scholarly journals Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II)

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1540 ◽  
Author(s):  
Guo Zhao ◽  
Hui Wang ◽  
Gang Liu ◽  
Zhiqiang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Accurate Determination ◽  
Back Propagation ◽  
Voltammetric Sensor ◽  
Artificial Neural

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.

scholarly journals Simultaneous Determination of Some Components in Detergent Washing Powder by Mid-Infrared Spectrometry and Artificial Neural Network

2011 ◽  
Vol 94 (1) ◽  
pp. 322-326
Author(s):  
Mohammadreza Khanmohammadi ◽  
Amir Bagheri Garmarudi ◽  
Mohammad Babaei Rouchi ◽  
Nafiseh Khoddami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simultaneous Determination ◽  
Sodium Tripolyphosphate ◽  
Back Propagation ◽  
Infrared Spectrometry ◽  
Feed Forward Back Propagation ◽  
Powder Samples ◽  
Artificial Neural

Abstract A method has been established for simultaneous determination of sodium sulfate, sodium carbonate, and sodium tripolyphosphate in detergent washing powder samples based on attenuated total reflectance Fourier transform IR spectrometry in the mid-IR spectral region (800–1550 cm−1). Genetic algorithm (GA) wavelength selection followed by feed forward back-propagation artificial neural network (BP-ANN) was the chemometric approach. Root mean square error of prediction for BP-ANN and GA-BP-ANN was 0.0051 and 0.0048, respectively. The proposed method is simple, with no tedious pretreatment step, for simultaneous determination of the above-mentioned components in commercial washing powder samples.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

A Bayesian Regularized Artificial Neural Network for Simultaneous Determination of Loratadine, Naproxen and Diclofenac in Wastewaters

2020 ◽  
Vol 16 (8) ◽  
pp. 1083-1092
Author(s):  
Mojtaba Mohammadpoor ◽  
Roya Mohammadzadeh Kakhki ◽  
Hakimeh Assadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simultaneous Determination ◽  
Back Propagation ◽  
Optimum Number ◽  
Mean Square ◽  
Vis Spectroscopy ◽  
Artificial Neural ◽  
Mean Square Errors

Background:: Simultaneous determination of medication components in pharmaceutical samples using ordinary methods have some difficulties and therefore these determinations usually were made by expensive methods and instruments. Chemometric methods are an effective way to analyze several components simultaneously. Objective:: In this paper, a novel approach based on Bayesian regularized artificial neural network is developed for the determination of Loratadine, Naproxen, and Diclofenac in water using UV-Vis spectroscopy. Methods: A dataset is collected by performing several chemical experiments and recording the UV-Vis spectra and actual constituent values. The effect of a different number of neurons in the hidden layer was analyzed based on final mean square error, and the optimum number was selected. Principle Component Analysis (PCA) was also applied to the data. Other back-propagation methods, such as Levenberg-Marquardt, scaled conjugate gradient, and resilient backpropagation, were tested. Results:: In order to see the proposed network performance, it was performed on two crossvalidation methods, namely partitioning data into train and test parts, and leave-one-out technique. Mean square errors between expected results and predicted ones implied that the proposed method has a strong ability in predicting the expected values. Conclusion:: he results showed that the Bayesian regularization algorithm has the best performance among other methods for simultaneous determination of Loratadine, Naproxen, and Diclofenac in water samples.

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.

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