International Journal of Computational Intelligence Systems - Call for Papers: Advances in Computational Intelligence for Civil Aviation Safety Management
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Title: Advances in Computational Intelligence for Civil Aviation Safety Management
Guest Editors: Zhen-Song Chen, Sheng-Hua Xiong, Witold Pedrycz and Mirosław J. Skibniewski
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The civil aviation sector has experienced significant expansion in recent decades, characterized by a doubling of air traffic every 15 years. Nevertheless, the swift growth of this phenomenon further amplifies the intricate systemic risks that have the potential to result in catastrophic tragedies. The task of ensuring safety in aviation systems and operations becomes ever more difficult as they expand in size and intricacy on a global scale.
The conventional methods of safety management in the aviation industry are insufficient when confronted with contemporary issues. Legacy paradigms mainly depend on the utilization of reactive event investigation and human skills in order to evaluate risks. Aviation in the present day encompasses intricately integrated socio-technical systems, characterized by intricate interplays among many elements such as aircraft, air traffic control, airports, laws, and human factors. Relying just on manual analysis is insufficient to effectively handle the vast amount, rapid rate, and diverse nature of safety data that is accessible. The current situation has resulted in a significant need for sophisticated computational approaches that facilitate the prediction and proactive management of aviation safety.
The recent advancements in artificial intelligence (AI), machine learning (ML), and data science have the potential to significantly transform safety standards within the aviation industry. Intelligent algorithms present innovative methods for extracting practical insights from the vast array of diverse aviation data. This enables the identification of concealed dangers and risks in advance of actual situations. Expert systems have the capability to automate the process of obtaining knowledge and making decisions related to safety management. State-of-the-art deep learning methodologies possess a remarkable ability to effectively integrate and analyze many sources of multimodal data, therefore revealing connections that are beyond the perceptual capabilities of human observers. The utilization of physics-based modeling and simulation enables the replication of safety occurrences inside a controlled virtual environment, facilitating their investigation.
Essentially, contemporary computational intelligence facilitates a fundamental change in approach, moving away from dependence on human knowledge and reactive inquiry towards automated, methodical, anticipatory safety management driven by aviation data. Numerous assessments underscore the significant possibility of implementing these advancements in the realm of aviation safety protocols. The primary objective of this special issue is to stimulate innovation by exploring the convergence of computational intelligence and aviation safety management.
We seek novel contributions demonstrating real-world viability and measured safety improvements from applying AI, ML and data science to core aviation safety challenges including, but not limited to:
- Physics-based simulation models for safety scenario analysis
- Risk identification using predictive analytics and forecasting
- Anomaly detection in aircraft condition monitoring data
- Automated analysis of flight data, cockpit voice recorder data, and incident reports
- Natural language processing for aviation safety reports
- Computer vision applications in aviation infrastructure inspection
- Predictive maintenance utilizing deep learning on maintenance logs
- Human factors modeling using cognitive systems engineering
- Virtual reality environments for aviation safety training
- Expert systems and knowledge-based decision support tools
- Multi-modal data fusion from diverse aviation safety sources
- Visual analytics dashboards for safety data exploration
- Blockchain applications for secure aviation data infrastructure
Implementations demonstrating measurable improvements in aviation safety, even in proprietary environments, are especially encouraged. This special issue provides a platform to showcase transformative safety management paradigms enabled by modern computational intelligence. The insights gleaned will provide invaluable guidance as the aviation industry continues adopting data-driven automated safety practices. SI also invites original research papers, review articles, and case studies that address the topics of the 2023 Civil Aviation Safety (Fire and Ice) Theme Academic Forum (CASFI23).
Important dates
Submission of papers: Aug 31, 2024
Notification of review results: Nov 30, 2024
Submission of revised papers: Dec 31, 2024
Notification of final review results: Feb 28, 2025
Guest Editors
Dr. Zhen-Song Chen, School of Civil Engineering, Wuhan University, China
Dr. Sheng-Hua Xiong, College of Civil Aviation Safety Engineering, Civil Aviation Flight University of China, China
Prof. Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Canada
Prof. Mirosław J. Skibniewski, Department of Civil and Environmental Engineering, University of Maryland, USA