GENETIC ALGORITHM - OPTIMIZED GATED RECURRENT UNIT (GRU) NETWORK FOR SEMANTIC WEB SERVICES CLASSIFICATION

Abstract

In the current era, as there is an abundant expansion of functionally similar web services, it becomes a prodigious issue for the web service discovery process. The service classification plays a significant role to greatly reduce the search space and retrieves the desirable service quickly and accurately. The classification is performed using the functional values. Recent research activities recommend RNN (Recurrent Neural Network) deep learning algorithms for efficient classification process. The state-of-the-art of GRU (Gated Recurrent Unit) one of the RNN model, provides a proficient classification process. However, the ratio of training and testing dataset, and hyperparameters namely neural network size, and batch size etc, affects the classification accuracy. The objective of the paper is to incorporate GRU model for efficient classification process. The novelty of the proposed model lies in implementing the GRU model for semantic web service classification. Furthermore, the genetic algorithm is used to predict the optimal ratio of training and testing dataset and optimal hidden neural Network units of GRU model in order to attain optimal classification. The experimental results exemplifies that the semantic web service classification is efficiently deliberated using the proposed GA-GRU model that outperforms the classification process as compared with the conventional semantic extraction using accuracy, precision, F-measure, recall and FDR (False Date Rate) rate.