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Applied Sciences
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Selected Papers from the ICCAI and IMIP 2022
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Selected Papers from the ICCAI and IMIP 2022

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 12874

Special Issue Editors

Prof. Dr. Jie Yang

E-Mail Website
Guest Editor
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
Interests: artificial intelligence; machine learning; computer vision; medical image analysis and 3D construction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hiroshi Fujita

E-Mail Website
Guest Editor
Faculty of Engineering, Gifu University, Gifu 501-1194, Japan
Interests: computer-aided diagnosis system; image analysis and processing; image evaluation in medicine

Special Issue Information

Dear Colleagues,

2022 8th International Conference on Computing and Artificial Intelligence (ICCAI 2022) and its combined conference — 2022 4th International Conference on Intelligent Medicine and Image Processing (IMIP 2022) are international conferences devoted specifically to facilitate synergies in research and development in the areas of Computing and artificial intelligence, intelligent medicine and image processing. It provides a communication platform for those who are leading experts and scholars from around the world.

This Special Issue will mainly focus on intelligent computing, artificial intelligence, computer and information technology, intelligent medicine, image processing and other related areas. We cordially invite authors in the field to submit original research or review articles pertaining to this important and fast-progressing field of applied sciences.

Prof. Dr. Jie Yang
Prof. Dr. Hiroshi Fujita
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

  • intelligent computing
  • artificial intelligence
  • computer and information technology
  • image analysis and processing
  • computer vision
  • big data science and information intelligence
  • medical image analysis and processing
  • computer-aided detection/diagnosis
  • AI-based medicine

Published Papers (6 papers)

