Automatic Selection of Open Source Multimedia Softwares Using Error Back-Propagation Neural Network

Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning

(Top left) Ash-RGB created using MODIS infrared bands 32–31, 31–29, and 31 for the eruption of Chaitén, 6 May 2008, 1505Z; (bottom left) false color RGB image created using

In this study, an artificial neural network method using the back-propagation algorithm was proposed for the prediction of ferric iron precipitation in uranium bioleaching

In this work, we proposed a classification of MR image using the genetic optimized neural network, imposed with sequential forward selection and k-NN

This Proposed methodology provides a Comparative Analysis of three Algorithms such as Deep Learning Algorithm, Naive Bayes and Back Propagation Neural Network

Every group developing software and the results of work to the public with open source license makes the open source project (Evers, 2000).. The open source project is difficult

This paper shows that GGAP-RBF has huge time complexity as compared to MLP and ANFIS. Moreover, GGAP-RBF does not out-perform MLP and ANFIS in Recall Rate. The paper also shows

In this research, the accuracy of decision trees (DT) (CART algorithm), artificial neural networks (Levenberg-Marquardt back propagation algorithm) and modified