scholarly journals Role of Feed Forward Neural Networks Coupled with Genetic Algorithm in Capitalizing of Intracellular Alpha-Galactosidase Production byAcinetobactersp.

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Sirisha Edupuganti ◽  
Ravichandra Potumarthi ◽  
Thadikamala Sathish ◽  
Lakshmi Narasu Mangamoori
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Squared Error ◽  
Feed Forward Neural Networks ◽  
Alpha Galactosidase

Alpha-galactosidase production in submerged fermentation byAcinetobactersp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), andR2-value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL.

Missing Data Approaches to Classification

2010 ◽  
pp. 187-209
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Missing Data ◽  
Network Model ◽  
Network Models ◽  
Feed Forward Neural Network ◽  
Neural Network Models ◽  
Feed Forward ◽  
Missing Data Problem ◽  
Feed Forward Neural Networks

In this chapter, a classifier technique that is based on a missing data estimation framework that uses autoassociative multi-layer perceptron neural networks and genetic algorithms is proposed. The proposed method is tested on a set of demographic properties of individuals obtained from the South African antenatal survey and compared to conventional feed-forward neural networks. The missing data approach based on the autoassociative network model proposed gives an accuracy of 92%, when compared to the accuracy of 84% obtained from the conventional feed-forward neural network models. The area under the receiver operating characteristics curve for the proposed autoassociative network model is 0.86 compared to 0.80 for the conventional feed-forward neural network model. The autoassociative network model proposed in this chapter, therefore, outperforms the conventional feed-forward neural network models and is an improved classifier. The reasons for this are: (1) the propagation of errors in the autoassociative network model is more distributed while for a conventional feed-forward network is more concentrated; and (2) there is no causality between the demographic properties and the HIV and, therefore, the HIV status does change the demographic properties and vice versa. Therefore, it is better to treat the problem as a missing data problem rather than a feed-forward problem.

scholarly journals Data Mining for Healthcare Data: A Comparison of Neural Networks Algorithms

2017 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
Debby E. Sondakh
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Error Rates ◽  
Large Dataset ◽  
Training Time ◽  
Healthcare Data ◽  
Squared Error ◽  
Hidden Layer

Classification has been considered as an important tool utilized for the extraction of useful information from healthcare dataset. It may be applied for recognition of disease over symptoms. This paper aims to compare and evaluate different approaches of neural networks classification algorithms for healthcare datasets. The algorithms considered here are Multilayer Perceptron, Radial Basis Function, and Voted Perceptron which are tested based on resulted classifiers accuracy, precision, mean absolute error and root mean squared error rates, and classifier training time. All the algorithms are applied for five multivariate healthcare datasets, Echocardiogram, SPECT Heart, Chronic Kidney Disease, Mammographic Mass, and EEG Eye State datasets. Among the three algorithms, this study concludes the best algorithm for the chosen datasets is Multilayer Perceptron. It achieves the highest for all performance parameters tested. It can produce high accuracy classifier model with low error rate, but suffer in training time especially of large dataset. Voted Perceptron performance is the lowest in all parameters tested. For further research, an investigation may be conducted to analyze whether the number of hidden layer in Multilayer Perceptron’s architecture has a significant impact on the training time.

Use of Artificial Neural Networks and Arima to Forecasting Consumption Sawnwood of Pinus sp. in Brazil

2019 ◽  
Vol 21 (1) ◽  
pp. 51-61 ◽  
Author(s):  
D.A. Buratto ◽  
R. Timofeiczyk Junior ◽  
J.C.G.L. Silva ◽  
J.R. Frega ◽  
M.S.S.A. Wiecheteck ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Arima Model ◽  
Secondary Data ◽  
Information Criterion ◽  
Absolute Error ◽  
Percentage Error ◽  
Explanatory Variables ◽  
Artificial Neural

The objective of this study was to analyze the application of an artificial neural networks model and an ARIMA model to predict the consumption of sawnwood of pine. For this, we use real and secondary data collected and obtained from a historical data source, corresponding to the period from 1997 to 2016, which were later tested to generate the forecast models. Based on economic and statistical criteria, six explanatory variables were used to fit the best model. The choice of the model was made based on Mean Squared Error, Mean Absolute Error, Theil U metric, Percentage Error of Forecast and Akaike value information criterion. The results indicated that the models generated through the ARIMA model presented better performance when compared to the artificial neural network. The best adjusted model estimated a reduction of 1.33% in consumption of sawnwood of pine in Brazil for the period between 2017 and 2020.

scholarly journals A Neural Network Approximation Based on a Parametric Sigmoidal Function

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 262 ◽  
Author(s):  
Beong Yun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Approximation Method ◽  
Closed Interval ◽  
Activation Function ◽  
Sigmoidal Function ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Extended Application ◽  
Feed Forward Neural Networks