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Research

16 pages, 4009 KiB  
Article
Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
by Shuguang Li, Zhenxu Li, Xinxin Liu, Chunxiang Shan, Yang Zhao and Hong Cheng
Appl. Sci. 2022, 12(17), 8670; https://doi.org/10.3390/app12178670 - 29 Aug 2022
Cited by 2 | Viewed by 1536
Abstract
Vision-based localization techniques and detection technologies are key algorithms for the localization and navigation of unmanned vehicles. Especially in scenarios where GPS signals are missing, Simultaneous Localization and Mapping (SLAM) techniques that rely on vision, inertial navigation system (INS) and other sensors have [...] Read more.
Vision-based localization techniques and detection technologies are key algorithms for the localization and navigation of unmanned vehicles. Especially in scenarios where GPS signals are missing, Simultaneous Localization and Mapping (SLAM) techniques that rely on vision, inertial navigation system (INS) and other sensors have important applications. Among them, vision combined with the IMU SLAM system has the advantage of realistic scale, which is lacking in monocular vision and computational power compared to multi-visual vision, so it is suitable for application in an unmanned vehicle system. In this paper, we propose a fusion localization algorithm that combines a visual-inertial SLAM system and map road information, processing road information in a map under structured roads, and detecting lane lines and locating its local position by a monocular camera, applying a strategy of position prediction and update for map-SLAM fusion localization. It solves the problem of accumulating errors in a pure SLAM system without loopback and provides accurate global-local positioning results for unmanned vehicle positioning and navigation. Full article
(This article belongs to the Special Issue Selected Papers from the ICCAI and IMIP 2022)
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18 pages, 1606 KiB  
Article
The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM
by Bingxin Xue, Cui Zhu, Xuan Wang and Wenjun Zhu
Appl. Sci. 2022, 12(16), 8273; https://doi.org/10.3390/app12168273 - 18 Aug 2022
Cited by 4 | Viewed by 1567
Abstract
Graph Convolutional Neural Network (GCN) is widely used in text classification tasks. Furthermore, it has been effectively used to accomplish tasks that are thought to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make [...] Read more.
Graph Convolutional Neural Network (GCN) is widely used in text classification tasks. Furthermore, it has been effectively used to accomplish tasks that are thought to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and it is not good at capturing local information. The Bidirectional Encoder Representation from Transformers (BERT) has the ability to capture contextual information in sentences or documents, but it is limited in capturing global (the corpus) information about vocabulary in a language, which is the advantage of GCN. Therefore, this paper proposes an improved model to solve the above problems. The original GCN uses word co-occurrence relationships to build text graphs. Word connections are not abundant enough and cannot capture context dependencies well, so we introduce a semantic dictionary and dependencies. While the model enhances the ability to capture contextual dependencies, it lacks the ability to capture sequences. Therefore, we introduced BERT and Bi-directional Long Short-Term Memory (BiLSTM) Network to perform deeper learning on the features of text, thereby improving the classification effect of the model. The experimental results show that our model is more effective than previous research reports on four text classification datasets. Full article
(This article belongs to the Special Issue Selected Papers from the ICCAI and IMIP 2022)
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15 pages, 2846 KiB  
Article
An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos
by Deying Feng, Jie Yang, Yanxia Wei, Hairong Xiao and Laigang Zhang
Appl. Sci. 2022, 12(15), 7670; https://doi.org/10.3390/app12157670 - 29 Jul 2022
Cited by 1 | Viewed by 1085
Abstract
Existing person search methods mainly focus on searching for the target person using database images. However, this is different from real-world surveillance videos which involve a temporal relationship between video frames. To solve this problem, we propose an efficient person search method that [...] Read more.
Existing person search methods mainly focus on searching for the target person using database images. However, this is different from real-world surveillance videos which involve a temporal relationship between video frames. To solve this problem, we propose an efficient person search method that employs spatio-temporal features in surveillance videos. This method not only considers the spatial features of persons in each frame, but also utilizes the temporal relationship of the same person between adjacent frames. For this purpose, the spatial features are extracted by combining Yolo network with Resnet-50 model, and the temporal relationship is processed by gated recurrent unit. The spatio-temporal features are generated by the following average pooling layer and used to represent persons in the videos. To ensure search efficiency, locality sensitive hashing is used to organize massive spatio-temporal features and calculate the similarity. A surveillance video database is also constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves search accuracy while ensuring search efficiency. Full article
(This article belongs to the Special Issue Selected Papers from the ICCAI and IMIP 2022)
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20 pages, 1948 KiB  
Article
Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
by Suzhen Wang, Zhongbo Hu, Yongchen Deng and Lisha Hu
Appl. Sci. 2022, 12(12), 6154; https://doi.org/10.3390/app12126154 - 17 Jun 2022
Cited by 6 | Viewed by 1802
Abstract
Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing [...] Read more.
Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices. Full article
(This article belongs to the Special Issue Selected Papers from the ICCAI and IMIP 2022)
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14 pages, 380 KiB  
Article
One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments
by Kexin Lv, Zheng Sun, Fan He, Xiaolin Huang and Jie Yang
Appl. Sci. 2022, 12(10), 5128; https://doi.org/10.3390/app12105128 - 19 May 2022
Viewed by 1363
Abstract
This paper focuses on the design of a distributed algorithm for generalized eigenvalue problems (GEPs) in one-shot communication. Since existing distributed methods for eigenvalue decomposition cannot be applied to GEP, a general one-shot distributed GEP framework is proposed. The theoretical analysis of the [...] Read more.
This paper focuses on the design of a distributed algorithm for generalized eigenvalue problems (GEPs) in one-shot communication. Since existing distributed methods for eigenvalue decomposition cannot be applied to GEP, a general one-shot distributed GEP framework is proposed. The theoretical analysis of the approximation error reveals its relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers. If the symmetric data covariance has repeated eigenvalues in GEP, e.g., in canonical component analysis, we further modify the method for better convergence and prove the necessity experimentally. Numerical experiments validate the effectiveness of the proposed algorithms both on synthetic and real-world datasets. Full article
(This article belongs to the Special Issue Selected Papers from the ICCAI and IMIP 2022)
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18 pages, 3373 KiB  
Article
Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
by Zhihao Liao, Neng Fan and Kai Xu
Appl. Sci. 2022, 12(9), 4735; https://doi.org/10.3390/app12094735 - 08 May 2022
Cited by 10 | Viewed by 4035
Abstract
Transformer complements convolutional neural network (CNN) has achieved better performance than improved CNN-based methods. Specially, Transformer is utilized to be combined with U-shaped structure, skip-connections, encoder, and even them all together. However, the intermediate supervision network based on the coarse-to-fine strategy has not [...] Read more.
Transformer complements convolutional neural network (CNN) has achieved better performance than improved CNN-based methods. Specially, Transformer is utilized to be combined with U-shaped structure, skip-connections, encoder, and even them all together. However, the intermediate supervision network based on the coarse-to-fine strategy has not been combined with Transformer to improve the generalization of CNN-based methods. In this paper, we propose Swin-PANet, which is applying a window-based self-attention mechanism by Swin Transformer in the intermediate supervision network, called prior attention network. A new enhanced attention block based on CCA is also proposed to aggregate the features from skip-connections and prior attention network, and further refine details of boundaries. Swin-PANet can address the dilemma that traditional Transformer network has poor interpretability in the process of attention calculation and Swin-PANet can insert its attention predictions into prior attention network for intermediate supervision learning which is humanly interpretable and controllable. Hence, the intermediate supervision network assisted by Swin Transformer provides better attention learning and interpretability in network for accurate and automatic medical image segmentation. The experimental results evaluate the effectiveness of Swin-PANet which outperforms state-of-the-art methods in some famous medical segmentation tasks including cell and skin lesion segmentation. Full article
(This article belongs to the Special Issue Selected Papers from the ICCAI and IMIP 2022)
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