Explainable and Multimodal Machine Learning Algorithms for Intelligent Real-World Applications

Dear Colleagues,

Recent advances in machine learning and artificial intelligence have significantly improved the ability of intelligent systems to analyze complex data, support decision-making and operate in real-world environments. However, the successful deployment of these systems still requires algorithmic solutions that are not only accurate, but also explainable, trustworthy, robust, computationally efficient and capable of integrating heterogeneous data from multiple sources.

This Special Issue, “Explainable and Multimodal Machine Learning Algorithms for Intelligent Real-World Applications”, invites high-quality original research and review articles focusing on advanced machine learning, deep learning, explainable AI and multimodal learning algorithms for practical intelligent systems. The aim is to provide a focused platform for researchers developing transparent, reliable and application-oriented AI methods that address real-world challenges, particularly in healthcare, biomedical engineering, smart systems and data-driven decision-making.

We particularly welcome contributions that combine methodological innovation with rigorous experimental validation, reproducibility and measurable practical impact. Topics of interest include, but are not limited to:

• Explainable and trustworthy artificial intelligence;
• Multimodal machine learning and data fusion algorithms;
• Deep learning architectures for real-world intelligent systems;
• Computer vision, pattern recognition and medical image analysis;
• Biomedical data analytics and healthcare decision-support systems;
• Human-centered AI and interpretable intelligent systems;
• Robustness, uncertainty estimation, fairness and model reliability;
• Transfer learning, domain adaptation, continual learning and federated learning;
• Optimization algorithms for machine learning and deep learning models;
• Edge AI, lightweight models and resource-efficient intelligent computing;
• Predictive modeling, anomaly detection and intelligent analytics;
• AI algorithms for smart healthcare, smart industry and scientific applications.

The goal of this Special Issue is to collect high-quality studies that advance explainable, multimodal and trustworthy machine learning algorithms and demonstrate their value in real-world intelligent applications. We encourage submissions that clearly present algorithmic novelty, practical relevance and strong evaluation across realistic datasets or application scenarios.

Prof. Dr. Jong-Ha Lee
Dr. Sitara Afzal
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms 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 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.