Machine vision inspection systems. Volume 2, Machine -learning-based approaches / edited by Muthukumaran Malarvel, Soumya Ranjan Nayak, Prasant Kumar Pattnaik and Surya Narayan Panda.

Contributor(s): Malarvel, Muthukumaran [editor.] | Nayak, Soumya Ranjan, 1984- [editor.] | Pattnaik, Prasant Kumar, 1969- [editor.] | Panda, Sury Narayan [editor.]
Language: English Publisher: Beverly, MA : Scrivener Publishing, 2021Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119786092; 9781119786122; 1119786126Other title: Machine -learning-based approachesSubject(s): Computer vision | Computer vision -- Industrial applications | Engineering inspection -- Automation | Image processing | Image processing -- Digital techniquesGenre/Form: Electronic books.DDC classification: 006.3/7 LOC classification: TA1634 | .M3354 2020ebOnline resources: Full text available at Wiley Online Library Click here to view
Contents:
Machine Learning-Based Virus Type Classification Using Transmission Electron Microscopy Virus Images / Kalyan Kumar Jena, Sourav Kumar Bhoi, Soumya Ranjan Nayak, Chittaranjan Mallick -- Capsule Networks for Character Recognition in Low Resource Languages / C Abeysinghe, I Perera, DA Meedeniya -- An Innovative Extended Method of Optical Pattern Recognition for Medical Images With Firm Accuracy-4f System-Based Medical Optical Pattern Recognition / Priya EL Dhivya, D Jeyabharathi, KS Lavanya, S Thenmozhi, R Udaiyakumar, A Sharmila -- Brain Tumor Diagnostic System-A Deep Learning Application / T Kalaiselvi, ST Padmapriya -- Machine Learning for Optical Character Recognition System / Gurwinder Kaur, Tanya Garg -- Surface Defect Detection Using SVM-Based Machine Vision System with Optimized Feature / Ashok Kumar Patel, Venkata Naresh Mandhala, Dinesh Kumar Anguraj, Soumya Ranjan Nayak -- Computational Linguistics-Based Tamil Character Recognition System for Text to Speech Conversion / S Suriya, M Balaji, TM Gowtham, Kumar S Rahul -- A Comparative Study of Different Classifiers to Propose a GONN for Breast Cancer Detection / Ankita Tiwari, Bhawana Sahu, Jagalingam Pushaparaj, Muthukumaran Malarvel -- Mexican Sign-Language Static-Alphabet Recognition Using 3D Affine Invariants / Guadalupe Carmona-Arroyo, Homero V Rios-Figueroa, Martha Lorena Avendao-Garrido -- Performance of Stepped Bar Plate-Coated Nanolayer of a Box Solar Cooker Control Based on Adaptive Tree Traversal Energy and OSELM / S Shanmugan, FA Essa, J Nagaraj, Shilpa Itnal -- Applications to Radiography and Thermography for Inspection / Inderjeet Singh Sandhu, Chanchal Kaushik, Mansi Chitkara -- Prediction and Classification of Breast Cancer Using Discriminative Learning Models and Techniques / M Pavithra, R Rajmohan, T Ananth Kumar, R Ramya -- Compressed Medical Image Retrieval Using Data Mining and Optimized Recurrent Neural Network Techniques / Vamsidhar Enireddy, C Karthikeyan, Kumar T Rajesh, Ashok Bekkanti -- A Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay / Madhusudana Rao Nalluri, K Kannan, Diptendu Sinha Roy.
Summary: Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes image processing, machine vision and, pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Now a day's the current research on machine inspection gained more popularity among various researchers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examining while inspection process. This volume 2 covers machine learning-based approaches in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), natural language processing, medical diagnosis, etc. This edited book is designed to address various aspects of recent methodologies, concepts, and research plan out to the readers for giving more depth insights for perusing research on machine vision using machine learning-based approaches.
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Includes bibliographical references and index.

