Machine Learning in Action: Practical Applications and Emerging Trends
A special issue of AI (ISSN 2673-2688).
Deadline for manuscript submissions: 31 May 2026 | Viewed by 976
Special Issue Editors
Interests: application of machine learning to civil engineering; application of IoT to civil engineering; machine learning and the environment
Special Issues, Collections and Topics in MDPI journals
Interests: embedded electronics; machine learning; image processing; security and defense; indirect monitoring
Special Issue Information
Dear Colleagues,
We are pleased to invite you to contribute to this Special Issue, ‘Machine Learning in Action: Practical Applications and Emerging Trends’, which focuses on the deployment of machine learning systems in real-world settings. While recent years have seen remarkable advances in machine learning (ML) theory, a critical gap remains between academic research and operational deployment.
This Special Issue aims to bridge that gap by showcasing applied ML work across diverse domains, including, but not limited to, healthcare, finance, energy, transportation, industrial processes, and environmental monitoring. We seek contributions that detail the development, deployment, and performance of ML-based solutions in practice. Of particular interest are papers that highlight domain-specific constraints, implementation challenges, data limitations, or integration issues encountered in the field.
Although the academic literature is rich in algorithmic and theoretical advances, relatively few publications have systematically documented how these methods perform when embedded in real environments. This Special Issue will contribute a much-needed supplement to the existing body of scholarship by focusing on the transition from research prototypes to functioning systems, emphasizing the lessons learned, contextual adaptation, and long-term maintenance. It will serve as a valuable reference both for practitioners and researchers aiming to understand what it takes to transport ML from the lab to real-world settings.
We look forward to receiving your submissions.
Dr. Alberto José Alvarellos González
Dr. Daniel Carreres-Prieto
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. AI 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
- applied machine learning
- real-world deployment
- domain-specific machine learning
- machine learning systems in production
- practical challenges in machine learning
- data-driven decision making
- case studies in machine learning
- evaluation of machine learning applications
- translational machine learning
- machine learning for domain-centered applications
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