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Applied Sciences
Special Issues
Collaborative Learning and Optimization Theory and Its Applications
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Collaborative Learning and Optimization Theory and Its Applications

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

Deadline for manuscript submissions: 10 August 2024 | Viewed by 2506

Special Issue Editors

Dr. Yue Wu

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Guest Editor
Department of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vision; image processing and pattern recognition; theory and applications of computational intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Mingyang Zhang

E-Mail Website1 Website2
Guest Editor
School of Electronic Engineering, Key Laboratory of Collaborative Intelligence Systems of Ministry of Education, Xidian University, Xi’an 710071, China
Interests: hyperspectral remote sensing; hyperspectral unmixing; remote sensing change detectionl; remote sensing image classification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qiguang Miao

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vision; machine learning; high performance calculation; big data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Qin

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Guest Editor
Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Interests: machine learning; evolutionary computation; computer vision; services computing; pervasive computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on "Collaborative Learning and Optimization Theory and Its Application" aims to explore the synergistic relationship between collaborative learning and optimization theory, and their applications in various fields. Collaborative learning refers to the process of multiple agents or entities working together to solve a common problem or achieve a common goal. Optimization theory, on the other hand, focuses on finding the best solution or achieving optimal performance in a given context.

This Special Issue welcomes research papers that investigate the theoretical foundations, methodologies, and applications of collaborative learning and optimization. The scope of this issue covers a wide range of topics, including but not limited to:

  • Artificial intelligence;
  • Deep learning;
  • Fuzzy logic;
  • Evolutionary computation;
  • Machine learning;
  • Reinforcement learning;
  • Collaborative optimization algorithms;
  • Collaborative learning models and frameworks;
  • Collaborative optimization in real-world applications.

Researchers and practitioners are invited to contribute their original research papers, case studies, and review articles that provide insights into the latest advancements, challenges, and potential applications of collaborative learning and optimization theory. This Special Issue aims to foster interdisciplinary collaborations and knowledge exchange, promoting the development and deployment of innovative solutions in this emerging field.

Dr. Yue Wu
Dr. Mingyang Zhang
Prof. Dr. Qiguang Miao
Prof. Dr. Kai Qin
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

  • collaborative learning
  • collaborative optimization
  • artificial intelligence
  • deep learning
  • evolutionary computation
  • fuzzy logic and systems

Published Papers (3 papers)

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Research

24 pages, 9170 KiB  
Article
Model for Determining the Psycho-Emotional State of a Person Based on Multimodal Data Analysis
by Nataliya Shakhovska, Oleh Zherebetskyi and Serhii Lupenko
Appl. Sci. 2024, 14(5), 1920; https://doi.org/10.3390/app14051920 - 26 Feb 2024
Viewed by 525
Abstract
The paper aims to develop an information system for human emotion recognition in streaming data obtained from a PC or smartphone camera, using different methods of modality merging (image, sound and text). The objects of research are the facial expressions, the emotional color [...] Read more.
The paper aims to develop an information system for human emotion recognition in streaming data obtained from a PC or smartphone camera, using different methods of modality merging (image, sound and text). The objects of research are the facial expressions, the emotional color of the tone of a conversation and the text transmitted by a person. The paper proposes different neural network structures for emotion recognition based on unimodal flows and models for the margin of the multimodal data. The analysis determined that the best classification accuracy is obtained for systems with data fusion after processing each channel separately and obtaining individual characteristics. The final analysis of the model based on data from a camera and microphone or recording or broadcast of the screen, which were received in the “live” mode, gave a clear understanding that the quality of the obtained results is highly dependent on the quality of the data preparation and labeling. This is directly related to the fact that the data on which the neural network is trained is highly qualified. The neural network with combined data on the penultimate layer allows a psycho-emotional state recognition accuracy of 0.90 to be obtained. The spatial distribution of emotion analysis was also analyzed for each data modality. The model with late fusion of multimodal data demonstrated the best recognition accuracy. Full article
(This article belongs to the Special Issue Collaborative Learning and Optimization Theory and Its Applications)
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21 pages, 5790 KiB  
Article
PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection
by Baobao Liu, Heying Wang, Zifan Cao, Yu Wang, Lu Tao, Jingjing Yang and Kaibing Zhang
Appl. Sci. 2024, 14(2), 938; https://doi.org/10.3390/app14020938 - 22 Jan 2024
Cited by 2 | Viewed by 933
Abstract
Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a detection system. Firstly, we have improved YOLOv7 [...] Read more.
Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a detection system. Firstly, we have improved YOLOv7 by integrating new convolution operators into the Extended-Efficient Layer Aggregation Network for optimized feature extraction, reducing computations while capturing spatial features effectively. Secondly, to enhance the performance of the feature fusion network, we use Receptive Field Block as the feature pyramid of YOLOv7 and introduce Content-Aware ReAssembly of FEatures as upsampling operators for PRC-Light YOLO. By generating real-time adaptive convolution kernels, this module extends the receptive field, thereby gathering vital information from contexts with richer content. To further optimize the efficiency of model training, we apply the HardSwish activation function. Additionally, the bounding box loss function adopts the Wise-IOU v3, which incorporates a dynamic non-monotonic focusing mechanism that mitigates adverse gradients from low-quality instances. Finally, in order to enhance the PRC-Light YOLO model’s generalization ability, we apply data augmentation techniques to the fabric dataset. In comparison to the YOLOv7 model, multiple experiments indicate that our proposed fabric defect detection model exhibits a decrease of 18.03% in model parameters and 20.53% in computational load. At the same time, it has a notable 7.6% improvement in mAP. Full article
(This article belongs to the Special Issue Collaborative Learning and Optimization Theory and Its Applications)
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17 pages, 15473 KiB  
Article
A High-Quality Hybrid Mapping Model Based on Averaging Dense Sampling Parameters
by Fanxiao Yi, Weishi Li, Mengjie Huang, Yingchang Du and Lei Ye
Appl. Sci. 2024, 14(1), 335; https://doi.org/10.3390/app14010335 - 29 Dec 2023
Viewed by 593
Abstract
Navigation map generation based on remote sensing images is crucial in fields such as autonomous driving and geographic surveying. Style transfer is an effective method for obtaining a navigation map of the current environment. However, there is lack of robustness of the map-style [...] Read more.
Navigation map generation based on remote sensing images is crucial in fields such as autonomous driving and geographic surveying. Style transfer is an effective method for obtaining a navigation map of the current environment. However, there is lack of robustness of the map-style transfer model, resulting in unsatisfactory quality of the generated navigation maps. To address these challenges, we average the parameters of generators sampled from different iterations with a dense sampling strategy in the Generative Adversarial Network (CycleGAN). The results demonstrate that the training efficiency of our method on the MNIST and generation quality on the Google Map dataset are significantly superior to traditional style transfer methods. Moreover, it performs well in multi-environment hybrid mapping. Our method improves the generalization ability of the model and converts existing navigation maps to other styles of maps precisely. It can better adapt to different types of urban layout and road planning, bringing innovative solutions for traffic management and navigation systems. Full article
(This article belongs to the Special Issue Collaborative Learning and Optimization Theory and Its Applications)
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