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Applied Sciences
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Artificial Intelligence and Information Visualization in Social and Industrial Systems
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Artificial Intelligence and Information Visualization in Social and Industrial Systems

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 8257

Special Issue Editors

Prof. Dr. Fei Xiong

E-Mail Website
Guest Editor
School of Electronic Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: data mining; social network; artificial intelligence application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shirui Pan

E-Mail Website
Guest Editor
School of Information and Communication Technology, Griffith University, Camperdown, NSW 4215, Australia
Interests: graph, social and multimedia data; deep learning; machine learning
Dr. Hongshu Chen

E-Mail Website
Guest Editor
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
Interests: text mining and analytics; knowledge discovery and visualization; computing in management and social science
Special Issues, Collections and Topics in MDPI journals
Dr. Xuzhen Zhu

E-Mail Website
Guest Editor
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: propagation dynamics on complex networks; link prediction; recommender systems

Special Issue Information

Dear Colleagues,

With the breakthrough of artificial intelligence, information visualization driven by artificial intelligence is a high priority in social and industrial systems. This technology is being used in more and more practical scenarios. It can be used in emergency detection, social pattern awareness and influence maximization in social systems, and can also help intelligent decision and optimal resources allocation in industrial systems. Furthermore, information visualization uses visual perception channels to convert data into graphic expressions, supplemented by interactive means, so as to enhance people's cognitive ability regarding data. For instance, for social and news topics, in order to help users quickly understand news contents, topics can be extracted and displayed in a word cloud. Using this visualization technology, key information can be obtained quickly. In terms of equipment maintenance and other issues involving factory assembly lines, advanced machine learning models are used to evaluate the health status or remaining life of mechanical products, and relevant statistical data are presented to operators through visualization. In the future, artificial intelligence and information visualization will play a greater role in social and industrial systems. They provide convenient tools for government and enterprise decision-making, and bring convenience to the work and life of the public.

The goal of this Special Issue of Applied Sciences is to welcome contributions to theories and methods of artificial intelligence and information visualization, as well as their applications in all kinds of social and industrial systems. We encourage articles with multidisciplinary methods for social and industrial data mining.

Prof. Dr. Fei Xiong
Prof. Dr. Shirui Pan
Dr. Hongshu Chen
Dr. Xuzhen Zhu
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

  • network representation learning
  • social network mining
  • topic discovery and visualization
  • web image processing
  • knowledge graph and its applications
  • deep learning in social computing and industrial mining
  • decision making based on machine learning
  • fault diagnosis driven by artificial intelligence

Published Papers (5 papers)

