BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System

Mei, Yu; Yang, Jing; Lu, Yin; Hao, Feilin; D, Xu; Pan, Hua; Wang, Jiade

doi:10.3390/ijerph16142454

Cited by 21 publications

(6 citation statements)

References 34 publications

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“…c) If the output layer does not get the expected output, calculate the error change value of the output layer, propagate the error signal back along the original connection path through the network, and then modify the weights of each layer until the desired goal is achieved. The BPANN model can theoretically approximate any continuous function with arbitrary precision (Mei et al 2019;Jun et al 2020).…”

Section: Bpann Modeling Strategymentioning

confidence: 99%

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Chen

Shao

et al. 2022

J Anal Sci Technol

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Mefatinib (MET306) is a novel second-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) designed to address the highly unmet clinical need of gefitinib-induced resistance and irreversibly bind to mutated tyrosine kinase domain of EGFR and human epidermal growth factor receptor 2 (HER2). In this study, a liquid chromatography–tandem mass spectrometry method was established and validated for determining MET306 in non-small cell lung cancer patients and a backpropagation artificial neural network was developed and constructed to predict the pharmacokinetic process. The mobile phase was water containing 5 mM ammonium acetate and acetonitrile at a flow rate of 0.3 mL min−1, within a 4.5 min run time. MET306 was separated on a Hypersil Gold-C18 at 40 °C and subjected to mass analysis using positive electrospray ionization. A total of 524 data were used as development groups and 145 data were used as testing groups. The final established Northern Goshawk Optimization-Backpropagation Artificial Neural Network (NGO-BPANN) model consisted of one input layer with 6 neurons, 1 hidden layer with 10 nodes, and 1 output layer with one node processed by MATLAB2021a.The calibration range of MET306 was 0.5–200 ng mL−1 with the correlation coefficient r ≥ 0.99. Accuracies ranged from 97.20 to 110.80% and the inter- and intra-assay precision were less than 15%. The ranges of extraction recoveries were 104.95% to 112.09% for analyte and internal standard and there was no significant matrix effect. The storage stability under different conditions was in accordance with the bioanalytical guidelines. The time-concentration profiles of the measured and predicted concentrations of MET306 by NGO-BPANN agree well. An NGO-BPANN model was developed to predict the plasma concentration and pharmacokinetic parameters of MET306 in the first time.

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Section: Bpann Modeling Strategymentioning

confidence: 99%

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Chen

Shao

et al. 2022

J Anal Sci Technol

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“…The DEA-BP model is applied to the prediction of teacher's teaching evaluation results. The prediction error of DEA-BP model is compared with that of the Gradient Boosting Decision Tree (GBDT) algorithm [19] and the improved BP neural network model based on Particle Swarm Optimization (PSO-BP) algorithm [20]. The results are shown in Table 3.…”

Section: Verification Of Fast Apriori Algorithm Based On the Decision Treementioning

confidence: 99%

Intelligent Decision System of Higher Educational Resource Data under Artificial Intelligence Technology

2021

Int. J. Emerg. Technol. Learn.

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It aims to apply the neural network algorithm to the mining of educational resource data and provide new ideas for the intelligent development of teaching evaluation. The potential correlations between the teaching evalua-tion results and the teacher’s age, gender, professional title, and academic qualification are analyzed with the Apriori algorithm, which is improved with the decision tree based on the research of the existing university teaching evaluation system. The back propagation (BP) neural network model is improved based on the differential evolution algorithm (DEA). The DEA-BP model is applied to the prediction of teaching evaluation results for analysis. The results show that the execution time of the improved association rule algorithm (ARA) is significantly better than that of other models. In addition, the teacher’s age (40 - 50 years old or 50 - 60 years old), gender (female), professional title (senior or deputy senior), and academic qualifications (undergraduate or master) have certain correlation with the teaching evaluation results (excellent). When the DEA-BP algorithm is adopted to predict the teaching evaluation results, the average absolute error (1.05%) and the relative accuracy rate (95.44%) between its prediction value and the true value are optimal. Therefore, the ARA algorithm and DEA-BP algorithm based on the decision tree can intelligently extract the potential laws and knowledge in the teaching evaluation data, and provide support for teaching evaluation decisions. Thus, it exerts the role of promotion in the mining of educational resource data in universities and the intelligent development of decision-making systems

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“…Improvement in the prediction accuracy of the PSO-based hybrid model has been documented in many previous studies. Mei et al [18] introduced PSO into ANN in a electro-oxidation system and achieved accurate predictions with R 2 of 0.99 and 0.9944 for COD removal and total energy consumption, respectively. Khajeh et al [19] validated the hybrid model, ANN-PSO, which was robust in modelling Mn(II) and Co(II) removal efficiency in adsorption (R 2 was 0.942 and 0.944 for Mn(II) and Co(II)alt, respectively).…”

Section: Application Of Hybrid Ann On Modelling the Watermentioning

confidence: 99%

Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

Lin

Luo

Zhang

et al. 2021

Mathematical Problems in Engineering

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Domestic sewage in rural regions is mainly treated by small-scale treatment terminals in China. The large quantities and high dispersion of these terminals render the chemical measurement of effluent to be a time and energy intensive work and further hinder the efficient surveillance of terminals’ performance. After a thorough investigation of 136 operating terminals, this study successfully employs two artificial neural network (ANN) models to predict effluent total nitrogen (TN) and COD (R2 both higher than 0.8) by setting some easily detectable parameters, e.g., pH and conductivity, as inputs. To prevent ANN models getting stuck on local optima and enhance the model performance, genetic algorithm (GA) and particle swarm optimization (PSO) are introduced into ANN, respectively. By comparison, ANN-PSO excels in modelling both TN and COD. The root mean square error (RMSE) and R2 of ANN-PSO in modelling TN are 9.14 and 0.90, respectively, in the training stage, and 11.54 and 0.90, respectively, in the validation stage. The RMSE and R2 of ANN-PSO in modelling COD are 22.10 and 0.90, respectively, in the training stage, and 26.57 and 0.85, respectively, in the validation stage. This is the first study to provide performance prediction models that are available for different terminals. Two established ANN-PSO models show great practical significance in monitoring huge amounts of terminals despite the slight sacrifice of models’ accuracy caused by the great heterogeneity of different terminals.

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BP–ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System

Cited by 21 publications

References 34 publications

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Development and validation of a new LC–MS/MS method for the determination of mefatinib in human plasma and its first application in pharmacokinetic studies

Intelligent Decision System of Higher Educational Resource Data under Artificial Intelligence Technology

Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

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