Abstract
We propose intelligent methods to classify two different HIV virus types, i.e., R5X4 and R5 or X4 with low computational complexity. Since R5X5 virus has same the features of R5 and X4 viruses, diagnosis of R5X4 can not be determined easily. In this study, the statistical data of R5X4, R5 and X4 was obtained by accessible residues and modelled by Auto-regressive (AR) model. After that the pre-processed data was used for determining the optimal σ value in Radial Basis Kernel of Support Vector Machine (SVM).
Keywords
Support vector machine
Kernel (algebra)
Basis (linear algebra)
Pattern recognition (psychology)
Computer science
Artificial intelligence
Sigma
Human immunodeficiency virus (HIV)
Value (mathematics)
Machine learning
Mathematics
Virology
Physics
Combinatorics
Biology
Subject Areas
HIV Research and Treatment
·Virology ·Life Sciences
Hepatitis C virus research
·Hepatology ·Health Sciences
Influenza Virus Research Studies
·Epidemiology ·Health Sciences
OpenAlex SDG Match
SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).
Good health and well-being 79%
Loading…