Hybrid jellyfish search sine cosine optimisation-based deep learning for big data classification using MapReduce framework on epileptic seizure data
Year: 2024
Type: article
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.
Authors: Jamunadevi Chandrasekar, Arul Ponnusamy
Institution: Bharathidasan University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4405734076