Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment-Reference-Cited by-同舟云学术

Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment

Published:2023 Issue: Volume:11 Page:100052-100069
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Nijaguna G. S.¹,Lal N. Dayananda²,Divakarachari Parameshachari Bidare³^ORCID,Prado Rocío Pérez de⁴^ORCID,Woźniak Marcin⁵^ORCID,Patra Raj Kumar⁶

Affiliation:

1. Department of Artificial Intelligence and Machine Learning, S.E.A. College of Engineering and Technology, Bengaluru, India

2. Department of CSE, GITAM School of Technology, GITAM (Deemed to be University), Bengaluru, India

3. Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India

4. Telecommunication Engineering Department, University of Jaén, Linares, Jaén, Spain

5. Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland

6. Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, India

Funder

Rector of the Silesian University of Technology under Proquality

Spanish Research Projects

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10242063.pdf?arnumber=10242063

Reference43 articles.

1. An IoMT enabled deep learning framework for automatic detection of fetal QRS: A solution to remote prenatal care;krupa;J King Saud Univ -Comput Inf Sci,2022

2. Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

3. Meta Structural Learning Algorithm With Interpretable Convolutional Neural Networks for Arrhythmia Detection of Multisession ECG

4. Evaluation of surface EMG-based recognition algorithms for decoding hand movements

5. Robust Deep Identification using ECG and Multimodal Biometrics for Industrial Internet of Things

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