Special Issue "Machine Learning Applications in Aviation Safety"
A special issue of Aerospace (ISSN 2226-4310).
Deadline for manuscript submissions: closed (30 September 2020).
Interests: multi-disciplinary design optimization; multi-disciplinary analysis; probabilistic design; aircraft design; propulsion design; rotorcraft; systems engineering; systems of systems and technology assessments
Interests: air transportation systems; safety and risk; machine learning; sustainability; deep learning; aviation big data; smart manufacturing
The present Special Issue entitled “Machine Learning Applications in Aviation Safety” focuses on topics related to the application of machine learning, deep learning, and other emerging data-driven techniques in the context of enhancing safety in aviation and the air transportation system. Machine learning and deep learning techniques have revolutionized many domains of application such as image recognition, natural language processing, autonomous driving, etc. These techniques have proved increasingly useful in the analysis of big data obtained from aviation operations in recent years. Therefore, this Special Issue solicits novel applications of such techniques for the goal of improving the safety and reliability of aviation operations—both commercial and general aviation. The applications could be intended for in-flight or retrospective analysis and conducted at individual aircraft level, fleet level, or system level. Authors are invited to submit full research articles or review manuscripts addressing (but not limited to) the following topics:
- Data processing frameworks for handling big data in aviation domain;
- Data fusion framework for leveraging multiple sources of information;
- Predictive models for risk likelihood using aviation data;
- Precursor identification for safety incidents, events, accidents using text/data mining;
- Anomaly detection in air traffic or operations using flight data;
- Challenges and opportunities in the application of machine learning in aviation safety data.
Moreover, the focal topics listed above are not meant to exclude articles from additional related areas. We are looking forward to receiving your submissions and kindly invite you to address the Guest Editors in case of further questions.
Prof. Dr. Dimitri Mavris
Dr. Tejas Puranik
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- anomaly detection
- machine learning
- deep learning
- big data
- air transportation system