Deep Learning for Intelligent Systems: Challenges and Opportunities
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 25 March 2025 | Viewed by 85
Special Issue Editors
Interests: multi-modal data management; data warehousing and mining; social media and web services; e-learning technologies
Special Issue Information
Dear Colleagues,
Deep learning has emerged as a powerful tool for developing intelligent systems across various domains, including computer vision, natural language processing, robotics, Internet of Things (IoT), smart cities, and healthcare, among others. This Special Issue aims to provide a comprehensive overview of the challenges and opportunities associated with applying deep learning in the context of intelligent systems.
The objective of this Special Issue is to foster a deeper understanding of the challenges faced by researchers and practitioners when utilizing deep learning techniques to build intelligent systems. We aim to explore the potential opportunities that deep learning offers in overcoming these challenges and advancing the field of intelligent systems. By collecting high-quality research papers, we intend to provide a platform for discussing novel methodologies, techniques, and applications in this rapidly evolving field.
This Special Issue aims to gather original research and review articles focusing on the recent advances, technologies, solutions, applications, and challenges in the field of deep learning for intelligent systems. Topics of interest include, but are not limited to, the following:
- Deep learning for sensors;
- Deep learning models and architectures for intelligent systems;
- Deep learning for healthcare and medical diagnosis systems;
- Deep learning for intelligent transportation systems;
- Deep learning for smart cities and IoT applications;
- Deep reinforcement learning for decision making in intelligent systems;
- Deep learning on graphs;
- Deep learning for recommender systems;
- Deep learning on web and social media;
- Explainable deep learning for transparent and interpretable intelligent systems;
- Transfer learning and domain adaptation for intelligent systems;
- Deep learning for multimodal data fusion in intelligent systems;
- Deep learning for autonomous robotics and intelligent agents;
- Deep learning for natural language understanding and generation in intelligent systems;
- Ethical considerations and fairness in deep-learning-based intelligent systems;
- Trustworthy deep learning techniques.
Prof. Dr. Qing Li
Dr. Wenqi Fan
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. Sensors 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 2600 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
- recommender systems
- intelligent transportation systems
- intelligent agents
- multimodal data fusion
- deep learning
