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Applied Sciences
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Artificial Neural Network Applications in Pattern Recognition
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Artificial Neural Network Applications in Pattern Recognition

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 (31 October 2023) | Viewed by 2378

Special Issue Editors

Dr. Hong Fu

E-Mail Website
Guest Editor
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China
Interests: computer vision algorithms; artificial intelligence in healthcare and education; eye tracking and motion detection
Special Issues, Collections and Topics in MDPI journals
Dr. Zhen Liang

E-Mail Website
Guest Editor
School of Biomedical Engineering, Shenzhen University, Shenzhen 518052, China
Interests: brain encoding and decoding system; affective computing; affective brain-computer interface
Dr. Tse-Tin Chan

E-Mail Website
Guest Editor
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China
Interests: wireless communications; internet of things; machine learning in communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neural networks, also known as artificial neural networks (ANNs), have been a hot research topic in the field of artificial intelligence for a number of years. Artificial neural networks (ANNs) began with their streamlined application in many areas. In the last ten years, artificial neural networks have made enabled great progress in pattern recognition, intelligent robots, automatic control, forecast estimates, biology, medicine, economy, and other fields. They have successfully solved many practical problems with good intelligence features.

As mentioned above, significant progress has been made in addressing the challenges of artificial neural networks applied to pattern recognition. The main research areas in pattern recognition are image processing, computer vision, speech and language information processing, brain network group, brain-like intelligence, etc. However, there are still several issues that remain to be addressed.

In this Special Issue of Applied Sciences, we invite contributions that cover artificial neural network (ANN) applications and improvements in the following aspects:

pattern recognition; intelligent robot; automatic control; forecast estimates; biology; medicine; education; healthcare; economy; image processing; computer vision; speech and language information processing; brain network group; and brain-like intelligence.

Dr. Hong Fu
Dr. Zhen Liang
Dr. Tse-Tin Chan
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

  • pattern recognition
  • intelligent robots
  • automatic control
  • forecast estimates
  • biology
  • medicine
  • education
  • healthcare
  • economy
  • image processing
  • computer vision
  • speech and language information processing
  • brain network group
  • brain-like intelligence

Published Papers (2 papers)

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Research

18 pages, 645 KiB  
Article
NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
by Volkan Altuntas
Appl. Sci. 2024, 14(2), 775; https://doi.org/10.3390/app14020775 - 16 Jan 2024
Viewed by 774
Abstract
Network node embedding captures structural and relational information of nodes in the network and allows for us to use machine learning algorithms for various prediction tasks on network data that have an inherently complex and disordered structure. Network node embedding should preserve as [...] Read more.
Network node embedding captures structural and relational information of nodes in the network and allows for us to use machine learning algorithms for various prediction tasks on network data that have an inherently complex and disordered structure. Network node embedding should preserve as much information as possible about important network properties where information is stored, such as network structure and node properties, while representing nodes as numerical vectors in a lower-dimensional space than the original higher dimensional space. Superior node embedding algorithms are a powerful tool for machine learning with effective and efficient node representation. Recent research in representation learning has led to significant advances in automating features through unsupervised learning, inspired by advances in natural language processing. Here, we seek to improve the representation quality of node embeddings with a new node vectorization technique that uses network analysis to overcome network-based information loss. In this study, we introduce the NodeVector algorithm, which combines network analysis and neural networks to transfer information from the target network to node embedding. As a proof of concept, our experiments performed on different categories of network datasets showed that our method achieves better results than its competitors for target networks. This is the first study to produce node representation by unsupervised learning using the combination of network analysis and neural networks to consider network data structure. Based on experimental results, the use of network analysis, complex initial node representation, balanced negative sampling, and neural networks has a positive effect on the representation quality of network node embedding. Full article
(This article belongs to the Special Issue Artificial Neural Network Applications in Pattern Recognition)
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19 pages, 4882 KiB  
Article
No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
by Fan Chen, Hong Fu, Hengyong Yu and Ying Chu
Appl. Sci. 2023, 13(11), 6802; https://doi.org/10.3390/app13116802 - 3 Jun 2023
Viewed by 1087
Abstract
When image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information about the image, utilizes contextual information to restore the original image [...] Read more.
When image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information about the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces a structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher-quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. In order to facilitate the model’s ability to extract both global and local features, we introduce a multi-scale feature fusion module. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases. Full article
(This article belongs to the Special Issue Artificial Neural Network Applications in Pattern Recognition)
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