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

Authors: D. Kavitha; A. Chinnasamy; P. Selvakumari

Addresses: Department of Computer Science and Engineering, Sri Sairam Engineering College, Anna University, Chennai, India ' Department of Computer Science and Engineering, Sri Sairam Engineering College, Anna University, Chennai, India ' Department of Computer Science and Engineering, Chennai Institute of Technology, Anna University, Chennai, India

Abstract: 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.

Keywords: decision making; big data; enhanced honey badger algorithm; adaptive cascaded long short-term memory; auto-encoder; MapReduce framework.

DOI: 10.1504/IJDMB.2025.143040

International Journal of Data Mining and Bioinformatics, 2025 Vol.29 No.1/2, pp.193 - 222

Received: 24 Jan 2023
Accepted: 01 May 2024
Published online: 02 Dec 2024 *

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