Machine Learning-Based Virus Type Classification Using Transmission Electron Microscopy Virus Images / Kalyan Kumar Jena, Sourav Kumar Bhoi, Soumya Ranjan Nayak, Chittaranjan Mallick -- Capsule Networks for Character Recognition in Low Resource Languages / C Abeysinghe, I Perera, DA Meedeniya -- An Innovative Extended Method of Optical Pattern Recognition for Medical Images With Firm Accuracy-4f System-Based Medical Optical Pattern Recognition / Priya EL Dhivya, D Jeyabharathi, KS Lavanya, S Thenmozhi, R Udaiyakumar, A Sharmila -- Brain Tumor Diagnostic System-A Deep Learning Application / T Kalaiselvi, ST Padmapriya -- Machine Learning for Optical Character Recognition System / Gurwinder Kaur, Tanya Garg -- Surface Defect Detection Using SVM-Based Machine Vision System with Optimized Feature / Ashok Kumar Patel, Venkata Naresh Mandhala, Dinesh Kumar Anguraj, Soumya Ranjan Nayak -- Computational Linguistics-Based Tamil Character Recognition System for Text to Speech Conversion / S Suriya, M Balaji, TM Gowtham, Kumar S Rahul -- A Comparative Study of Different Classifiers to Propose a GONN for Breast Cancer Detection / Ankita Tiwari, Bhawana Sahu, Jagalingam Pushaparaj, Muthukumaran Malarvel -- Mexican Sign-Language Static-Alphabet Recognition Using 3D Affine Invariants / Guadalupe Carmona-Arroyo, Homero V Rios-Figueroa, Martha Lorena Avendao-Garrido -- Performance of Stepped Bar Plate-Coated Nanolayer of a Box Solar Cooker Control Based on Adaptive Tree Traversal Energy and OSELM / S Shanmugan, FA Essa, J Nagaraj, Shilpa Itnal -- Applications to Radiography and Thermography for Inspection / Inderjeet Singh Sandhu, Chanchal Kaushik, Mansi Chitkara -- Prediction and Classification of Breast Cancer Using Discriminative Learning Models and Techniques / M Pavithra, R Rajmohan, T Ananth Kumar, R Ramya -- Compressed Medical Image Retrieval Using Data Mining and Optimized Recurrent Neural Network Techniques / Vamsidhar Enireddy, C Karthikeyan, Kumar T Rajesh, Ashok Bekkanti -- A Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay / Madhusudana Rao Nalluri, K Kannan, Diptendu Sinha Roy.

Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes image processing, machine vision and, pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Now a day's the current research on machine inspection gained more popularity among various researchers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examining while inspection process.

This volume 2 covers machine learning-based approaches in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), natural language processing, medical diagnosis, etc. This edited book is designed to address various aspects of recent methodologies, concepts, and research plan out to the readers for giving more depth insights for perusing research on machine vision using machine learning-based approaches.

About the Author
Muthukumaran Malarvel obtained his PhD in digital image processing and he is currently working as an associate professor in the Department of Computer Science and Engineering at Chitkara University, Punjab, India. His research interests include digital image processing, machine vision systems, image statistical analysis & feature extraction, and machine learning algorithms.

Soumya Ranjan Nayak obtained his PhD in computer science and engineering from the Biju Patnaik University of Technology, India. He has more than a decade of teaching and research experience and currently is working as an assistant professor, Amity University, Noida, India. His research interests include image analysis on fractal geometry, color and texture analysis jointly and separately.

Prasant Kumar Pattnaik PhD (Computer Science), Fellow IETE, Senior Member IEEE is a Professor at the School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. He has more than a decade of teaching and research experience. His areas of interest include mobile computing, cloud computing, cyber security, intelligent systems and brain computer interface.

Surya Narayan Panda is a Professor and Director Research at Chitkara University, Punjab, India. His areas of interest include cybersecurity, networking, advanced computer networks, machine learning, and artificial intelligence. He has developed the prototype of Smart Portable Intensive Care Unit through which the doctor can provide immediate virtual medical assistance to emergency cases in the ambulance. He is currently involved in designing different healthcare devices for real-time issues using AI and ML.

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