Performance comparison of neural network training algorithms in modeling of bimodal drug delivery

Ghaffari, Alireza; Abdollahi, Hamid; Khoshayand, Mohammad Reza; Bozchalooi, Iman Soltani; Dadgar, Amin; Rafiee-Tehrani, Morteza

doi:10.1016/j.ijpharm.2006.07.056

Cited by 196 publications

(146 citation statements)

References 28 publications

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“…This software has a graphical user interface (GUI) that supports different types of training algorithms. This enables the user to load the data sets, design the network structure, select the training algorithm, and generate the different models for each output variable in a single operation [49]. The networks were trained with incremental backpropagation as this has been reported to be the most commonly used algorithm [34].…”

Section: Ann Analysismentioning

confidence: 99%

“…The predictive ability of the ANN methods is assessed on the basis of the mean square error (MSE), root mean squared error (RMSE), the coefficient of determination (R 2 ), and the absolute average deviation (AAD) [49] between the predicted values of the network and the actual values, which are calculated by Equations (4)- (7) as follows:…”

Section: Predictability Of Model Evaluated In Artificial Neural Netwomentioning

confidence: 99%

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Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

2018

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Abstract:Bioethanol production has seen an increasing trend in research recently, with a focus on increasing its economic viability. The aim of this study is to develop a low-cost fermentation medium with a minimum of redundant nutritional supplements, thereby minimizing the costs associated with nutritional supplements and seed production. Corn steep liquor (CSL) in glucose fermentation by Saccharomyces Type 1 (ST1) strain and Anchor Instant Yeast (AIY), which are low-cost media, is used as a replacement for yeast extract (YE). The fermentation process parameters were optimized using artificial neural networks (ANN) and the response surface method (RSM). The study shows that for CSL, maximum average ethanol concentrations of 41.92 and 45.16 g/L, representing 82% and 88% of the theoretical yield, were obtained after 36 h of fermentation in a shake flask for ST1 and AIY, respectively. For YE, ethanol concentrations equivalent to 86% and 88% of theoretical yield were obtained with ST1 and AIY, respectively after 48 h. Although ANN better predicted the responses compared to RSM, optimum conditions were better predicted by RSM. This study shows that corn steep liquor is an inexpensive potential nutrient that may have significant cost implications for commercial ethanol production.

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Section: Ann Analysismentioning

confidence: 99%

Section: Predictability Of Model Evaluated In Artificial Neural Netwomentioning

confidence: 99%

Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

2018

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“…The BPXNC then reads the input and output values in the training dataset and changes the value of the weighted links to reduce the differentiation between the predicted and observed values. The error in prediction is reduced across several training cycles (epoch 50) until the network reaches the best level of classification accuracy while avoiding overfitting [23].…”

Section: Experimental Designmentioning

confidence: 99%

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Idowu

Fergus

Hussain

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-Worldwide the rate of preterm birth is increasing, which presents significant health, developmental and economic problems. Current methods for predicting preterm births at an early stage are inadequate. Yet, there has been increasing evidence that the analysis of uterine electrical signals, from the abdominal surface, could provide an independent and easy way to diagnose true labour and predict preterm delivery. This analysis provides a heavy focus on the use of advanced machine learning techniques and Electrohysterography (EHG) signal processing. Most EHG studies have focused on true labour detection, in the window of around seven days before labour. However, this paper focuses on using such EHG signals to detect preterm births. In achieving this, the study uses an open dataset containing 262 records for women who delivered at term and 38 who delivered prematurely. The synthetic minority oversampling technique is utilized to overcome the issue with imbalanced datasets to produce a dataset containing 262 term records and 262 preterm records. Six different artificial neural networks were used to detect term and preterm records. The results show that the best performing classifier was the LMNC with 96% sensitivity, 92% specificity, 95% AUC and 6% mean error.

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“…The yi is the net input to node j in hidden or output layer, the weight related to neuron i and neuron j are indicated as wij, xi is the input of neuron j, bj is the bias connected to node j (29). Sigmoidal transfer function is usuallyused for nonlinear relationship (30,31). The general form of this function is showed below (28):…”

Section: Ann Descriptionmentioning

confidence: 99%

“…Nevertheless, it is very hard to choose the number of hidden layers (30). Most of literatures indicate that one hidden layer is good enough to validate the prediction and maybe the best to decide for all applied feed-forward network design (38).…”

Section: Ann Modelingmentioning

confidence: 99%

Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran

2013

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Background: Access to safe drinking water is one of the basic human rights and essential for healthy life. Concerns about the effects of copper on human health have led to numerous guidelines and regulations limiting its concentrations in water. Objectives: The major goal of this study is to demonstrate artificial neural network model of the Chahnimeh1 reservoir water quality (Heavy metal concentration) and show the potential of the ANN for producing models capable of efficient forecasting of Cu concentration. Materials and Methods: Water samples were collected from Chahnimeh1 reservoir which was the most important source of drinking water in Sistan-balochistan and analyzed for physical quality parameters such as: EC (electric conductivity), TDS(total dissolved solids), T(temperature), pH and heavy metal (Cu) concentration using standard methods. In this study, a three-layer artificial neural network (ANN) model was investigated to predict the Cu concentration in the water of Chahnimeh1 reservoir. The input variables are electric conductivity, total dissolved solids, temperature and pH, while the Cu concentration in water is the output. We applied The Levenberg-Marquardt (LM) algorithm to train ANN. Results: According to the ANN outputs, hidden layer with 7 neurons had the best performance for predicting Cu concentration. Evaluation indexes including MSE and R in this article were obtained as 0.00008 and 0.9346; 0.00019 and 0.8612; 0.00014 and 0.9372 for training, validation and testing date sets respectively. Conclusions: As we can see the ANN outputs values are very close to actual Cu concentration, so indicating that predicted values are accurate and the network design is proper and the input variables well suitable for the prediction of Cu concentration.

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Performance comparison of neural network training algorithms in modeling of bimodal drug delivery

Cited by 196 publications

References 28 publications

Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran

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