Recent Advances in Statistical Learning: Theories, Technologies and Environmental & Industrial Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 521
Special Issue Editors
Interests: deep learning; statistical modeling; image analysis; time-series analysis
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; geo-mathematics; image analysis; time-series analysis
Special Issues, Collections and Topics in MDPI journals
Interests: automotive control; intelligent driving system; chance constrained optimization; computation; statistics
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; optimization and control theory; artificial intelligence; power system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the outstanding renaissance of artificial intelligence (AI) has achieved unprecedented success in all fields across the world. Meanwhile, innovative applications in natural language processing (NLP), computer vision (CV), and recommender systems (RS) have demonstrated the superior learning power of AI in learning effective patterns from datasets. In fact, the foundation of AI is heavily reliant on statistical learning models and theories. Statistical learning from big data or sparse data can be very helpful to investigate intelligent systems' characteristics and guarantee their stability, security, and economic viability.
However, there are still many challenging problems in the statistical learning field that remain unsolved due to the various data formats, more complex data structures, and high sparsity of the data. Thus, new techniques and advanced engineering applications in statistical learning are still appealing to many scholars in the field.
This Special Issue aims to provide selected contributions on recent advances in statistical learning theories, models, and applications. The potential topics include, but not limited to, the following:
- Statistical learning and data driven control intelligent systems;
- Deep learning theory and applications;
- Statistical learning in linear and nonlinear systems;
- Unsupervised statistical learning;
- Deep generative models and related applications;
- Computer vision;
- Natural language processing;
- Deep reinforcement learning.
Dr. Yusen He
Dr. Huajin Li
Dr. Xun Shen
Prof. Dr. Zhenhao Tang
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. Electronics 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
- statistical learning
- data-driven modeling
- artificial intelligence
- deep learning
