Advances in Deep Learning and Next-Generation Internet Technologies
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 20 April 2025 | Viewed by 544
Special Issue Editors
Interests: intelligent image processing; machine learning and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image understanding; hyperspectral image processing; artificial intelligence oceanography
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image processing; video understanding
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the research on deep learning algorithms and their applications within next-generation Internet technologies. Deep-learning-based approaches have demonstrated significant success in areas such as computer vision, natural language processing, and autonomous systems. However, as Internet technologies evolve with the rise of 5G/6G, edge computing, IoT, and cloud infrastructures, the integration of these two fields offers new challenges and opportunities for more efficient, scalable, and real-time AI solutions.
This Special Issue aims to furnish a thorough exploration of recent advancements and emerging trends in the domain of deep learning, including the development of novel architecture design, attention mechanisms for feature extraction, training methodologies, model compression, and various specific applications. Moreover, the Special Issue also explores how deep learning algorithms can be optimized, deployed, and enhanced when integrated with emerging Internet systems. By combining the power of AI with the infrastructure of connected systems, this Issue seeks to investigate the new methodologies, architectures, and applications that emerge from this fusion.
Topic areas include, but are not limited to, the following:
- Novel deep learning algorithms;
- Few-shot, zero-shot, and unsupervised learning;
- Transfer learning and domain adaptation;
- Attention mechanisms for feature extraction;
- Model compression and optimization for deployment in edge/IoT devices;
- Deep learning on edge computing and IoT, 5G/6G networks, and cloud computing;
- AI in autonomous driving, smart cities, healthcare, and industrial IoT;
- Artificial Intelligence of Things;
- Remote sensing and satellite imaging using AI.
You may choose our Joint Special Issue in Algorithms.
Dr. Shun Zhang
Dr. Feng Gao
Dr. Mingyang Ma
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. Future Internet 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 1600 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
- deep learning
- model compression
- feature extraction
- edge computing
- 5G/6G networks
- cloud computing
- autonomous driving
- smart cities
- healthcare
- industrial IoT
- remote sensing
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