Title: Hybrid jellyfish search sine cosine optimisation-based deep learning for big data classification using MapReduce framework on epileptic seizure data

Authors: Jamunadevi Chandrasekar; Arul Ponnusamy

Addresses: Department of Computer Science, Government Arts College, Tiruchirappalli-620 022, India; Affiliated to: Bharathidasan University, Trichy-24, India ' Department of Computer Science, Government Arts College, Tiruchirappalli-620 022, India; Affiliated to: Bharathidasan University, Trichy-24, India

Abstract: The increase in the amount of big data with the technical advances makes the traditional software tools face difficulties and unable to handle them. In the medical field, big data technologies require new frameworks to leverage them. This paper proposes a novel big data classification using a MapReduce framework on epileptic seizure data is proposed. Here, the big data classification is accomplished in a MapReduce framework, wherein the mapper phase is applied with the data partitioned by deep embedded clustering (DEC). The classification is carried out in the reducer phase, where a deep long short-term memory (DLSTM) trained using the jellyfish search sine cosine (JSCS) algorithm is used for epileptic seizure detection based on the salient features determined from the EEG data. The JSCS-DLSTM is investigated for its efficiency based on accuracy, specificity, and sensitivity and is found to record superior values of 0.915, 0.927, and 0.919, respectively.

Keywords: deep embedded clustering; DEC; jellyfish search sine cosine algorithm; JSCS; deep long short-term memory; big data classification; epileptic seizure detection.

DOI: 10.1504/IJIIDS.2025.143484

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.1, pp.1 - 31

Received: 19 Jul 2023
Accepted: 13 Mar 2024
Published online: 23 Dec 2024 *