Recent Advances in Deep Learning and Emerging Applications

Dear Colleagues,

Deep learning has emerged as one of the most transformative technological advances of the past decade, offering unprecedented capabilities in perception, prediction, and decision-making across an expansive range of scientific and industrial fields. As computational power, data availability, and model architectures continue to evolve, deep learning will play a central role in addressing complex challenges in areas such as environmental monitoring, healthcare analytics, autonomous systems, and intelligent manufacturing. These developments not only highlight the technical progress of the field, but also its potential to drive broader societal and economic benefits through improved efficiency, enhanced sustainability, and data-driven innovation.

In this Special Issue, ‘Recent Advances in Deep Learning and Emerging Applications’, we will showcase cutting-edge research that advances both the theoretical foundations and the practical applications of deep learning. It will provide a platform for researchers, engineers, and practitioners to share innovative neural network models, optimization strategies, learning algorithms, and multimodal data analysis techniques. We also seek contributions that explore the integration of deep learning into real-world systems, including smart cities, medical diagnosis, environmental assessment, and sustainable development. By fostering interdisciplinary dialog and methodological innovation, this Special Issue aims to accelerate progress toward more robust, interpretable, and socially beneficial deep learning technologies.

This Special Issue aligns closely with the scope of AI, as deep learning offers powerful tools for supporting sustainable decision-making, optimizing resource use, enhancing environmental resilience, and enabling intelligent systems that contribute to long-term societal well-being. By presenting state-of-the-art research and practical advances, the Special Issue will contribute to the journal’s mission of promoting sustainability science and responsible technological development.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

• Novel deep learning architectures and training methodologies;
• Optimization algorithms for improving robustness, generalization, and efficiency;
• Deep learning applications in environmental monitoring, smart cities, and sustainable systems;
• Medical and healthcare applications based on deep neural networks;
• Multimodal learning and data fusion techniques;
• Interpretable, trustworthy, and energy-efficient deep learning models;
• Case studies demonstrating real-world deployment of deep learning technologies.

Through this Special Issue, we aim to facilitate knowledge exchange, stimulate collaborative research, and inspire practical advancements in deep learning and its diverse applications.

We look forward to receiving your contributions.

Prof. Dr. Jeng-Shyang Pan
Prof. Dr. Junzo Watada
Dr. Pei Hu
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. AI 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.

• deep learning
• sustainable decision-making
• multimodal learning
• interpretable