It is well known that feed-forward neural networks can be used for approximation to functions based on an appropriate activation function. In this paper, employing a new sigmoidal function with a parameter for an activation function, we consider a constructive feed-forward neural network approximation on a closed interval. The developed approximation method takes a simple form of a superposition of the parametric sigmoidal function. It is shown that the proposed method is very effective in approximation of discontinuous functions as well as continuous ones. For some examples, the availability of the presented method is demonstrated by comparing its numerical results with those of an existing neural network approximation method. Furthermore, the efficiency of the method in extended application to the multivariate function is also illustrated.

scholarly journals Stock Market Prediction Performance of Neural Networks: A Literature Review

2017 ◽  
Vol 9 (11) ◽  
pp. 100 ◽  
Author(s):  
Özgür Ican ◽  
Taha Bugra Çelik
Keyword(s):  
Neural Networks ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Absolute Error ◽  
Absolute Deviation ◽  
Error Measures ◽  
Squared Error ◽  
Number Of Publications

In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures - namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) - have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

scholarly journals COVID-19 Infection Forecasting based on Deep Learning in Iran

2020 ◽  
Author(s):  
Mehdi Azarafza ◽  
Mohammad Azarafza ◽  
Jafar Tanha
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
National Level ◽  
Absolute Error ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
Error Metrics ◽  
Squared Error ◽  
Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

scholarly journals Algorithm for calculating synapse weights of the first layer of a neural network on the base of metric recognition methods. Part 1.

2020 ◽  
pp. 20-30
Author(s):  
Polad Geidarov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Three Dimensional ◽  
Analytical Calculation ◽  
Training Algorithms ◽  
Feed Forward Neural Network ◽  
Software Environment ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
And Training

Introduction: Metric recognition methods make it possible to preliminarily and strictly determine the structures of feed-forward neural networks, namely, the number of neurons, layers, and connections based on the initial parameters of the recognition problem. They also make it possible to analytically calculate the synapse weights of network neurons based on metric expressions. The setup procedure for these networks includes a sequential analytical calculation of the values of each synapse weight in the weight table for neurons of the zero or first layer, which allows us to obtain a working feed-forward neural network at the initial stage without the use of training algorithms. Then feed-forward neural networks can be trained by well-known learning algorithms, which generally speeds up the process of their creation and training. Purpose: To determine how much time the process of calculating the values of weights requires and, accordingly, how reasonable it is to preliminarily calculate the weights of a feed-forward neural network. Results: An algorithm is proposed and implemented for the automated calculation of all values of synapse weight tables for the zero and first layers as applied to the task of recognizing black-and-white monochrome symbol images. The proposed algorithm is described in the Builder C++ software environment. The possibility of optimizing the process of calculating the weights of synapses in order to accelerate the entire algorithm is considered. The time spent on calculating these weights for different configurations of neural networks based on metric recognition methods is estimated. Examples of creating and calculating synapse weight tables according to the considered algorithm are given. According to them, the analytical calculation of the weights of a neural network takes just seconds or minutes, being in no way comparable to the time necessary for training a neural network. Practical relevance: Analytical calculation of the weights of a neural network can significantly accelerate the process of creating and training a feed-forward neural network. Based on the proposed algorithm, we can implement one for calculating three-dimensional weight tables for more complex images, either blackand-white or color grayscale ones.

scholarly journals Algorithm for calculating synapse weights of the first layer of a neural network on the base of metric recognition methods. Part 2

2020 ◽  
pp. 25-38
Author(s):  
Polad Geidarov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Analytical Calculation ◽  
Training Algorithms ◽  
Feed Forward Neural Network ◽  
Software Environment ◽  
Feed Forward ◽  
Black And White ◽  
Feed Forward Neural Networks ◽  
And Training

Introduction: Metric recognition methods make it possible to preliminarily and strictly determine the structures of feed-forward neural networks, namely, the number of neurons, layers, and connections based on the initial parameters of the recognition problem. They also make it possible to analytically calculate the synapse weights of network neurons based on metric expressions. The setup procedure for these networks includes a sequential analytical calculation of the values of each synapse weight in the weight table for neurons of the zero or first layer, which allows us to obtain a working feed-forward neural network at the initial stage without the use of training algorithms. Then feed-forward neural networks can be trained by well-known learning algorithms, which generally speeds up the process of their creation and training. Purpose: To determine how much time the process of calculating the values of weights requires and, accordingly, how reasonable it is to preliminarily calculate the weights of a feed-forward neural network. Results: An algorithm is proposed and implemented for the automated calculation of all values of synapse weight tables for the zero and first layers as applied to the task of recognizing black-and-white monochrome symbol images. The proposed algorithm is described in the Builder C++ software environment. The possibility of optimizing the process of calculating the weights of synapses in order to accelerate the entire algorithm is considered. The time spent on calculating these weights for different configurations of neural networks based on metric recognition methods is estimated. Examples of creating and calculating synapse weight tables according to the considered algorithm are given. According to them, the analytical calculation of the weights of a neural network takes just seconds or minutes, being in no way comparable to the time necessary for training a neural network. Practical relevance: Analytical calculation of the weights of a neural network can significantly accelerate the process of creating and training a feed-forward neural network. Based on the proposed algorithm, we can implement one for calculating three-dimensional weight tables for more complex images, either black and-white or color grayscale ones.

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