Machine Vision Inspection Systems, Machine Learning-Based Approaches

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Scrivener Publishing

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Publishers at Scrivener

Martin Scrivener ( martin@scrivenerpublishing.com)

Phillip Carmical ( pcarmical@scrivenerpublishing.com)

Edited by

Muthukumaran Malarvel

Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Soumya Ranjan Nayak

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

Prasant Kumar Pattnaik

School of Computer Engineering, KIIT Deemed to be University, India

Surya Narayan Panda

Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2021 Scrivener Publishing LLC

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-78609-2

Cover image: Pixabay.Com

Cover design by: Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

The edited book aims to bring together leading researchers, academic scientists, and research scholars to put forward and share their experiences and research results on all aspects of an inspection system for detection analysis for various machine vision applications. It also provides a premier interdisciplinary platform for educators, practitioners and researchers to present and discuss the most recent innovations, trends, methodology, applications, and concerns as well as practical challenges encountered and solutions adopted in the inspection system in terms of machine learning-based approaches of machine vision for real and industrial application. The book is organized into fourteen chapters.

Chapter 1deliberated about various dangerous infectious viruses affect human society with a detailed analysis of transmission electron microscopy virus images (TEMVIs). In this chapter, several TEMVIs such as Ebola virus (EV), Enterovirus (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.