Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing envi...
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Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing environment, which is employed to extend the throughput and reduce the turnaround and reaction time. This paper proposed a new scheduling algorithm called "feedforward neural network in the grid computing (FFNNGC) system," which is used to solve some real-life problems related to the pattern classification. In the proposed method, we have used a feed-forwardalgorithm to find the output in the grid computing network, and the network training is done until the system converges to a minimum error solution. The pattern classification problem consists of 13 real-life, and artificial dataset problems, including two class and multiclass problems. Experiments were performed under these real-life problems, and the results indicated that the proposed method is helpful in such types of problems.
Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) algorithm is a new architecture-based on recurrent neural network which combines a Radial Basis Gated Unit within the Long Short Term Memory (LSTM) network a...
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Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) algorithm is a new architecture-based on recurrent neural network which combines a Radial Basis Gated Unit within the Long Short Term Memory (LSTM) network architecture. This unit then gives an advantage to RBGU-RNN over the existing LSTM network. Firstly, given that the RBGU is just an activation unit and which do not perform any weighted operations as it should in a classical neuron unit, it has an advantage for not propagating (duplicating) error as compared to the LSTM. Secondly, due to the fact that this unit is located at the beginning of the network treatment workflow, it provides standardization to the data set, before they are run into the weighted units, which is not the case of a simple LSTM. This study then provided a theoretical and experimental comparison of the LSTM and RBGU-RNN. Indeed, using a real world call data record, precisely a survey on the end user cell network data traffic, we built up a cellular traffic prediction model. We start with ARIMA model which permit us to choose the number of time steps needed to build the RBGU-RNN prediction model that is the number of time steps needed to predict the next individual in the time series. The results show that RBGU-RNN accurately predict cellular data traffic with great success in generalization than LSTM. The R-squared statistics or determination coefficients show that 58.31 % of user traffic consumption can be explained by LSTM model, while 96.86 % of the user traffic consumption can be done by RBGU-RNN model in the training set. Likewise, in the test set, we found that 61.24 % of user traffic consumption can also be explained by LSTM model and 95.20 % can be done by RBGU-RNN. Also, the RBGU-RNN has more efficient gradient descent than the standard LSTM by analysing and experimenting the graphs given by the Mean Squared Error (MSE), the Mean Absolute Percentage Error (MAPE) and the Maximum Absolute Error (MAXAE) functions over the numbe
Satellite-based image analysis becomes a study subject in a variety of applications such as geological survey, agricultural applications for soil analysis, environmental impact analysis, natural disaster to find out t...
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ISBN:
(纸本)9781728140421
Satellite-based image analysis becomes a study subject in a variety of applications such as geological survey, agricultural applications for soil analysis, environmental impact analysis, natural disaster to find out the change, urban planning to find appropriate locations for further urban development, etc. In this work image classification plays most important role in these applications. In this work HDR satellite image dataset used for pre-processing purpose. Normally the sky's dynamic range is as large as 10(6):1, while the LDR (Low Dynamic Range) display has a dynamic range of 10(3):1. Therefore, HDR satellite image needs to compress this dynamic range to get LDR image. TMO (Tone Mapping Operator) is used for excellent visualization purposes to solve this issue and has served excellent image quality. Satellite captured images are very large in size, with ordinary display facing difficulties in viewing and understanding the information available in an image. Therefore, the whole image needs to be divided into tiny components with big data, so it helps to comprehend and visualize. To overcome this problem segmentation method is to be used. There are various image segmentation methods like Edge based, Clustering based, watershed based, etc. In this work compared clustering based image segmentation like K-means clustering, Mean shift clustering and SLIC (Simple Linear Iterative Clustering) by extracting various features like processing time, Mean, Entropy, variance, etc,. In this work HDR satellite image classified using Artificial Neural Network (ANN) with feed forward algorithm based on supervised learning which delivered 99.53% accuracy.
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