Machine Translation Using Deep Learning: A Comparison

Abstract

High dimensionality would be one of the major challenges faced by people working in research with big data as a high dimensionality that happens, while a dataset comprises of a big number of features. For resolving this issue, often researchers make use of a feature selection step for identification and removal of irrelevant features and repetitive features. Acceleration Artificial Bee Colony-Artificial Neural Network (AABC-ANN) has been introduced in the preceding research for handling the feature selection process over the big data. Computational complexity and inaccuracy of dataset remain as a problem for these methods. Enhanced Particle Swarm Optimization with Genetic Algorithm – Modified Artificial Neural Network (EPSOGA–MANN) is described in the proposed methodology for avoiding the above-mentioned issues. Modules including preprocessing, feature selection, and classification have been included in this research process. Fuzzy C Means (FCM) denotes the clustering algorithm which is used to handle the noise information efficiently in preprocessing. Feature selection process is carried out by means of EPSOGA algorithm optimally in this research. More important and relevant features are selected by EPSOGA optimization algorithm and as a result more accurate classification results are achieved in this work for huge volume of dataset. Input, hidden, and output layers are the three layers of MANN. It is introduced for improving the time complexity by means of neurons. The performance evaluation of the research method is conducted in the Matlab simulation environment.