Advances in Pattern Analysis and Machine Learning

Dear Colleagues,

The rapid development of artificial intelligence and data-driven technologies has profoundly transformed the field of pattern analysis and machine learning. However, as real-world environments become increasingly dynamic, uncertain, and heterogeneous, traditional closed-world assumptions and static learning paradigms are no longer adequate. This Special Issue, Advances in Pattern Analysis and Machine Learning, seeks to highlight cutting-edge methodologies and applications that address these emerging challenges, with a particular emphasis on robustness, adaptability, interpretability, privacy preservation, and temporal awareness.

(1) Overall Outline

1. Focus: This collection focuses on advancing the theoretical foundations and practical applications of machine learning techniques that enable systems to be robust, interpretable, adaptive, and effective in dynamic environments.
2. Scope: Topics of interest include, but are not limited to, robust and open set recognition, federated learning, domain adaptation, multi-label learning, temporal and sequential data analysis, and model interpretability. Contributions may encompass novel models, optimization strategies, algorithmic frameworks, and real-world applications across various domains, including computer vision, natural language processing, and biomedical informatics.
3. Purpose: The purpose of this Special Issue is to provide a platform for disseminating innovative research, promote cross-disciplinary dialogue, and build a comprehensive reference point for the development of trustworthy and scalable intelligent systems.

(2) Relation to Existing Literature

This collection will supplement the existing literature by bringing together multiple frontier themes that are often studied in isolation. Whereas prior surveys or special issues have tended to focus on single aspects (e.g., only federated learning or only adversarial robustness), this collection integrates cross-cutting perspectives on robustness, privacy, domain shift, interpretability, and temporal modeling. It will thus bridge the gap between traditional pattern recognition and modern machine learning frameworks, offering novel insights and interdisciplinary approaches for deploying intelligent systems in dynamic and uncertain real-world contexts.

We warmly invite contributions from researchers, practitioners, and industry experts to join us in advancing the frontier of pattern analysis and machine learning.

Dr. Yuexuan An
Dr. Shen-Huan Lyu
Dr. Mingjing Du
Dr. Xingyu Zhao
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 250 words) can be sent to the Editorial Office for assessment.

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.

• robust machine learning
• federated learning
• domain adaptation
• temporal pattern analysis
• model interpretability