methods are implemented: Multiple Layers Perceptrons (MLP), Decision Tree (DT), and GRNN for estimation of crude oil viscosity based on compositional data [8].…”

Section: Introductionmentioning

confidence: 99%

“…We start at the root of the DT and move our way down the tree based on the value of each characteristic in each node's subtree. This procedure is repeated until no more leaves or nodes remain [8,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning estimation of crude oil viscosity as function of API, temperature, and oil composition: Model optimization and design space

Zhang

Kang

2023

PLoS ONE

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Measurement of viscosity of crude oil is critical for reservoir simulators. Computational modeling is a useful tool for correlation of crude oil viscosity to reservoir conditions such as pressure, temperature, and fluid compositions. In this work, multiple distinct models are applied to the available dataset to predict heavy-oil viscosity as function of a variety of process parameters and oil properties. The computational techniques utilized in this work are Decision Tree (DT), MLP, and GRNN which were utilized in estimation of heavy crude oil samples collected from middle eastern oil fields. For the estimation of viscosity, the firefly algorithm (FA) was employed to optimize the hyper-parameters of the machine learning models. The RMSE error rates for the final models of DT, MLP, and GRNN are 40.52, 25.08, and 30.83, respectively. Also, the R2-scores are 0.921, 0. 978, and 0.933, respectively. Based on this and other criteria, MLP is chosen as the best model for this study in estimating the values of crude oil viscosity.

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