Efficient data retrieval model based on semantic similarity analysis using chiroptera buzzard optimisation tuned deep CNN

To extract meaningful insights, integrating the data classification and semantic text summarisation is essential, aiding in the identification of contextually significant content. Most of the existing techniques encounter multiple challenges from the perspective of machine understanding, especially for languages with limited resources, and fail to learn the sequence of correlations effectively. Nevertheless, there is still much space for enhancing the speed of data retrieved because current approaches fail to take the spatial and semantic aspects into account. To tackle this issue, this research presents an efficient data retrieval model utilising chiroptera buzzard optimisation adapted deep convolutional neural network (CBO adapted deep CNN) for semantic similarity analysis. Specifically, the chiroptera buzzard optimisation is utilised for feature selection and fine-tuning the hyperparameters of DCNN that improves the classification accuracy. Hence, the proposed model reduces the computational complexity and provides remarkable performance in terms of metrics attaining 99.98% accuracy, 99.53% recall, 99.93% precision, 99.84% Fbeta, 99.52% Cohen kappa, and 99.52% F1-score for 90% of training.