Big data classification using mapreduce framework enabled deep quantum dilated convolutional neural network

In general, big data deals with the examination of enormous amount of data from distributed regions utilising ML modules. A big data classification technique using a hybrid deep learning-based optimisation algorithm in the MapReduce framework is proposed. Initially, data partitioning is conducted using deep fuzzy clustering (DFC). In the mapper phase, data normalisation is performed using linear normalisation. Then, feature fusion is done by Neyman measure with deep residual network (DRN). Later, data augmentation is performed using the oversampling model synthetic minority oversampling technique (SMOTE). Finally, in the reducer phase, big data classification is done utilising deep quantum dilated convolutional neural network (DQDCNN) model merged by quantum dilated convolutional neural network (QDCNN) and deep quantum neural network (DQNN). Here, DQDCNN is trained by Gazelle Hunter optimisation (GHO) developed by Gazelle optimisation algorithm (GOA) and Hunter Prey optimisation (HPO). The measures utilised for GHO-DQDCNN obtained supreme values of 92.4%, 92.9% and 92.6%.