scholarly journals Determination of the reduction coefficients in a continuous finishing group of stands in a hot rolling mill using artificial neural network

2020 ◽  
Vol 966 ◽  
pp. 012101
Author(s):  
O R Latypov ◽  
S I Platov ◽  
R R Dema ◽  
N V Urtsev
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hot Rolling ◽  
Rolling Mill ◽  
Hot Rolling Mill ◽  
Artificial Neural

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals Determination of <i>Plasmodium</i> Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network

2014 ◽  
Vol 04 (02) ◽  
pp. 78-88 ◽  
Author(s):  
Lucy Gitonga ◽  
Daniel Maitethia Memeu ◽  
Kenneth Amiga Kaduki ◽  
Mjomba Allen Christopher Kale ◽  
Njogu Samson Muriuki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Life Stages ◽  
Blood Smears ◽  
Artificial Neural ◽  
Thin Blood Smears

scholarly journals Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yadollah Abdollahi ◽  
Azmi Zakaria ◽  
Nor Asrina Sairi ◽  
Khamirul Amin Matori ◽  
Hamid Reza Fard Masoumi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visible Light ◽  
Visible Light Irradiation ◽  
Back Propagation ◽  
Light Irradiation ◽  
Maximum Efficiency ◽  
Data Set ◽  
Artificial Neural

The artificial neural network (ANN) modeling ofm-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration ofm-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.

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