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Electronics
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Advances in Machine Learning, Volume II
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Advances in Machine Learning, Volume II

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 4431

Special Issue Editors

Prof. Dr. Jihoon Yang

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sogang University, Seoul 04107, Republic of Korea
Interests: machine learning; artificial intelligence; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Unsang Park

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sogang University, Seoul 04107, Korea
Interests: computer vision; pattern recognition; biometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,  

Today, machine learning, which aims to teach computers in a bid to make them act like humans, has become essential. A number of algorithms, techniques, and methodologies have been proposed for a variety of tasks, including autonomous driving, game playing, disease diagnosis and treatment, fraud detection, spam filtering, speech recognition, object detection, search, and recommendation.  

This Special Issue is seeking high-quality research papers in all areas of machine learning. It is open to well-organized reviews as well as application papers. Topics include but are not limited to the following: 

  • Deep learning;
  • Reinforcement learning;
  • Automated machine learning;
  • On-device learning;
  • Transfer learning;
  • Meta learning;
  • Application of machine learning in real-world domains. 

Prof. Dr. Jihoon Yang
Dr. Unsang Park
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. Electronics 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 2400 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

  • machine learning
  • deep learning
  • data mining and analysis

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

Published Papers (2 papers)

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Research

15 pages, 5030 KiB  
Article
MLBSNet: Mutual Learning and Boosting Segmentation Network for RGB-D Salient Object Detection
by Chenxing Xia, Jingjing Wang and Bing Ge
Electronics 2024, 13(14), 2690; https://doi.org/10.3390/electronics13142690 - 10 Jul 2024
Cited by 1 | Viewed by 1095
Abstract
RGB-D saliency object detection (SOD) primarily segments the most salient objects from a given scene by fusing RGB images and depth maps. Due to the inherent noise in the original depth map, fusion failures may occur, leading to performance bottlenecks. To address this [...] Read more.
RGB-D saliency object detection (SOD) primarily segments the most salient objects from a given scene by fusing RGB images and depth maps. Due to the inherent noise in the original depth map, fusion failures may occur, leading to performance bottlenecks. To address this issue, this paper proposes a mutual learning and boosting segmentation network (MLBSNet) for RGB-D saliency object detection, which consists of a deep optimization module (DOM), a semantic alignment module (SAM), a cross-modal integration (CMI) module, and a separate reconstruct decoder (SRD). Specifically, the deep optimization module aims to obtain optimal depth information by learning the similarity between the original and predicted depth maps. To eliminate the uncertainty of single-modal neighboring features and capture the complementary features of multiple modalities, a semantic alignment module and a cross-modal integration module are introduced. Finally, a separate reconstruct decoder based on a multi-source feature integration mechanism is constructed to overcome the accuracy loss caused by segmentation. Through comparative experiments, our method outperforms 13 existing methods on five RGB-D datasets and achieves excellent performance on four evaluation metrics. Full article
(This article belongs to the Special Issue Advances in Machine Learning, Volume II)
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19 pages, 4886 KiB  
Article
Task Offloading of Deep Learning Services for Autonomous Driving in Mobile Edge Computing
by Jihye Jang, Khikmatullo Tulkinbekov and Deok-Hwan Kim
Electronics 2023, 12(15), 3223; https://doi.org/10.3390/electronics12153223 - 26 Jul 2023
Cited by 9 | Viewed by 2695
Abstract
As the utilization of complex and heavy applications increases in autonomous driving, research on using mobile edge computing and task offloading for autonomous driving is being actively conducted. Recently, researchers have been studying task offloading algorithms using artificial intelligence, such as reinforcement learning [...] Read more.
As the utilization of complex and heavy applications increases in autonomous driving, research on using mobile edge computing and task offloading for autonomous driving is being actively conducted. Recently, researchers have been studying task offloading algorithms using artificial intelligence, such as reinforcement learning or partial offloading. However, these methods require a lot of training data and critical deadlines and are weakly adaptive to complex and dynamically changing environments. To overcome this weakness, in this paper, we propose a novel task offloading algorithm based on Lyapunov optimization to maintain the system stability and minimize task processing delay. First, a real-time monitoring system is built to utilize distributed computing resources in an autonomous driving environment efficiently. Second, the computational complexity and memory access rate are analyzed to reflect the characteristics of the deep learning applications to the task offloading algorithm. Third, Lyapunov and Lagrange optimization solves the trade-off issues between system stability and user requirements. The experimental results show that the system queue backlog remains stable, and the tasks are completed within an average of 0.4231 s, 0.7095 s, and 0.9017 s for object detection, driver profiling, and image recognition, respectively. Therefore, we ensure that the proposed task offloading algorithm enables the deep learning application to be processed within the deadline and keeps the system stable. Full article
(This article belongs to the Special Issue Advances in Machine Learning, Volume II)
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