Computer Science, Machine Learning, Algorithms, and Applied Mathematics

Dear Colleagues,

With the rapid development of intelligent systems and data-centric technologies, modern computational challenges increasingly require advanced mathematical modeling, efficient learning algorithms, and robust optimization strategies. Machine learning, evolutionary computation, and deep neural architectures have become indispensable tools for analyzing high-dimensional, heterogeneous, and dynamic data in various scientific and engineering domains.

At the same time, emerging application areas—such as the Marine Internet of Things (IoT), autonomous systems, smart sensing, and complex environmental monitoring—demand more accurate prediction models, adaptive learning mechanisms, and scalable computational frameworks. These advances highlight the importance of integrating applied mathematics with algorithmic innovation to develop new theories and methods that can support reliable decision-making in complex real-world scenarios.

This Topic invites high-quality research and review articles addressing the theoretical foundations, technological enablers, and practical applications of computer science–driven machine learning and algorithmic innovation, including advances in learning theory, optimization algorithms, intelligent computing frameworks, and data-driven systems across scientific and engineering domains. Topics of interest include, but are not limited to, the following:

• Machine learning algorithms and theoretical foundations;
• Deep learning models and their applications;
• Evolutionary computation and multi-objective optimization;
• Swarm intelligence and cooperative learning frameworks;
• Feature engineering, representation learning, and dimensionality reduction;
• High-dimensional, heterogeneous, or streaming data analysis;
• Marine IoT data modeling, intelligent sensing, and environmental monitoring;
• Object detection, object tracking, and perception algorithms;
• Optimization-driven learning, probabilistic modeling, and mathematical algorithm design;
• Applied mathematics methods in intelligent systems and computational science;

This Topic aims to propose novel algorithms, provide deep mathematical insights, or demonstrate effective applications in engineering, environmental science, autonomous systems, and related fields.

Dr. Kunpeng Zhang
Prof. Dr. Jun-Juh Yan
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. AppliedMath is an international peer-reviewed open access monthly 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 1200 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
• evolutionary computation
• multi-objective optimization
• computational algorithms
• feature engineering
• marine IoT
• intelligent sensing
• object detection and tracking
• computational intelligence