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Research

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16 pages, 7524 KiB  
Article
Vehicle Trajectory Prediction Based on Graph Convolutional Networks in Connected Vehicle Environment
by Jian Shi, Dongxian Sun and Baicang Guo
Appl. Sci. 2023, 13(24), 13192; https://doi.org/10.3390/app132413192 - 12 Dec 2023
Viewed by 852
Abstract
Vehicle trajectory prediction is an important research basis for the decision making and path planning of the intelligent and connected vehicle. In the connected vehicle environment, vehicles share information and drive cooperatively, and the intelligent and connected vehicles are able to obtain more [...] Read more.
Vehicle trajectory prediction is an important research basis for the decision making and path planning of the intelligent and connected vehicle. In the connected vehicle environment, vehicles share information and drive cooperatively, and the intelligent and connected vehicles are able to obtain more accurate and rich perception information, which provides a data basis for accurate prediction of vehicle trajectories. However, attaining accurate and effective vehicle trajectory predictions poses technical challenges due to insufficient extraction of vehicular spatial–temporal interaction features. In this paper, we propose a vehicle trajectory prediction model based on graph convolutional neural network (GCN) in a connected vehicle environment. Specifically, using the driving scene information obtained by the intelligent and connected vehicle, the spatial graph and temporal graph are constructed based on the spatial interaction coefficient (SIC) and self-attention mechanism, respectively. Then, the graph data are entered into the interaction extraction module, and the spatial interaction features and temporal interaction features are extracted separately using the graph convolutional networks, which are fused to obtain the spatial–temporal interaction information. Finally, the interaction features are learned based on the convolutional neural networks to output the future trajectory information of all vehicles in the scene by one forward operation rather than a step-by-step process. The ablation experiment results show that the method proposed in this study to model the spatiotemporal interaction among vehicles based on SIC and self-attention mechanism reduces the prediction error by 5% and 12%, respectively. The results from the model comparison experiment show that the proposed method engenders an 8% improvement in prediction accuracy over the state-of-the-art solution, providing technical and theoretical support for trajectory prediction research of intelligent and connected vehicles. Full article
(This article belongs to the Special Issue Artificial Intelligence and Information Visualization in Social and Industrial Systems)
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11 pages, 1115 KiB  
Article
Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
by Songtao Cai, Qicheng Ma, Yupeng Hou and Guangping Zeng
Appl. Sci. 2023, 13(22), 12202; https://doi.org/10.3390/app132212202 - 10 Nov 2023
Viewed by 695
Abstract
Intention detection and slot filling are two major subtasks in building a spoken language understanding (SLU) system. These two tasks are closely related to each other, and information from one will influence the other, establishing a bidirectional contributory relationship. Existing studies have typically [...] Read more.
Intention detection and slot filling are two major subtasks in building a spoken language understanding (SLU) system. These two tasks are closely related to each other, and information from one will influence the other, establishing a bidirectional contributory relationship. Existing studies have typically modeled the two-way connection between these two tasks simultaneously in a unified framework. However, these studies have merely contributed to the research direction of fully using the correlations between feature information of the two tasks, without sufficient focusing on and utilizing native textual semantics. In this article, we propose a semantic guidance (SG) framework, enabling enhancing the understanding of textual semantics by dynamically gating the information from both tasks to acquire semantic features, ultimately leading to higher joint task accuracy. Experimental results on two widely used public datasets show that our model achieves state-of-the-art performance. Full article
(This article belongs to the Special Issue Artificial Intelligence and Information Visualization in Social and Industrial Systems)
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16 pages, 3855 KiB  
Article
Prediction of Inbound and Outbound Passenger Flow in Urban Rail Transit Based on Spatio-Temporal Attention Residual Network
by Jun Yang, Xueru Dong, Huifan Yang, Xiao Han, Yan Wang and Jiayue Chen
Appl. Sci. 2023, 13(18), 10266; https://doi.org/10.3390/app131810266 - 13 Sep 2023
Cited by 2 | Viewed by 927
Abstract
Passenger flow prediction is a critical approach to ensure the effective functioning of urban rail transit. However, there are few studies that combine multiple influencing factors for short-term passenger flow prediction. It is also a challenge to accurately predict passenger flow at all [...] Read more.
Passenger flow prediction is a critical approach to ensure the effective functioning of urban rail transit. However, there are few studies that combine multiple influencing factors for short-term passenger flow prediction. It is also a challenge to accurately predict passenger flow at all stations in the line at the same time. To overcome the above limitations, a deep learning-based method named ST-RANet is proposed, which consists of three spatio-temporal modules and one external module. The model is capable of predicting inbound and outbound passenger flow for all stations within the network simultaneously. We model the spatio-temporal data in terms of three temporal characteristics, including closeness, period, and trend. For each characteristic, we construct a spatio-temporal module that innovatively integrates the attention mechanisms into the middle of residual units and convolutional neural networks (CNNs) to extract and learn spatio-temporal features. Subsequently, the results of the three modules are integrated using a parameter matrix method, which allows for dynamic aggregation based on data. The integration results are further combined with external factors, such as holidays and meteorological information, to obtain passenger flow prediction values for each station. The proposed model is validated using real data from Beijing Subway, and optimized parameters are applied for 30-min granularity passenger flow predictions. Comparing the performance against 5 baseline models and verifying with data from multiple lines, the results indicate that the proposed ST-RANet model shows the best results. It is demonstrated that the method proposed in this paper has high prediction accuracy and good applicability. Full article
(This article belongs to the Special Issue Artificial Intelligence and Information Visualization in Social and Industrial Systems)
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15 pages, 2527 KiB  
Article
Modeling Graph Neural Networks and Dynamic Role Sorting for Argument Extraction in Documents
by Qingchuan Zhang, Hongxi Chen, Yuanyuan Cai, Wei Dong and Peng Liu
Appl. Sci. 2023, 13(16), 9257; https://doi.org/10.3390/app13169257 - 15 Aug 2023
Cited by 1 | Viewed by 762
Abstract
The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event arguments while disregarding the dynamic adjusting of the order according [...] Read more.
The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences. The second challenge is using predefined orders to extract event arguments while disregarding the dynamic adjusting of the order according to the importance of argument roles. To address these issues, we propose a model based on graph neural networks which realizes the semantic interaction among documents, sentences, and entities. Additionally, our model adopts a dynamic argument detection strategy, extracting arguments depending on their number in correspondence with each role. The experimental results confirm the outperformance of our model, which surpasses previous methods by 7% and 1.9% in terms of an F1 score. Full article
(This article belongs to the Special Issue Artificial Intelligence and Information Visualization in Social and Industrial Systems)
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Review

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31 pages, 2153 KiB  
Review
A Comprehensive Survey of Recommender Systems Based on Deep Learning
by Hongde Zhou, Fei Xiong and Hongshu Chen
Appl. Sci. 2023, 13(20), 11378; https://doi.org/10.3390/app132011378 - 17 Oct 2023
Cited by 4 | Viewed by 3770
Abstract
With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still [...] Read more.
With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic. Full article
(This article belongs to the Special Issue Artificial Intelligence and Information Visualization in Social and Industrial Systems)
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