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Intelligent Fractal-Based Image Analysis
Applications in Pattern Recognition and Machine Vision
1st Edition - March 1, 2024
Editors: Soumya Ranjan Nayak, Janmenjoy Nayak, Khan Muhammad, Yeliz Karaca
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 4 6 8 - 0
Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different… Read more
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Intelligent Fractal-Based Image Analysis: Application in Pattern Recognition and Machine Vision provides insights into the current strengths and weaknesses of different applications as well as research findings on fractal graphics in engineering and science applications. The book aims to improve the exchange of ideas and coherence between various core computing methods and highlights the relevance of related application areas for advanced as well as novice-user application. The book presents core concepts, methodological aspects, and advanced feature opportunities, focusing on major, real-time applications in engineering and health science. It will appeal to researchers, data scientists, industry professionals, and graduate students.Fractals are infinite, complex patterns used in modeling physical and dynamic systems. Fractal theory research has increased across different fields of applications including engineering science, health science, and social science. Recent literature shows the vital role fractals play in digital image analysis, specifically in biomedical image processing. Fractal graphics is an interdisciplinary field that deals with how computers can be used to gain high-level understanding from digital images. Integrating artificial intelligence with fractal characteristics has resulted in new interdisciplinary research in the fields of pattern recognition and image processing analysis.
- Investigates advanced fractal theories spanning neural networks, fuzzy logic, machine learning, deep learning, and hybrid intelligent systems in solving pattern recognition problems
- Explores the application of fractal theories to a wide range of medical image processing modalities
- Presents case studies that illustrate the application and integration of fractal theories into intelligent computing in the resolution of important pattern recognition and machine vision problems
Researcher data scientists, industry professionals, and graduate students in the field of fractal graphics and its related applications
Part 1: Intelligent Fractal-Based Image Analysis Introduction to Intelligent Fractal-Based Image Analysis – Editors 1.1 Insights into Intelligent Fractal-Based Image Analysis with Pattern Recognition 1.2 Analysis of Mandelbrot Set Fractal Images Using Machine Learning Based Approach 1.3 Chaos-based Image Encryption 1.4 Fractal Feature-based Image Classification Part 2: Recognition Model Using Fractal Features 2.1 The study of Source Image and its Futuristic Quantum Applications: An insight from Fractal Analysis 2.2 Wavelet Multifractal Characterization of Anisotropic Oscillating Singularities and Application in Nanomaterials 2.3 GID-Net: Generic Image Denoising using Convolutional Auto-encoders 2.4 Geometrical Description of Image Analysis Using Fractal Theory Part 3: Fractals in Disease Identification and Control 3.1 Fractal Theory and the Explainable Artificial Intelligence of Cancer Medical Imaging 3.2 Computational Complexity of Multifractal Models-based MRI Image Processing for Subgroups of Multiple Sclerosis Patients’ Diagnosis and Course in Precision Medicine 3.3 AI-Stochastic Fractal Analysis of the Alzheimer disease (AD) Medical Images 3.4 Preliminary Study of Retinal Lesions Classification on Rational Fundus Images for the Diagnosis of Retinal Diseases
- No. of pages: 350
- Language: English
- Published: March 1, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443184680
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Soumya Ranjan Nayak
Soumya Ranjan Nayak is an assistant professor at the Amity School of Engineering and Technology, Amity University, Noida, India. He received his PhD degree in computer science and engineering under the MHRD Govt. of India. He has published over 80 articles in peer-reviewed journals and conferences of international repute like Elsevier, Springer, World Scientific, IOS Press, Taylor & Francis, Inderscience, and IGI Global. His current research interests include medical image analysis and classification, machine learning, deep learning, pattern recognition, fractal graphics, and computer vision. He serves as a reviewer for many peer-reviewed journals such as IEEE Journal of Biomedical and Health Informatics, IEEE Access, Applied Mathematics and Computation, Journal of Applied Remote Sensing, Mathematical Problems in Engineering, International Journal of Light and Electron optics, Journal of Intelligent and Fuzzy Systems, Future Generation Computer Systems, and Pattern Recognition Letters.
Affiliations and expertise
Assistant Professor, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, IndiaJN
Janmenjoy Nayak
Janmenjoy Nayak is an Assistant Professor, P.G. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University, Baripada, Odisha, India. He has been a Gold Medallist in Computer Science twice in his career, and has been awarded the “Innovation in Science Pursuit for Inspired Research” (INSPIRE) Research Fellowship from the Department of Science & Technology, Government of India (at both Junior Research Fellow and Senior Research Fellow level) and Best Researcher Award from Jawaharlal Nehru University of Technology, Kakinada, Andhra Pradesh for the academic year 2018–19. He has received many other awards from national and international academic agencies. Dr. Nayak has edited 19 books and 8 special issues on the applications of computational intelligence, soft computing, and pattern recognition, which have been published by Springer and Inderscience. He has published more than 190 refereed articles in various book chapters, conferences, and peer-reviewed journals of Elsevier, Inderscience, Springer, the Institute of Electrical and Electronics Engineers (IEEE), and others. He has also served as a reviewer for more than 100 journals and conferences produced by the IEEE, the Association for Computing Machinery (ACM), Springer, Elsevier, Wiley, and Inderscience. He has 11 years of experience in both teaching and research. Dr. Nayak is a senior member of the IEEE and a life member of societies such as the Soft Computing Research Society (SCRS), the Computer Society of India (CSI India), the Orissa Information Technology Society (OITS), the Orissa Mathematical Society (OMS), and the International Association of Engineers (IAENG), Hong Kong. He has successfully conducted and is associated with 14 internationally renowned series conferences such as ICCIDM, HIS, ARIAM, CIPR, and SCDA. His areas of interest include data mining, nature-inspired algorithms, and soft computing.
Affiliations and expertise
Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCBD) University, Mayurbhanj, IndiaKM
Khan Muhammad
Khan Muhammad received his PhD in Digital Contents from Sejong University, South Korea in February 2019. He was an Assistant Professor in the Department of Software, Sejong University from March 2019 to February 2022. He is currently the director of Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) and an Assistant Professor (Tenure-Track) in the Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, South Korea. His research interests include intelligent video surveillance, medical image analysis, information security, video summarization, multimedia data analysis, computer vision, and smart cities. He has registered 10 patents and contributed more than 220 papers in peer-reviewed journals and conference proceedings in his research areas. He is an Associate Editor/Editorial Board Member for more than 15 journals. He was among the most highly cited researchers in 2021 and 2022, according to the Web of Science (Clarivate).
Affiliations and expertise
Assistant Professor, Department of Interaction Science and Director of Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Sungkyunkwan University, Seoul, South KoreaYK
Yeliz Karaca
Yeliz Karaca is an assistant professor of applied mathematics, and a researcher at the University of Massachusetts Medical School, USA. She received her Ph.D. degree in Mathematics from Marmara University, Istanbul, Turkey in 2012. Among the other awards, she was also granted the “Cooperation in Neurological Sciences and Support Award” by Turkish Neurology Association as the first mathematician in Turkey. Her research interests focus on computational methods, complex systems, computational complexity, nonlinear dynamics, fractals, multifractional methods with their applications, wavelets and entropy, advanced AI applications, solutions of advanced mathematical challenges, mathematical neuroscience and biology as well as advanced data analysis in medicine and other related domains.
Affiliations and expertise
Assistant Professor of Applied Mathematics and Researcher, University of Massachusetts Medical School, Worcester, Massachusetts, USA