Читать книгу Machine Vision Inspection Systems, Machine Learning-Based Approaches - Группа авторов - Страница 12

Kalyan Kumar Jena1*, Sourav Kumar Bhoi1, Soumya Ranjan Nayak2 and Chittaranjan Mallick3

1Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur, India

2Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India

3Department of Mathematics, Parala Maharaja Engineering College, Berhampur, India

Abstract

Viruses are the submicroscopic infectious agents having the capability of replication itself inside the living cells of human body. Different dangerous infectious viruses greatly affect the human society along with plants, animals and microorganisms. It is very difficult for the survival of human society due to these viruses. In this chapter, Machine Learning (ML)-based approach is used to analyze several transmission electron microscopy virus images (TEMVIs). In this work, several TEMVIs such as Ebola virus (EV), Entero virus (ENV), Lassa virus (LV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Zika virus (ZV), etc. are analyzed. The ML-based approach mainly focuses on the classification techniques such as Logistic Regression (LR), Neural Network (NN), k-Nearest Neighbors (kNN) and Naive Bayes (NB) for the processing of TEMVIs. The performance of these techniques is analyzed using classification accuracy (CA) parameter. The simulation of this work is carried out using Orange3-3.24.1.

Keywords: ML, TEMVIs, Classification Techniques, LR, NN, kNN, NB