Novel Applications of Machine Learning and Bayesian Optimization, 2nd Edition
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 December 2025 | Viewed by 33
Special Issue Editors
Interests: Bayesian optimization; Gaussian processes; applications of machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: data science; machine learning and artificial intelligence; prognostic and diagnostic technologies for oncology; hyperspectral imaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning and Bayesian optimization are transforming applied sciences by enabling data-driven discovery, prediction, and decision-making. For example, in the chemical and molecular sciences, machine learning accelerates the discovery of novel materials and powers data-driven force fields. In biomedical and clinical sciences, it supports image analysis, disease diagnosis, and predictions of patient outcomes. In the environmental and Earth sciences, machine learning is used to forecast earthquake probabilities and automate the detection of environmental hazards such as litter. Large language models are opening further avenues, from automating hypothesis generation, to generating code for computational experiments. When data are scarce or expensive to obtain, Bayesian optimization plays a crucial role in experimental design, parameter tuning, and exploring trade-offs, and has a long history in engineering design.
This Special Issue will publish high-quality, original research papers advancing the state of the art in the application of machine learning and/or Bayesian optimization
Dr. Glenn Hawe
Dr. Aidan D. Meade
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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
- machine learning
- deep learning
- large language models
- Bayesian optimization
- applied sciences
- healthcare
- materials science
- environmental science
- predictive modelling
- data-driven design
- anomaly detection
- classification
- regression
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