Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition
share announcement

Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1751

Special Issue Editors

Prof. Dr. Yujin Lim

E-Mail Website
Guest Editor
Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Interests: multi-agent system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hideyuki Takahashi

E-Mail Website
Guest Editor
Department of Data Science, Faculty of Informatics, Tohoku Gakuin University, Miyagi 984-8588, Japan
Interests: Internet of Things; ubiquitous computing; multi-agent system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning and artificial intelligence have attracted great attention in almost every field in recent years. Applications of deep learning and artificial intelligence methods are now pervasive, being used into various fields beyond conventional computer engineering areas. Therefore, the goal of this Special Issue is to discuss new ideas and recent experimental results in the fields of the applications of deep learning and artificial intelligence methods.

Topics of interest include, but are not limited to, the following subjects:

  • Artificial intelligence tools and applications;
  • Automatic control;
  • Natural language processing;
  • Computer vision and speech understanding;
  • Data mining and analysis;
  • Heuristic and AI planning strategies;
  • Intelligent system;
  • Robotics.

Prof. Dr. Yujin Lim
Prof. Dr. Hideyuki Takahashi
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. Applied Sciences 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
  • deel learning
  • artificial intelligence
  • natural language processing
  • computer vision
  • data mining
  • robotics

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 3191 KiB  
Article
A Multi-Target Identification and Positioning System Method for Tomato Plants Based on VGG16-UNet Model
by Xiaojing Li, Jiandong Fang and Yvdong Zhao
Appl. Sci. 2024, 14(7), 2804; https://doi.org/10.3390/app14072804 - 27 Mar 2024
Viewed by 387
Abstract
The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on [...] Read more.
The axillary buds that grow between the main and lateral branches of tomato plants waste nutrients and lead to a decrease in yield, necessitating regular removal. Currently, these buds are removed manually, which requires substantial manpower and incurs high production costs, particularly on a large scale. Replacing manual labor with robots can lead to cost reduction. However, a critical challenge is the accurate multi-target identification of tomato plants and precise positioning for axillary bud removal. Therefore, this paper proposes a multi-target identification and localization method for tomato plants based on the VGG16-UNet model. The average intersection and pixel accuracies of the VGG16-UNet model after introducing the pretrained weights were 85.33% and 92.47%, respectively, which were 5.02% and 4.08% higher than those of the VGG16-UNet without pretrained weights, achieving the identification of main branches, side branches, and axillary bud regions. Then, based on the multi-objective segmentation of the tomato plants in the VGG16-UNet model, the regions of the axillary buds in the tomato plants were identified by HSV color space conversion and color threshold range selection. Morphological dilation and erosion operations were used to remove noise and connect adjacent regions of the same target. The endpoints and centroids of the axillary buds were identified using the feature point extraction algorithm. The left and right positions of the axillary buds were judged by the relationship between the position of the axillary bud centroid and the position of the main branch. Finally, the coordinate parameters of the axillary bud removal points were calculated using the feature points to determine the relationship between the position of the axillary bud and the position of the branch. Experimental results showed that the average accuracy of the axillary bud pruning point recognition was 85.5%. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

11 pages, 8566 KiB  
Article
Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
by Leon Ruddat, Laurenz Reichardt, Nikolas Ebert and Oliver Wasenmüller
Appl. Sci. 2024, 14(7), 2781; https://doi.org/10.3390/app14072781 - 26 Mar 2024
Viewed by 395
Abstract
Detecting vulnerable road users is a major challenge for autonomous vehicles due to their small size. Various sensor modalities have been investigated, including mono or stereo cameras and 3D LiDAR sensors, which are limited by environmental conditions and hardware costs. Radar sensors are [...] Read more.
Detecting vulnerable road users is a major challenge for autonomous vehicles due to their small size. Various sensor modalities have been investigated, including mono or stereo cameras and 3D LiDAR sensors, which are limited by environmental conditions and hardware costs. Radar sensors are a low-cost and robust option, with high-resolution 4D radar sensors being suitable for advanced detection tasks. However, they involve challenges such as few and irregularly distributed measurement points and disturbing artifacts. Learning-based approaches utilizing pillar-based networks show potential in overcoming these challenges. However, the severe sparsity of radar data makes detecting small objects with only a few points difficult. We extend a pillar network with our novel Sparsity-Robust Feature Fusion (SRFF) neck, which combines high- and low-level multi-resolution features through a lightweight attention mechanism. While low-level features aid in better localization, high-level features allow for better classification. As sparse input data are propagated through a network, the increasing effective receptive field leads to feature maps of different sparsities. The combination of features with different sparsities improves the robustness of the network for classes with few points. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

18 pages, 1514 KiB  
Article
MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
by Minseok Koo and Jaesung Park
Appl. Sci. 2024, 14(2), 605; https://doi.org/10.3390/app14020605 - 10 Jan 2024
Viewed by 648
Abstract
Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the [...] Read more.
Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the system while providing users with a reasonable service delay by adjusting the number of active MECSs according to the load imposed on the system. To tackle the problem, we identify two crucial issues that influence the design of an effective sleep control technique and propose methods to address each of these issues. The first issue is accurately predicting the system load. Changes in system load are spatio-temporally correlated among MECSs. By leveraging such correlation information with STGCN (Spatio-Temporal Graph Convolutional Network), we enhance the prediction accuracy of task arrival rates for each MECS. The second issue is rapidly selecting MECSs to sleep when the load distribution over an MEC system is given. The problem of choosing sleep MECS is a combinatorial optimization problem with high time complexity. To address the issue, we employ a genetic algorithm and quickly determine the optimal sleep MECS with the predicted load information for each MECS. Through simulation studies, we verify that compared to the LSTM (Long Short-Term Memory)-based method, our method increases the energy efficiency of an MEC system while providing a compatible service delay. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop