EPJ ST Special Issue: Artificial Intelligence and Complex Networks meet Natural Sciences
- Details
- Published on 07 May 2024
Guest Editors: Alexander Hramov, Dibakar Ghosh, Alexander Pisarchik, Alexey Pavlov, Drozdstoy Stoyanov, Alexey Zaikin, Semyon Kurkin, Miguel A.F. Sanjuán
Aims and scope: Artificial intelligence (AI) is an advanced computational tool used to analyze big data in both fundamental and applied sciences. It has become increasingly popular in complex network science due to its ability to identify hidden patterns and nonlinear relationships in large amounts of ambiguous and nonstationary data from interacting systems. Researchers and engineers are using machine learning and network theory approaches to gain new insights into the behavior of complex systems ranging from the brain to molecules, from the Universe to nano-scale systems as well as for various practical applications such as diagnostic and expert systems, mathematical simulation, prediction and intelligence systems, biomedicine, et al. AI and network approaches have a special role in solving forecasting and early warning problems (e.g., climate modeling, extreme events forecasting, infrastructure stability/resilience problems).
AI-based and complex network methods are particularly important in the medical diagnosis of neurological diseases, where machine learning is a powerful tool for identifying biomarkers of various neurological disorders at an early stage. In this case, the methods and approaches of explainable artificial intelligence (XAI), which is extremely important for modern digital medicine, play a significant role.
The problem of application of AI methods in modern natural science can be conditionally divided into theoretical aspects of the development of machine learning and the theory of complex networks, as well as their combination, and into applications of AI in applied fields from space, climate, energy to biology and medicine.
This Special Issue is dedicated to the publication of high-quality original research and reviews that contribute to the methodological/theoretical aspects of the advancement of state-of-the-art AI concepts and their applications in various domains within natural sciences and engineering. The potential submission topics encompass, but are not limited to:
- Methodological/theoretical problems:
- Explainable AI (XAI) and deep learning in natural sciences
- Multidisciplinary approaches
- Graph neural networks
- Higher-order interactions in complex networks
- Brain functional networks
- AI-based methods for analysis of complex networks
- Spiking neuronal networks
- Extreme events
- Applications:
- In vitro diagnostics with AI application
- Big data processing
- Reservoir computing
- AI-based intelligence systems for brain-computer interfaces
- Nonlinear time series and machine learning methodss
- Climate modeling and forecasting
- Forecasting and early warning problems
- Infrastructure stability and resilience problems
- Power grids
- Social networks
- Data analytics and mining for diagnostics and prediction.
Articles should be submitted to the Editorial Office of EPJ ST via the submission system, and should be clearly identified as intended for the topical issue “Artificial Intelligence and Complex Networks meet Natural Sciences”.
More detailed author information including paper types can be found in the Submission Guidelines. For the preparation of the manuscripts a special latex template (preferably single-column layout) is available here.
Guest Editors:
Prof. Alexander Hramov, Immanuel Kant Baltic Federal University, A. Nevskogo str. 14, 236016 Kaliningrad, Russia, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Prof. Dibakar Ghosh, Indian Statistical Institute, 8th Mile, Mysore Road, R.V.College Post, Bangalore 560059, India, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Prof. Alexander Pisarchik, Universidad Politécnica de Madrid, Rectorado A, Ramiro de Maeztu 7, 28040 Madrid, Spain, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Prof. Alexey Pavlov, Saratov State University, Institute of Physics, Department of Physics of Open Systems, Astrakhanskaya str. 83, 410012 Saratov, Russia, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Prof. Drozdstoy Stoyanov, Medical University of Plovdiv, 15A, Vassil Aprilov Blvd., 4002 Plovdiv, Bulgaria, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Prof. Alexey Zaikin, University College London, Gower Street, London WC1E 6BT, UK, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Prof. Semyon Kurkin, Immanuel Kant Baltic Federal University, A. Nevskogo str. 14, 236016 Kaliningrad, Russia, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Prof. Miguel A.F. Sanjuán, Nonlinear Dynamics, Chaos and Complex Systems Group, Departamento de Física, Universidad Rey Juan Carlos, Tulipán s/n, Móstoles, 28933 Madrid, Spain, Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
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