Application of Machine Learning and Deep Learning Methods in Science

Dear Colleagues,

The fields of machine learning (ML) and deep learning (DL) are rapidly developing, with new methods and technologies appearing daily. The actual problem is the effective application of these methods to address diverse challenges arising within human activities. The goal of this Special Issue is to collect and demonstrate practical examples of applying ML/DL methods to solve various scientific problems that provide significant benefits. By collecting such examples, we aim to define and extend the scope and understanding of ML/DL methods. To this end, we invite submissions in any form, including articles, reviews, notes, etc.

Dr. Alexander Ayriyan
Prof. Dr. Vladimir Korenkov
Prof. Dr. Gennady Ososkov
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. AppliedMath 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 1000 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.

• machine learning
• deep learning
• predictive modeling
• pattern recognition
• gradient boosting
• decision trees
• feature analysis and extraction
• natural language processing
• data preprocessing
• data and text mining
• unsupervised and supervised learning
• genetic algorithms
• clustering
• dimensionality reduction
• model evaluation
• model interpretation