1 Citation (Scopus)

Abstract

In the bio-molecular field, epitope classification is essential in vaccine development. A machine learning-based approach has been used for epitope classification using peptide data from b-cell, Severe Acute Respiratory Syndrome (SARS) to predict material for SARS-CoV-2 vaccine. This study aims to improve the performance of epitope classification by weighting peptide features and including input weight and biases of Classifier model using the Time Variant Inertia Weight, Acceleration Coefficients and Random Injection-Particle Swarm optimization (TVIWACRI-PSO) and the Extreme Learning Machine (ELM) classification algorithm. Experiment on b-cell, SARS dataset shows that feature weighting (FW) using TVIWACRI-PSO can improve accuracy performance by 8.4%.

Original languageEnglish
Title of host publication2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages153-158
Number of pages6
ISBN (Electronic)9798350312164
DOIs
Publication statusPublished - 2023
Event14th International Conference on Information and Communication Technology and System, ICTS 2023 - Surabaya, Indonesia
Duration: 4 Oct 20235 Oct 2023

Publication series

Name2023 14th International Conference on Information and Communication Technology and System, ICTS 2023

Conference

Conference14th International Conference on Information and Communication Technology and System, ICTS 2023
Country/TerritoryIndonesia
CitySurabaya
Period4/10/235/10/23

Keywords

  • epitope b-cell and sars
  • feature weight
  • sars-cov-2 vaccine
  • tviwacri-pso-elm

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