Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems
View on ScienceDirect

Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems

1st Edition - June 22, 2022

Write a review

  • Editors: Yeliz Karaca, Dumitru Baleanu, Yu-Dong Zhang, Osvaldo Gervasi, Majaz Moonis
  • eBook ISBN: 9780323886161
  • Paperback ISBN: 9780323900324

Purchase options

Purchase options
DRM-free (PDF, EPub)
eBook Format Help
Available
Sales tax will be calculated at check-out

Institutional Subscription

Support CenterReturns & Refunds
Free Global Shipping
No minimum order

Description

Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems addresses different uncertain processes inherent in the complex systems, attempting to provide global and robust optimized solutions distinctively through multifarious methods, technical analyses, modeling, optimization processes, numerical simulations, case studies as well as applications including theoretical aspects of complexity. Foregrounding Multi-chaos, Fractal and Multi-fractional in the era of Artificial Intelligence (AI), the edited book deals with multi- chaos, fractal, multifractional, fractional calculus, fractional operators, quantum, wavelet, entropy-based applications, artificial intelligence, mathematics-informed and data driven processes aside from the means of modelling, and simulations for the solution of multifaceted problems characterized by nonlinearity, non-regularity and self-similarity, frequently encountered in different complex systems. The fundamental interacting components underlying complexity, complexity thinking, processes and theory along with computational processes and technologies, with machine learning as the core component of AI demonstrate the enabling of complex data to augment some critical human skills. Appealing to an interdisciplinary network of scientists and researchers to disseminate the theory and application in medicine, neurology, mathematics, physics, biology, chemistry, information theory, engineering, computer science, social sciences and other far-reaching domains, the overarching aim is to empower out-of-the-box thinking through multifarious methods, directed towards paradoxical situations, uncertain processes, chaotic, transient and nonlinear dynamics of complex systems.

Key Features

  • Constructs and presents a multifarious approach for critical decision-making processes embodying paradoxes and uncertainty.
  • Includes a combination of theory and applications with regard to multi-chaos, fractal and multi-fractional as well as AI of different complex systems and many-body systems.
  • Provides readers with a bridge between application of advanced computational mathematical methods and AI based on comprehensive analyses and broad theories.

Readership

Academics (scientists, researchers, MSc. PhD. students) from the fields of Mathematics, Computer Science, Biology, Applied Mathematics and Information Technology. The audience includes researchers and practitioners in any field that deals with systems sciences – modelling of complex systems, systems analysis and nonlinear systems. Academics, researchers, and industries working in the fields of engineering, medicine, and natural sciences

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • List of contributors
  • Preface
  • Acknowledgment
  • Chapter 1. Introduction
  • Chapter 2. Theory of complexity, origin and complex systems
  • 1. Introduction
  • 2. Theory of complexity, origin and complex systems
  • 3. Complex order processes toward modern scientific path: from Darwin and onwards
  • 4. Concluding remarks and future directions
  • Chapter 3. Multi-chaos, fractal and multi-fractional AI in different complex systems
  • 1. Introduction
  • 2. Challenging dimensions of modern science, complexity and complex systems
  • 3. Artificial intelligence way of thinking, processes, complexity and complex systems
  • 4. Concluding remarks and future directions
  • Chapter 4. High-performance computing and computational intelligence applications with a multi-chaos perspective
  • 1. Introduction
  • 2. Related works
  • 3. High-performance computing approaches to solving complex problems
  • 4. Quantum computing to treat multi-chaos scenarios
  • 5. Techniques enabling the solution of complex problems based on computational intelligence
  • 6. The dilemma of respecting privacy in multi-chaos situations
  • 7. Conclusions
  • 8. Acronyms
  • Chapter 5. Human hypercomplexity. Error and unpredictability in complex multichaotic social systems
  • 1. Introduction
  • 2. The complexity of living energy and living beings
  • 3. Complicated, complex, and hypercomplex systems
  • 4. Taking a step back: a brief history of complexity
  • 5. An epistemology of error
  • 6. “Objects” are relations
  • 7. Everything depends on everything else
  • 8. Cognitive cages
  • 9. è troppo, o troppo ravvicinato?
  • Chapter 6. Multifractal complexity analysis-based dynamic media text categorization models by natural language processing with BERT
  • 1. Introduction
  • 2. Data and methodology
  • 3. Experimental results and discussion
  • 4. Conclusion and future directions
  • Chapter 7. Mittag-Leffler functions with heavy-tailed distributions' algorithm based on different biology datasets to be fit for optimum mathematical models' strategies
  • 1. Introduction
  • 2. Complex biological datasets and methodology
  • 3. Experimental results and discussion: computational application of Mittag-Leffler function based on heavy-tailed distributions for different biological datasets
  • 4. Conclusion and future directions
  • Chapter 8. Artificial neural network modeling of systems biology datasets fit based on Mittag-Leffler functions with heavy-tailed distributions for diagnostic and predictive precision medicine
  • 1. Introduction
  • 2. Complex biological datasets and methodology
  • 3. Experimental results and discussions: artificial neural network modeling of complex biological datasets to be fit based on Mittag-Leffler function with heavy-tailed distributions for diagnosis and prediction
  • 4. Conclusion and future directions
  • Chapter 9. Computational fractional-order calculus and classical calculus AI for comparative differentiability prediction analyses of complex-systems-grounded paradigm
  • 1. Introduction
  • 2. Datasets and methodology
  • 3. Experimental results and discussion
  • 4. Conclusion and future directions
  • Chapter 10. Pattern formation induced by fractional-order diffusive model of COVID-19
  • 1. Introduction
  • 2. Model
  • 3. Spatiotemporal model
  • 4. Weakly nonlinear analysis
  • 5. Numerical simulation
  • 6. Conclusion
  • Chapter 11. Prony's series and modern fractional calculus: Rheological models with Caputo-Fabrizio operator
  • 1. Introduction
  • 2. Prony's method
  • 3. Exponential sums approximation of functions
  • 4. Fractional operators in applied rheology
  • 5. Modeling linear viscoelastic responses
  • 6. Prony's series in linear viscoelasticity
  • 7. Final comments
  • Chapter 12. A chain of kinetic equations of Bogoliubov–Born–Green–Kirkwood–Yvon and its application to nonequilibrium complex systems
  • 1. Introduction
  • 2. Formulation of the problem
  • 3. The solution of the BBGKY hierarchy for many-type particle systems
  • 4. Derivation of the Gross–Pitaevskii equation from the BBGKY hierarchy
  • 5. Summary
  • Chapter 13. Hearing loss detection in complex setting by stationary wavelet Renyi entropy and three-segment biogeography-based optimization
  • 1. Introduction
  • 2. Dataset
  • 3. Methods
  • 4. Implementation
  • 5. Measure
  • 6. Experiment results and discussions
  • 7. Conclusions
  • Appendix
  • Chapter 14. Shannon entropy-based complexity quantification of nonlinear stochastic process: diagnostic and predictive spatiotemporal uncertainty of multiple sclerosis subgroups
  • 1. Introduction
  • 2. Materials and methods
  • 3. Experimental results
  • 4. Conclusion and future directions
  • Chapter 15. Chest X-ray image detection for pneumonia via complex convolutional neural network and biogeography-based optimization
  • 1. Introduction
  • 2. Dataset
  • 3. Methodology
  • 4. Experiment results and discussions
  • 5. Conclusions
  • Chapter 16. Facial expression recognition by DenseNet-121
  • 1. Introduction
  • 2. Dataset
  • 3. Methodology
  • 4. Experiment result and discussions
  • 5. Conclusions
  • Chapter 17. Quantitative assessment of local warming based on urban dynamics
  • 1. Introduction
  • 2. Study areas
  • 3. Materials and methods
  • 4. Results and discussion
  • 5. Conclusions
  • Chapter 18. Managing information security risk and Internet of Things (IoT) impact on challenges of medicinal problems with complex settings: a complete systematic approach
  • 1. Introduction to information security
  • 2. Information security in healthcare
  • 3. Impact of IoT in medical problems
  • 4. Medical problems with complex settings
  • 5. IoT and information security
  • 6. Challenges of medicinal problems using IoT: a case study
  • 7. Conclusion
  • Chapter 19. An extensive discussion on utilization of data security and big data models for resolving healthcare problems
  • 1. Information security
  • 2. Internet of Things
  • 3. Information security and IoT
  • 4. Data security and IoT in medicine
  • 5. Big data and its applications
  • 6. IoT and big data applications in medicine
  • 7. Complex system in healthcare
  • 8. Role of IoT and big data applications in medicine
  • 9. Conclusion
  • Index

