Machine Learning in Data Science
A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990). This special issue belongs to the section "Data".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 277
Special Issue Editors
Interests: artificial intelligence; big data; data analysis; databases; data mining; data structures; machine learning; privacy; security; trust
Special Issues, Collections and Topics in MDPI journals
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Data science is a field of study that focuses on the extraction of valuable information from noisy data, and incorporates various disciplines, such as data engineering, data preprocessing, visualization, predictive analytics, data mining, machine learning, and statistics. In recent years, there has been a rapidly growing interest in the mathematical and theoretical aspects of data science. This manifests in deterministic and non-deterministic models (i.e., probabilistic and a family of probabilistic known as statistical) in order to provide performance guarantee, robustness, reusable, and interpretable results.
The digital transformation of information systems has made feasible the effective use of data science techniques such as artificial intelligence (AI) and machine learning (ML) for various applications. In addition, the use of sensor technology and AI/ML will inevitably lead to more objective and improved performance, lower cost, and more effective system management overall.
The aim of this Special Issue is to provide original, high-quality innovative ideas and research solutions (for both theoretical and practical challenges) for data analysis and modeling with the aid of artificial intelligence and machine learning in various domains and applications.
Dr. Elias Dritsas
Dr. Phivos Mylonas
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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
- data science
- data mining
- artificial intelligence
- machine learning
- statistics
- predictive modeling
- monitoring
- data analytics
