
Cognitive Systems and Signal Processing in Image Processing
- 1st Edition - November 28, 2021
- Imprint: Academic Press
- 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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Request a sales quoteCognitive 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.
- Presents cognitive signal processing methodologies that are related to challenging image processing application domains
- Provides the state-of-the-art in cognitive signal processing approaches in the area of big-data image processing
- Focuses on other technical aspects and alternatives to traditional tools, algorithms and methodologies
- Discusses various real-time case studies and implemented works
Undergraduate and Graduate in electronic engineering and computer science; University Professors and research scholar; Researchers and engineers in automatics; Helps technical software developers who are potential data scientist aspirants; It will support Data analytics and AI managers who are leading a squad of analysts; Business analysts who work in the field of data science development projects
- 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
- Edition: 1
- Published: November 28, 2021
- No. of pages (Paperback): 398
- No. of pages (eBook): 398
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128244104
- eBook ISBN: 9780323860093
YZ
Yu-Dong Zhang
AS
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.