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DDPCSP 2023 : Data-Driven Processing from Complex Systems Perspective


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Abstract Registration Due May 30, 2023
Submission Deadline Jun 30, 2023
Notification Due Jul 30, 2023
Final Version Due Aug 30, 2023
Categories    big data   artificial intelligence   data-driven   complex systems

Call For Papers

Dear Colleagues,

Complex networked systems with interacting elements characterized by non-linearity, high dimensionality, and heterogeneity in the interconnected universe of today require a solid understanding and control of their structure and dynamics, which has become a grand challenge for various fields of science. Complex systems and their conceptual knowledge along with the related approaches and methodologies are geared toward a viable model regarding how different data entities and streams have an impact on and interact with one another for the generation of features and trends on a multitude of spatiotemporal scales. Computational predictive analytics in highly complex and diverse fields have been and are currently developed for the characterization and quantification of concurrently and mutually interacting facets of different scenarios regarding real-world, universal, and natural phenomena.

New computational methods with complex data and the related advancements in computations aim at a more profound and versatile understanding of the substantial masses of data and have enabled an improvement of predictions by transferring the results based on data analytics into the benefits at large, which underpins the utility and interdisciplinary approach of the domain. Accordingly, data-driven and multifarious methods are required for the optimal prediction solutions and critical decision-making processes, whereby Artificial Intelligence (AI), fractional calculus, and multifractal methods have the capability of learning and modeling the system’s complex behavior, establishing the governing methods from the experimental data. Science pertaining to complex systems relies on data-driven approaches that obtain rigorous principles that generate accurate predictions and reliable laws, enabling the parametrization of models given the available and viable quantification and optimization.

This Special Issue, considering the fact that driven models bring a novel ingredient in the overall modeling of complex systems, focuses on recent advancements, applications, and contributions in Artificial Intelligence (AI) applications, machine learning methods, data analysis, big data analytics, computational predictive analytics, computational complexity, spatiotemporal scales, fractals and multifractional methods, fractional calculus, dynamical processes as per fixed, variable, and distributed systems, nonlinear dynamics and non-equilibrium processes, stochastic processes, fractional order integrodifferentiation, hierarchical nonlinear principal component analyses by machine learning, and related concepts. With this endeavor, we aim at contributing to the research areas of diverse fields on nonlinear integrated systems in complex natural phenomena, complex signal and image processing, recurrent neural networks, differential/integral equations, multiresolution analysis, entropy, wavelets as a reflection of the versatile dimensions of the theoretical and applied areas concerned with mathematics, information science, computer science, physics, biology, medicine, genetics, neuroscience, chemistry, engineering, and social sciences , in addition to the extensive line of other applied sciences.

Potential topics of the special issue include but are not limited to:

Advanced data analysis and/or data visualization in complex models;
Big data analysis within multifractal analysis or fractional calculus methods in complex systems;
Advanced topics in fractional calculus and complex systems;
Quantization optimization algorithms for complex systems;
AI approaches in complex systems for real-world, universal, and natural phenomena;
Data-driven stochastic differential equations;
Machine learning applications in complex data;
Optimization by deep neural networks;
Medical algorithmic applications;
AI applications in signal processing;
Advanced AI and embedded vision for complex surveillance environments;
Advanced computational imaging;
Fractional dynamical models in complex systems;
Fractal dynamics and kinetics of complex systems;
Discrete, stochastic, and hybrid dynamics;
Multifractal systems in real-world, universal, and natural phenomena.

Dr. Yeliz Karaca
Prof. Dr. Yudong Zhang
Dr. Khan Muhammad
Dr. Shuihua Wang
Guest Editors

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