Applications of Machine Learning with White-Boxing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2024 | Viewed by 180
Special Issue Editor
Special Issue Information
Dear Colleagues,
AI is used to build devices and machines that can learn and mimic cognitive the functions of humans. ML is the discipline that develops and investigates methods for predicting a response variable Y based on data collected on Y and potential predictors or features X1, X2, ..., Xp. If the response Y is binary or categorical with only a few labels, the ML method is called a classifier. If data have no Y or labels, the ML method is referred to as an unsupervised learning method. If the response Y is continuous, the ML methods behave like linear or non-linear regression methods. ML provides the predictions that are needed by AI.
ML prediction methods are called Black-Box routines since they only provide a predicted value, with no explanation or interpretation of results. White-Boxing, another ML method, provides some explanation of how the outputs are related to the features.
This Special Issue aims to explore the cutting-edge applications of White-Box machine learning models in AI systems. It provides a platform for researchers, practitioners, and experts to share their innovative research, methodologies, and insights on the application and implications of White-Box approaches in machine learning.
Dr. Ashok K. Singh
Guest Editor
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
- artificial intelligence
- white-box models
- interpretability
- prediction
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
