Cognitive Systems and Signal Processing in Image Processing

1st Edition - November 28, 2021
Editors: Yu-Dong Zhang, Arun Kumar Sangaiah
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 4 4 1 0 - 4
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 6 0 0 9 - 3

Cognitive Systems and Signal Processing in Image Processing presents different frameworks and applications of cognitive signal processing methods in image processing. This book… Read more

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Cognitive Systems and Signal Processing in Image Processing presents different frameworks and applications of cognitive signal processing methods in image processing. This book provides an overview of recent applications in image processing by cognitive signal processing methods in the context of Big Data and Cognitive AI. It presents the amalgamation of cognitive systems and signal processing in the context of image processing approaches in solving various real-word application domains. This book reports the latest progress in cognitive big data and sustainable computing.

Various real-time case studies and implemented works are discussed for better understanding and more clarity to readers. The combined model of cognitive data intelligence with learning methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues for computer vision in real-time.

Cover image
Title page
Table of Contents
Copyright
Contributors
Chapter 1: A cognitive approach to digital health based on deep learning focused on classification and recognition of white blood cells
Abstract
1: Introduction
2: Literature review
3: Cognitive systems in medical image processing
4: Neural networks concepts
5: Metaheuristic algorithm proposal (experiment)
6: Results and discussion
7: Conclusions
8: Future research directions
Chapter 2: Assessment of land use land cover change detection in multitemporal satellite images using machine learning algorithms
Abstract
1: Introduction
2: Related works
3: Proposed work
4: Methodology
5: Results and discussions
6: Accuracy assessment
7: Conclusion
Chapter 3: A web application for crowd counting by building parallel and direct connection-based CNN architectures
Abstract
Acknowledgment
1: Introduction
2: Background
3: CNN algorithmic model
4: Experimental results
5: Future research directions
6: Conclusion
Appendices
Chapter 4: A cognitive system for lip identification using convolution neural networks
Abstract
1: Introduction
2: Survey of related work
3: Feature extraction and classification using CNN
4: Results
5: Conclusion and future work
Chapter 5: An overview of the impact of PACS as health informatics and technology e-health in healthcare management
Abstract
1: Introduction
2: Review literature on cognitive systems concepts
3: Review literature on implementation of PACS systems
4: PACS systems application
5: PACS environments and systems management
6: Discussion
7: Future trends
8: Conclusions
Chapter 6: Change detection techniques for a remote sensing application: An overview
Abstract
1: Introduction
2: Remote sensing data
3: Data preprocessing
4: Change detection technique
5: Conclusion
Chapter 7: Facial emotion recognition via stationary wavelet entropy and particle swarm optimization
Abstract
1: Introduction
2: Dataset
3: Methodology
4: Experiment results and discussions
5: Conclusions
Chapter 8: A research insight toward the significance in extraction of retinal blood vessels from fundus images and its various implementations
Abstract
1: Introduction
2: Literature review
3: Extraction of retinal blood vessels using supervised technique
4: Extraction of retinal blood vessels using unsupervised technique
5: Result
6: Conclusion
7: Future scope
Chapter 9: Hearing loss classification via stationary wavelet entropy and cat swarm optimization
Abstract
1: Introduction
2: Dataset
3: Methodology
4: Experiment results and discussions
5: Conclusions
Chapter 10: Early detection of breast cancer using efficient image processing algorithms and prediagnostic techniques: A detailed approach
Abstract
1: Introduction
2: Literature review
3: Breast cancer: A brief introduction
4: Cognitive approaches in breast cancer techniques
5: Proposed methodology
6: Algorithms used
7: Results and discussion
8: Conclusion
Chapter 11: Groundnut leaves and their disease, deficiency, and toxicity classification using a machine learning approach
Abstract
Acknowledgment
1: Introduction
2: Literature review
3: Methodology
4: Results and discussion
5: Conclusion
Chapter 12: EEG-based computer-aided diagnosis of autism spectrum disorder
Abstract
1: Introduction
2: Related work
3: Proposed work
4: Performance analysis
5: Conclusion
Chapter 13: Toward improving the accuracy in the diagnosis of schizophrenia using functional magnetic resonance imaging (fMRI)
Abstract
1: Introduction
2: Literature review
3: Methodology
4: Results and discussion
5: Conclusion
Chapter 14: An artificial intelligence mediated integrated wearable device for diagnosis of cardio through remote monitoring
Abstract
1: Introduction
2: Related work
3: Proposed work
4: Feature extraction
5: Performance analysis
6: Conclusion
Chapter 15: Deep learning for accident avoidance in a hostile driving environment
Abstract
1: Introduction
2: Literature review
3: Research challenges and motivation
4: Semantic segmentation
5: Segmentation using deep learning architecture
6: Detection
7: Object recognition
8: Image processing dataset
9: Natural language processing dataset
10: Audio/speech processing dataset
11: Deep learning architectures
12: Results and discussion
13: Semantic segmentation using deep learning
14: Vehicle detection using deep learning
15: Vehicle recognition using deep learning
16: Conclusion and future work
Chapter 16: Risk analysis of coronavirus patients who have underlying chronic cancer
Abstract
1: Introduction
2: Related work
3: About COVID-19 with chronic diseases
4: Experimental analysis
5: Discussion
6: Conclusion
Index

Yu-Dong Zhang

Yu-Dong Zhang received his Ph.D. from Southeast University. He worked as postdoc from 2010 to 2012 in Columbia University, USA, and as an assistant research scientist from 2012 to 2013 at the Research Foundation of Mental Hygiene, USA. He served as a full professor from 2013 to 2017 in Nanjing Normal University, where he was the founding director of Advanced Medical Image Processing Group in NJNU. He currently works as a professor in the Department of Informatics, University of Leicester, UK. His research interests include deep learning, convolutional neural networks, graph convolutional networks, attention networks, explainable AI, medical image analysis, bio-inspired computing, pattern recognition, transfer learning and medical sensors.

Affiliations and expertise

Professor, Department of Informatics, University of Leicester, Leicester, UK

Arun Kumar Sangaiah

Prof. Arun Kumar Sangaiah received his PhD from the School of Computer Science and Engineering, VIT University, Vellore, India. He is currently a Full Professor with National Yunlin University of Science and Technology, Taiwan. He is also a Professor at the School of Computing Science and Engineering, VIT University, Vellore, India. His areas of research interest include machine learning, Internet of Things, Sustainable Computing. He has published more than 300 research articles in refereed journals, 11 edited books, one patent (held and filed), as well as four projects funded by MOST-TAIWAN, one funded by Ministry of IT of India, and several international projects (CAS, Guangdong Research fund, Australian Research Council). Dr. Sangaiah has received many awards, Yushan Young Scholar, Clarivate Top 1% Highly Cited Researcher (2021,2022, 2023), Top 2% Scientist (Standord Report-2020,2021,2022, 2023), PIFI-CAS fellowship, Top-10 outstanding researcher, CSI significant Contributor etc. He is also serving as Editor-in-Chief and/or Associate Editor of various reputed ISI journals. Dr. Sangaiah is a visiting scientist (2018-2019) with Chinese Academy of Sciences (CAS), China and visiting researcher of Université Paris-Est (UPEC), France (2019-2020) and etc.

Affiliations and expertise

National Yunlin University of Science and Technology, Taiwan

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health