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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Theory of complexity, origin and complex systems - p. 9 3. Chapter 3 Multi-chaos, fractal and multi-fractional AI in different complex systems - p. 21 4. Chapter 4 High-performance computing and computational intelligence applications with a multi-chaos perspective - p. 55 5. Chapter 5 Human hypercomplexity. Error and unpredictability in complex multichaotic social systems - p. 77 6. Chapter 6 Multifractal complexity analysis-based dynamic media text categorization models by natural language processing with BERT - p. 95 7. Chapter 7 Mittag-Leffler functions with heavy-tailed distributions' algorithm based on different biology datasets to be fit for optimum mathematical models' strategies - p. 117 8. 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 - p. 133 9. Chapter 9 Computational fractional-order calculus and classical calculus AI for comparative differentiability prediction analyses of complex-systems-grounded paradigm - p. 149 10. Chapter 10 Pattern formation induced by fractional-order diffusive model of COVID-19 - p. 169 11. Chapter 11 Prony's series and modern fractional calculus - p. 187 12. Chapter 12 A chain of kinetic equations of Bogoliubov–Born–Green–Kirkwood–Yvon and its application to nonequilibrium complex systems - p. 201 13. Chapter 13 Hearing loss detection in complex setting by stationary wavelet Renyi entropy and three-segment biogeography-based optimization - p. 215 14. Chapter 14 Shannon entropy-based complexity quantification of nonlinear stochastic process - p. 231 15. Chapter 15 Chest X-ray image detection for pneumonia via complex convolutional neural network and biogeography-based optimization - p. 247 16. Chapter 16 Facial expression recognition by DenseNet-121 - p. 263 17. Chapter 17 Quantitative assessment of local warming based on urban dynamics - p. 277 18. Chapter 18 Managing information security risk and Internet of Things (IoT) impact on challenges of medicinal problems with complex settings - p. 291 19. Chapter 19 An extensive discussion on utilization of data security and big data models for resolving healthcare problems - p. 311 20. Index - p. 325
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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Theory of complexity, origin and complex systems - p. 9 3. Chapter 3 Multi-chaos, fractal and multi-fractional AI in different complex systems - p. 21 4. Chapter 4 High-performance computing and computational intelligence applications with a multi-chaos perspective - p. 55 5. Chapter 5 Human hypercomplexity. Error and unpredictability in complex multichaotic social systems - p. 77 6. Chapter 6 Multifractal complexity analysis-based dynamic media text categorization models by natural language processing with BERT - p. 95 7. Chapter 7 Mittag-Leffler functions with heavy-tailed distributions' algorithm based on different biology datasets to be fit for optimum mathematical models' strategies - p. 117 8. 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 - p. 133 9. Chapter 9 Computational fractional-order calculus and classical calculus AI for comparative differentiability prediction analyses of complex-systems-grounded paradigm - p. 149 10. Chapter 10 Pattern formation induced by fractional-order diffusive model of COVID-19 - p. 169 11. Chapter 11 Prony's series and modern fractional calculus - p. 187 12. Chapter 12 A chain of kinetic equations of Bogoliubov–Born–Green–Kirkwood–Yvon and its application to nonequilibrium complex systems - p. 201 13. Chapter 13 Hearing loss detection in complex setting by stationary wavelet Renyi entropy and three-segment biogeography-based optimization - p. 215 14. Chapter 14 Shannon entropy-based complexity quantification of nonlinear stochastic process - p. 231 15. Chapter 15 Chest X-ray image detection for pneumonia via complex convolutional neural network and biogeography-based optimization - p. 247 16. Chapter 16 Facial expression recognition by DenseNet-121 - p. 263 17. Chapter 17 Quantitative assessment of local warming based on urban dynamics - p. 277 18. Chapter 18 Managing information security risk and Internet of Things (IoT) impact on challenges of medicinal problems with complex settings - p. 291 19. Chapter 19 An extensive discussion on utilization of data security and big data models for resolving healthcare problems - p. 311 20. Index - p. 325
