Hybrid Bat Harris Hawks Optimized Approach for Data Retrieval Using Deep Convolution Neural Networks

In an era where there is an abundance of data available, knowledge discovery is an essential endeavor. For this procedure to effectively extract relevant information from a variety of data types, text mining models play a major role. Categorization strategies are essential for assigning known class labels to test data documents and distinguishing between binary and multi-class models. In order to improve the structured format of unstructured data found in documents, machine learning techniques are utilized to develop classifiers based on text mining outcomes. In order to improve data retrieval and promote knowledge discovery from documents, the research presents a novel strategy, the Bat Harris Hawks Optimization algorithm, to strengthen deep convolutional neural networks for efficient data retrieval.