Machine Learning and Related Statistical Applications in Complex Systems
A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 1348
Special Issue Editors
Interests: systems biology and modeling; analysis of complex biological systems; applied machine learning and related statistical applications in complex systems; ultra-wideband radio frequency receivers based on folding and compressive sensing; innovation in engineering and engineering education
Interests: linguistics; cognitive semantics; computational linguistics; humor
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The application of machine learning (ML) technologies has expanded exponentially in recent years, such that ML is now used in a wide number of fields. However, the widespread use of ML has also been accompanied by a number of challenges, including how best to apply ML in complex systems involving interdisciplinary coordination, the reliability and trustworthiness of ML outcomes, and time and resources to train complex system ML applications. Thus, we anticipate that the successful application of ML to complex systems and methods to tackle ML challenges in complex systems will be of interest to both the researchers and practitioners of ML.
We are, therefore, pleased to announce a Special Issue focusing on “Machine Learning and Related Statistical Applications in Complex Systems”. The goal of this Special Issue is to provide a venue for researchers and practitioners to share their latest results, including successful applications, innovative methodologies, and novel approaches to handle ML challenges in complex systems. We invite submissions on a wide range of topics involving ML and related statistical applications in complex systems falling within the two broad areas below:
- Analysis, prediction, classification, and design problems in complex systems, including power systems, transportation systems, financial systems, autonomous vehicles, biological systems, ecosystems, epidemiological modeling, climate modeling, geological modeling, etc.
- Challenges with ML applications, including the safety and reliability of ML in complex systems, such as autonomous vehicles, application of explainable AI (XAI) to complex systems, training bias in real-world applications, data acquisition and training time for complex systems, and application of hardware technologies to accelerate development and implementation of embedded ML systems.
Dr. Gerald L. Fudge
Dr. Christian F. Hempelmann
Guest Editors
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 submissions that pass pre-check are 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. Technologies 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 1600 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.
Keywords
- complex systems
- machine learning
- explainable AI (XAI)
- statistical applications
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