Machine Learning: Techniques, Industry Applications, Code Sharing, and Future Trends
A special issue of Computers (ISSN 2073-431X).
Deadline for manuscript submissions: 31 October 2025 | Viewed by 1432
Special Issue Editors
2. Rural Health Research Institute, Charles Sturt University, Orange, NSW 2800, Australia
Interests: artificial intelligence; uncertainty quantification; imbalanced data
Special Issues, Collections and Topics in MDPI journals
Interests: cloud computing; networks and distributed systems; blockchain; deep learning; natural language processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to highlight the importance of transparency, reproducibility, and openness in machine learning research by encouraging solutions accompanied by publicly shared codes. The goal is to promote best practices in sharing codes and datasets, making it easier for the research community to reproduce and build upon existing works.
Potential authors are encouraged to submit new concepts according to the submission guidelines. We also encourage researchers to share their codes in public repositories and implement them in open platforms like Kaggle, Code Ocean, etc. Editors and reviewers will aim to improve the presented concepts by providing effective feedback to researchers. This Special Issue can potentially bring about technological advances and an improved understanding of concepts among everyone involved, including readers.
Scope and Topics of Interest:
We invite original research papers, reviews, and case studies that demonstrate innovative applications of machine learning and provide public access to the codebases used for the research. The topics of interest include, but are not limited to, the following:
- Open-source machine learning frameworks and tools;
- New machine learning models with publicly available implementation;
- Benchmarking studies with open access datasets and codes;
- Case studies and applications of machine learning in various domains with shared codes;
- Best practices for reproducibility in machine learning research;
- Public repositories and tools for collaborative machine learning development;
- Studies on the impact of code sharing in AI research;
- Efficient data preprocessing, feature extraction, and model evaluation using shared codes;
- Reusable machine learning pipelines and workflows.
Dr. Hussain Mohammed Dipu Kabir
Dr. Subrota Kumar Mondal
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. Computers 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 1800 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
- open-source machine learning
- reproducible research
- code sharing in AI
- machine learning frameworks
- publicly available datasets
- transparent machine learning
- benchmarking in machine learning
- collaborative machine learning
- open science in AI
- code-based research validation
- machine learning algorithms with codes
- open repositories in ML
- GitHub for machine learning
- computational experiment reproducibility
- best practices in code sharing
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