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Algorithms
Special Issues
Advances in Deep Learning and Next-Generation Internet Technologies
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Advances in Deep Learning and Next-Generation Internet Technologies

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2061

Special Issue Editors

Dr. Shun Zhang

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
Interests: intelligent image processing; machine learning and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Gao

E-Mail Website
Guest Editor
Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: hyperspectral image processing; artificial intelligence in oceanography
Special Issues, Collections and Topics in MDPI journals
Dr. Mingyang Ma

E-Mail
Guest Editor
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
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 Future Internet.

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. 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 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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21 pages, 5140 KiB  
Article
Masked Convolutions Within Skip Connections for Video Anomaly Detection
by Demetris Lappas, Vasileios Argyriou and Dimitrios Makris
Algorithms 2025, 18(6), 326; https://doi.org/10.3390/a18060326 - 29 May 2025
Viewed by 237
Abstract
Video anomaly detection plays a crucial role in various fields such as surveillance, health monitoring, and industrial quality control. This research paper introduces a novel contribution to the field by presenting MaskedConv3D layers within a modified UNet architecture. These MaskedConv3D layers provide a [...] Read more.
Video anomaly detection plays a crucial role in various fields such as surveillance, health monitoring, and industrial quality control. This research paper introduces a novel contribution to the field by presenting MaskedConv3D layers within a modified UNet architecture. These MaskedConv3D layers provide a unique approach to information propagation in three-dimensional video data by selectively masking temporal regions of convolutional kernels. By incorporating these layers into the skip connections of the UNet, the model gains the ability to infer missing information in the temporal domain based on the surrounding context. This innovative mechanism enhances the preservation of spatial and temporal details, addressing the challenge of effectively detecting anomalies in video data. The proposed methodology is evaluated on popular video datasets, showcasing its effectiveness in capturing intricate patterns and contexts. The results highlight the superiority of the modified UNet with MaskedConv3D layers compared to traditional approaches. Overall, this research introduces a novel technique for information propagation in video data and demonstrates its potential for advancing video anomaly detection. Full article
(This article belongs to the Special Issue Advances in Deep Learning and Next-Generation Internet Technologies)
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Review

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34 pages, 443 KiB  
Review
Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
by Márton Bendegúz Bankó, Szymon Dyszewski, Michaela Králová, Márton Bertalan Limpek, Maria Papaioannou, Gaurav Choudhary and Nicola Dragoni
Algorithms 2025, 18(4), 209; https://doi.org/10.3390/a18040209 - 9 Apr 2025
Viewed by 1388
Abstract
This paper presents a systematic literature review based on the PRISMA model on machine learning-based Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks. The primary objective of the review is to compare research trends on deployment options, datasets, and [...] Read more.
This paper presents a systematic literature review based on the PRISMA model on machine learning-based Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks. The primary objective of the review is to compare research trends on deployment options, datasets, and machine learning techniques used in the domain between 2019 and 2024. The results highlight the dominance of certain datasets (BoT-IoT and TON_IoT) in combination with Decision Tree (DT) and Random Forest (RF) models, achieving high median accuracy rates (>99%). This paper discusses various datasets that are used to train and evaluate machine learning (ML) models for detecting Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks and how they impact model performance. Furthermore, the findings suggest that due to hardware limitations, there is a preference for lightweight ML solutions and preprocessed datasets. Current trends indicate that larger or industry-specific datasets will continue to gain popularity alongside more complex ML models, such as deep learning. This emphasizes the need for robust and scalable deployment options, with Software-Defined Networks (SDNs) offering flexibility, edge computing being extensively explored in cloud environments, and blockchain-integrated networks emerging as a promising approach for enhancing security. Full article
(This article belongs to the Special Issue Advances in Deep Learning and Next-Generation Internet Technologies)
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