Product details

  • No. of pages: 350
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: June 22, 2022
  • Imprint: Academic Press
  • eBook ISBN: 9780323886161
  • Paperback ISBN: 9780323900324

About the Editors

Yeliz Karaca

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

Dumitru Baleanu

Dumitru Baleanu
Dumitru Baleanu is a professor at the Institute of Space Sciences, Magurele-Bucharest, Romania and a visiting staff member at the Department of Mathematics, Çankaya, University, Ankara, Turkey. He received his Ph.D. from the Institute of Atomic Physics in 1996. His fields of interest include Fractional Dynamics and its applications, Fractional Differential Equations and their applications, Discrete Mathematics, Image Processing, Bioinformatics, Mathematical Biology, Mathematical Physics, Soliton Theory, Lie Symmetry, Dynamic Systems on time scales, Computational Complexity, the Wavelet Method and its applications, Quantization of systems with constraints, the Hamilton-Jacobi Formalism, as well as geometries admitting generic and non-generic symmetries.

Affiliations and Expertise

Professor, Institute of Space Sciences, Magurele-Bucharest, Romania

Yu-Dong Zhang

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

Osvaldo Gervasi

Osvaldo Gervasi
Osvaldo Gervasi is a professor at the Department of Mathematics and Computer Science in Perugia University, temporarily serving as deputy director. His scientific interests focus on parallel and distributed systems, computational science, virtual and augmented reality, artificial intelligence, free and libre open source software. He has served as the General Co-Chair or Program Co-Chair of the International Conference on Computational Science and Its Applications (ICCSA) since 2004 and is the President of the not for profit organization ICCSA.

Affiliations and Expertise

Professor, Department of Mathematics and Computer Science, Perugia University, Perugia, Italy

Majaz Moonis

Majaz Moonis
Majaz Moonis is a professor of Neurology and Psychiatry and Director of Stroke Services and Vascular Neurology Program in the University of Massachusetts Medical School and affiliated UMass Memorial Medical Center. His fields of interests include stroke outcomes, particularly role of statins and other medications on the vascular endothelium and its impact in improving stroke and dementia outcomes, automatic detection of AF for a wristwatch (CoPI), and interactions between stroke and dementia with emphasis on machine learning algorithms. He is also involved in clinical and medical applications to provide solutions to challenging health concerns.

Affiliations and Expertise

Professor of Neurology and Psychiatry and Director, Stroke Services and Vascular Neurology Program, University of Massachusetts Medical School and affiliated Umass Memorial Medical Center; Director of Stroke and Sleep Programs, Day Kimball Hospital, Putnam , Connecticut, USA

Ratings and Reviews

Write a review

There are currently no reviews for "Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems"