Complex Systems Science and Its Applications in Blockchain, AI, Quantum ML and Big Data Forecasting

Abstract submission deadline

closed (1 October 2022)

Manuscript submission deadline

closed (1 December 2022)

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Topic Information

Dear Colleagues,

Many complex systems in all disciplines (e.g., physics, mathematics, engineering, information theory, nonlinear dynamics, anthropology, computer science, meteorology, sociology, economics, psychology, biology, medicine, etc.) possess nonlinear characteristics that vary over time and encompass hidden patterns. Intelligent decision-making approaches able to deal with continuously adjustable market environments are required in Blockchain, IoT and Decentralized FOG, Edge Cloud computing and Web3.0 applications. New developments at the intersection of Big Data Science, Quantum Computing, Blockchain approaches, Artificial Intelligence (AI), Machine learning (ML), Chaotic and Nonlinear dynamics, Bayesian methods, Evolutionary algorithms, have emerged to study Complex Systems in all scientific fields and topical areas.

The time-varying temporal parameters and variables of inherent nonlinearities in most complex systems remain unknown. Big Data statistics, Detrended Fluctuation analysis, Multi-resolution technique, Wavelets etc., provide new insights in analyzing and forecasting complex systems. Future Technologies with an integrated capacity to reveal nonlinear relationships, learn temporally variant parameter and variable spaces, in order to facilitate expert-level decisions. Blockchain technologies are gaining in importance. We are seeking new evidence to examine how the emergence of crypto-assets will create a new modus operandi for business among global economies, promote economic integration, yet also raise regulatory issues that all economies would have to address. Gamification could be incorporated into the mobile payment to encourage users, however there is a dark side of mobile payment which is explored in money laundering.

We will explore the growing interest of theories derived from information systems, to further add to the advancement of blockchain security systems, cyber-security, cryptography etc. Blockchains have advanced rapidly in recent years, from Bitcoin, the first decentralized cryptocurrency, to Ethereum, which includes smart contracts and the developing permissioned blockchain, e.g., Hyperledger fabric. Studies on the dynamical modeling, chaotic and nonlinear analyses, and control of these complicated systems are rare. Classical and Quantum (hybrid) Artificial intelligence and Machine learning promises to shake up traditional techniques applied to management and control of complex systems. Quantum computing integrated with Big Data and AI and ML-based methods provided higher reliability, accuracy ad predictability for complex systems as well as facilitate the detection of hidden knowledge patterns. We intend to explore recent advances in Future Technologies and their hybrid novel synergies in studying complex systems, as well as to offer possible future directions of research in this field. Contributions could incorporate control, stabilization, estimation, prediction, nowcasting, identification and simulation of all types of complex systems across scientific fields, utilizing Future Technologies in analyzing complex systems.

TOPICS OF INTEREST
We invite researchers and industry experts to submit original contributions in the proposed areas. We welcome theoretical and empirical applications in all disciplines (e.g., physics, mathematics, engineering, information theory, nonlinear dynamics, anthropology, computer science, meteorology, sociology, economics, psychology, biology, medicine, etc.) with emphasis on the synergy between future technologies and complex systems science. The topics of interest include—but are not limited to—the following indicative list:

• Reinforcement learning-based methods for nowcasting and forecasting;
• High-frequency big-data-driven techniques and robust ensemble forecasting;
• Novel bootstrapping techniques in machine learning and quantum computing;
• Hybrid Bayesian and machine learning approaches for complex systems forecasting and nowcasting;
• Timescale, mixed-frequency and wavelet decomposition methods for big data samples;
• Recurrent neural networks and convolutional networks with nonlinear dynamical traits in complexity theory;
• Evolutionary approaches and reinforcement learning-based methods for big data analysis;
• Fractional control systems modeling and fractional complex systems;
• Detection and testing of stochasticity, chaos and/or fractality with statistical and hybrid quantum AI/ML approaches;
• Complex network/graph theory in big data samples;
• Nonlinear dynamical modeling, nonequilibrium phenomena and manifolds;
• Evolutionary approaches and genetic algorithms in hybrid AI/ML topologies;
• Ensemble methods in complex systems forecasting and nowcasting;
• Detrended fluctuation analysis and multiresolution and timescale domain analysis of big data samples;
• Edge cloud computing, FOG, IoT and Web3.0 applications;
• Detection of stochasticity, chaos, fractality and time-varying dynamics with AI/ML approaches;
• Fuzzy logic, neurofuzzy modeling and its applications in complex systems analysis;
• Bayesian methods for big data analysis, forecasting and nowcasting;
• Multivariate extreme value theory in complexity theory;
• Fractals, control systems and fractional order nonlinear dynamics;
• Blockchain-based transparency management in IoT, social media and AI/ML algorithms in big-data-driven forecasting;
• Ethical and societal aspects of digital currencies and blockchains, privacy concerns and security in the FinTech /DeFi space;
• Game theory in distributed ledgers and blockchains in big-data-driven economic forecasting;
• Novel blockchain cryptocurrency protocols and big-data-driven forecasting;
• Integration of blockchain protocols with AI, IoT and big-data-driven forecasting;
• Integration of chaotic, convolutional ANNs and reinforcement learning with novel quantum computing algorithms;
• Novel hybrid classical/quantum algorithms in PDE-based complex systems;
• Quantum key distribution (QKD) cryptographic approaches for various blockchain protocols.

Prof. Dr. Stelios Bekiros
Dr. Salim Lahmiri
Dr. Hadi Jahanshahi
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.3	3.7	2008	15 Days	CHF 1600
Entropy entropy	2.7	4.7	1999	20.8 Days	CHF 2600
Forecasting forecasting	3.0	4.0	2019	28.5 Days	CHF 1800
Fractal and Fractional fractalfract	5.4	3.6	2017	18.9 Days	CHF 2700
Machine Learning and Knowledge Extraction make	3.9	8.5	2019	19.9 Days	CHF 1800
Mathematics mathematics	2.4	3.5	2013	16.9 Days	CHF 2600

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