International Journal of Computer and Applications

A morphable model for the synthesis of 3D faces

/pdf/sec-x-2-recommended-elliptic-curve-domain-parameters-54wojro6fg.pdf

SEC X.2: Recommended Elliptic Curve Domain Parameters

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

/pdf/comprehensive-review-of-artificial-neural-network-4sb2zr5avn.pdf

Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Book review: Applied cryptography: Protocols, algorithms, and source code in C

To recognize application of Artificial Neural Networks (ANNs) in weather forecasting, especially in rainfall forecasting a comprehensive literature review from 1923 to 2012 is done and presented in this paper. And it is found that architectures of ANN such as BPN, RBFN is best established to be forecast chaotic behavior and have efficient enough to forecast monsoon rainfall as well as other weather parameter prediction phenomenon over the smaller geographical region.

/pdf/application-of-artificial-neural-networks-in-weather-30ru29lkua.pdf

Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review

The utilization of back-propagation neural network in identification of internal dynamics of chaotic motion is found appropriate. However, during its training through Rumelhart algorithm, it is found that, a high learning rate ( ) leads to rapid learning but the weights may oscillate, while a lower value of ` ' leads to slower learning process in weight updating formula   Momentum factor ( ) is to accelerate the convergence of error during the training in the equation   and   while transfer function sigmoid  . It is the most complicated and experimental task to identify optimum value of ` ' and ` ' during the training. To identify optimum value of   ` ' and  ` ' , firstly the network is trained with 103 epochs under different values of  ` ' in the close interval   and   At   the convergence of initial weights and minimization of error (i.e., mean square error) process is found appropriate. Afterwards to find optimum value of  , the network was trained again with   = 0.3 (fixed) and with different values of   in the close interval   for 103 epochs. It was observed that the convergence of initial weights and minimization of error was appropriate with  = 0.3 and   = 0.9. On this optimum value of   and   the network was trained successfully from local minima of error = 1.67029292416874E-03 at 103 epochs to global minima of error = 4.99180426869658E-04 at 15   105 epochs. At the global minima, the network has exhibited excellent performance in identification of internal dynamics of chaotic motion and in prediction of future values by past recorded data series. These essentials are presented through this research paper.

https://journalskuwait.org/kjs/index.php/KJS/article/download/394/44

Impact of learning rate and momentum factor in the performance of back-propagation neural network to identify internal dynamics of chaotic motion

Attempts to predict long-range monsoon rainfall over the subdivision EPMB, Three layer perception feed forward back propagation deterministic and probabilistic artificial neural network models have been developed. 61 years data for 1945–2006 have been used, of which the first 51 years (1945–1995) of data are used for training the network and data for the period 1996–2006 are used independently for validation. We have found that the mean absolute deviation (% of mean) of the model is less than and half of the standard deviation (% of mean) in the independent period (1996-2006) of the subdivision in deterministic forecast. Correlation between actual and particular model predicted values are more than 0.8 for the districts and 0.7 for the whole subdivision in deterministic forecast. However performance of the model in probabilistic forecast is better evaluated over deterministic forecast. The models developed and their evaluations have been presented in this paper.

Long-Range Monsoon Rainfall Pattern Recognition and Prediction for the Subdivision 'EPMB' Chhattisgarh Using Deterministic and Probabilistic Neural Network

An optimum architecture backpropagation neural network for rainfall–runoff modeling is rarely visible in terms of number of input vector, hidden neurons, learning rate, momentum factor, number of epochs for training up to global minima, and error minimized at global minima. These parameters are optimized to forecast total rainfall–runoff from Basantpur station of Mahanadi river basin of central India situated at 80°28′ and 86°43′E longitudes as well as 19°8′ and 23°32′N latitudes, with a breadth 587 and 400 km as case study. Intended for four optimum models for the month of July (5, 2, 0.31, 0.72, 5,000,000, 0.001787722), August (8, 2, 0.21, 0.71, 7,500,000, 0.004556357), September (3, 2, 0.31 0.89, 20,000,000, 0.007621867), and October (8, 2, 0.24, 0.85, 7,500,000, 0.001587539) are experimentally constructed. The performance of these models with reference to correlation coefficient between real and simulated models during training and testing time is found excellent except for model of September. Wherein, correlation coefficient as 0.95, 0.71, 0.94 for the training period and 0.86, 0.53, 0.88 for the testing period are found for the corresponding models of July, August, and October, respectively. It is concluded that the optimum backpropagation neural networks are successful in rainfall–runoff modeling over a river basin for forecast of total rainfall discharge. While, for any application an optimum architecture is pre-requisite to find most favorable performance; however, it does not indicate that model will perform well has been seen in the case of September. These facts and results are discussed in this research article.

Performance of optimum neural network in rainfall–runoff modeling over a river basin

The objective of the study is the application of the artificial neural networks, to the spatial interpolation of rainfall variables. This methodology is applied on mean rainfall variable of 102-rainguage station of Chhattisgarh, India. We have developed 3 layer perceptron feed forward back propagation artificial neural network model and achieved good results are presented through this paper.

Sanjeev Karmakar

Papers

Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review

Impact of learning rate and momentum factor in the performance of back-propagation neural network to identify internal dynamics of chaotic motion

Long-Range Monsoon Rainfall Pattern Recognition and Prediction for the Subdivision 'EPMB' Chhattisgarh Using Deterministic and Probabilistic Neural Network

Performance of optimum neural network in rainfall–runoff modeling over a river basin

Spatial interpolation of rainfall variables using artificial neural network