Intelligent decision-making framework for big data using enhanced honey badger-based adaptive hybrid deep learning network

By utilising the conventional models, it is also consuming more time for processing. Hence, there is a crucial requirement for real-world application over big data procedures to perform a scalable and effective solution. For the experimentation, input data is gathered from different application-oriented datasets. Initially, the input data is congregated and undergoes for data cleaning stage and then the cleaned data is given as input for optimal feature extraction, in which the enhanced map-reduce model is applied for extracting the optimal features. These obtained optimal features are fed into adaptive cascaded long short-term memory and auto-encoder-based long short-term memory (ACLALSTM), in which the parameters are optimised by using enhanced HBA for effective decision-making in proposed big data analysis. The experimental analysis shows, that the proposed big data-based decision-making model shows the tendency to provide rapid decisions that help to analyse the big data effectively.