Intelligent Fractal-Based Image Analysis

Applications in Pattern Recognition and Machine Vision
Erscheinungsjahr 2024
ISBN: 978-0-443-18468-0
Verlag: Elsevier Science Publishing Co Inc

Buch, Englisch, 318 Seiten, Format (B × H): 230 mm x 151 mm, Gewicht: 510 g

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.

Autoren/Hrsg.

Nayak, Janmenjoy University

Nayak, Soumya Ranjan

Muhammad, Khan

Karaca, Yeliz

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Mathematik | Informatik Mathematik Mathematik Allgemein Diskrete Mathematik, Kombinatorik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

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 Encryption1.4 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

Über Autor(innen)

Nayak, Soumya Ranjan
Dr. Soumya Ranjan Nayak now holds the position of Assistant Professor in the School of Computer Engineering at Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, located in Odisha, India. He obtained a Doctor of Philosophy (Ph.D) and Master of Technology (M.Tech) in Computer Science and Engineering under a scholarship provided by the Ministry of Human Resource Development (MHRD) of the Government of India. These degrees were earned at CET, BPUT Rourkela, India. Prior to this, he completed a Bachelor of Technology (B. Tech) and a Diploma in Computer Science and Engineering. He has authored over 150 articles that have been published in reputable international journals and conferences such as Elsevier, Springer, World Scientific, IOS Press, Taylor & Francis, Hindawi, Inderscience, IGI Global, and others. These publications have undergone a rigorous peer-review process. In addition to the aforementioned accomplishments, the individual has authored 16 book chapters, published 6 books, and obtained 7 Indian patents (with 4 patents being granted). Furthermore, they have secured 4 International patents, all of which have been granted. The researcher's current areas of focus encompass medical picture analysis and classification, machine learning, deep learning, pattern recognition, fractal graphics, and computer vision. The author's writings have garnered over 1500 citations, with an h-index of 24 and an i10-index of 63, as reported by Google Scholar. Dr. Nayak holds the position of an associate editor for several esteemed academic journals, including the Journal of Electronic Imaging (SPIE), Mathematical Problems in Engineering (Hindawi), Journal of Biomedical Imaging (Hindawi), Applied Computational Intelligence and Soft Computing (Hindawi), and PLOS One. He is currently fulfilling the role of a guest editor for special issues of renowned academic journals such as Springer Nature, Elsevier, and Taylor & Franchise. He has been affiliated as a reviewer for numerous esteemed peer-reviewed journals, including 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, Pattern Recognition Letters, and others. He has additionally held the Technical Program Committee Member position for several conferences of significant worldwide recognition.

Karaca, Yeliz
Yeliz Karaca is an Assistant Professor of Applied Mathematics, and a researcher at the University of Massachusetts (UMass) Chan Medical School, Worcester, USA. She received her Ph.D. degree in Mathematics in 2012. Along with the other awards she has been conferred, she was granted the Cooperation in Neurological Sciences and Support Award by Turkish Neurology Association as the first mathematician in Turkey. She also holds a medical card as the only mathematician entitled for it. Furthermore, she received the Outstanding Young Scientist Award in 2012 and Best Paper Awards in her specialized discipline, among the other national and international awards in different categories as well as grants. Another award of hers is Outstanding Reviewer Award (Mathematics Journal, MDPI) in 2021. She is the Editor-in-Chief of the book series named Systems Science & Nonlinear Intelligence Dynamics by World Scientific. Dr. Karaca has been acting as the lead editor, editor and associate editor in many different SCI indexed journals. She also has active involvement with diverse projects, some of which are Institute of Electrical and Electronics Engineers (IEEE, as senior member), Organization for Women in Science for the Developing World (OWSD); Complex Human Adaptive Organizations and Systems (CHAOS)- University of Perugia, Italy; International Engineering and Technology Institute (IETI, as the member of Board of Director). Her research interests include complex systems sciences with applications in various terrains, applied mathematics, advanced computational methods, AI applications, computational complexity, fractional calculus, fractals and multifractals, stochastic processes, different kinds of differential and difference equations, discrete mathematics, algebraic complexity, complexity science, wavelet and entropy, solutions of advanced mathematical challenges, mathematical neuroscience and biology as well as advanced data analysis in medicine and other related theoretical, computational and applied domains.

Affiliations and expertise

Assistant Professor of Applied Mathematics and Researcher, University of Massachusetts (UMass) Medical School, Worcester, Massachusetts, USA

Muhammad, Khan
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).