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.
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Table of Contents
1. Approach cognitive for health digital based on deep learning focused on classification and recognition of white blood cells Ana Carolina Borges Monteiro, Reinaldo Padilha Franca, Rangel Arthur, and Yuzo Iano
2. Assessment of land use land cover change detection in multitemporal satellite images using machine learning algorithm Mahalakshmi Murugan, S Rohini, and N Sureshkumar
3. Web application for crowd counting by building parallel and direct connection method CNN architectures Zhilin Hu
4. A cognitive system for lip identification using convolution neural networks Vishesh Agarwal and Rahul Raman
5. An overview of the impact of PACS as health informatics and technology e-health in healthcare management Reinaldo Padilha Franca, Ana Carolina Borges Monteiro, Rangel Arthur, and Yuzo Iano
6. Change detection technique for remote sensing application: An overview Rohini Selvaraj and Sureshkumar Nagarajan
7. Facial emotion recognition via stationary wavelet entropy and particle swarm optimization Xiang Li and Junding Sun
8. A research insight toward the significance in extraction of retinal blood vessels from fundus images and its various implementations Nimisha Anns Oommen and P. Darsana
9. Hearing loss classification via stationary wavelet entropy and cat swarm optimization Chong Yao
10. Early detection of breast cancer using efficient image processing algorithms and prediagnostic techniques: A detailed approach G. Boopathi Raja
11. Plant leaf and its disease, deficiency, and toxicity classification using machine learning approach K.R. Anu Bama and S. SujaPriyadharsini
12. EEG-based computer-aided diagnosis of autism spectrum disorder A. Sivasangari, Kishore Sonti, Grace Prince Kanmani, Sindhu, and D. Deepa
13. Toward improving the accuracy in the diagnosis of schizophrenia using functional magnetic resonance imaging (fMRI) M. Kaviya Elakkiya and Dejey
14. Artificial intelligence mediated integrated wearable device for diagnosis of cardio through remote monitoring A. Sivasangari, R. Subhashini, S. Poonguzhali, Immanuel Rajkumar, J.S. Vimali, and D. Deepa
15. Deep learning for accident avoidance in a hostile driving environment S. Selva Nidhyananthan, R. Newlin Shebiah, B. Vijaya Kumari, and K. Gopalakrishnan
16. Risk analysis of coronavirus patients who have underlying chronic disease cancer V. Kakulapati
Authors
Yu-Dong Zhang Professor, Department of Informatics, University of Leicester, Leicester, UK. 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. Arun Kumar Sangaiah National Yunlin University of Science and Technology, Taiwan.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 Universit� Paris-Est (UPEC), France (2019-2020) and etc.
