Artificial Intelligence and Big Data Processing: Transforming Industrial Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 714

Special Issue Editors


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Guest Editor
School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China
Interests: cloud computing; big data; deep learning; IoT; wireless networks
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China
Interests: cloud computing; big data; deep learning; IoT; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) and Big Data is reshaping industrial landscapes, driving innovation, and optimizing processes across multiple sectors. This Special Issue aims to explore cutting-edge developments in the application of AI and Big Data technologies to solve complex industrial challenges. Its primary focus is on enhancing operational efficiency, improving decision-making processes, and fostering sustainable practices through advanced analytics and intelligent automation. By leveraging AI algorithms and Big Data analytics, industries can achieve significant improvements in resource management, productivity, and cost reduction. Additionally, this Special Issue will examine real-world case studies and novel methodologies that demonstrate the transformative potential of these technologies in areas,-- such as predictive maintenance, where AI can foresee equipment failures and reduce downtime; supply chain optimization, which enhances logistics and inventory management; and personalized healthcare solutions that tailor treatments to individual patient needs. Furthermore, this Special Issue will delve into the implications of these technologies for workforce development, examining how AI and Big Data can augment human capabilities and create new job opportunities.

By highlighting interdisciplinary approaches, including collaborations between academia and industry, this Special Issue seeks to provide insights into the future trajectory of AI and Big Data in various industrial contexts. Through these contributions, we aim to foster a deeper understanding of the synergies between AI, Big Data, and industrial applications, ultimately guiding future research and practical implementations. The scope of this Special Issue includes, but is not limited to, the following topics:

  • AI algorithms for predictive maintenance and process optimization in manufacturing systems.
  • Big Data analytics for enhancing process accuracy and efficiency in industrial automation.
  • Real-time process optimization and quality control using AI and Big Data integration.
  • Leveraging AI and Big Data for supply chain optimization and logistics efficiency.
  • AI-driven clinical decision support systems in healthcare for improved diagnostics and treatment strategies.
  • Integrating AI with Big Data for remote monitoring and telehealth applications in patient management.
  • Utilizing Big Data to enhance predictive analytics in manufacturing and operational processes.
  • AI technologies for improving energy efficiency and sustainability in industrial applications.
  • Big Data frameworks for real-time analysis in smart manufacturing environments.
  • Machine learning applications for optimizing resource allocation in healthcare and industrial settings.

Prof. Dr. Ming Liu
Dr. Haigang Gong
Guest Editors

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Keywords

  • Artificial Intelligence
  • big data
  • smart manufacturing
  • industrial automation
  • data-driven decision-making

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Published Papers (1 paper)

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Research

17 pages, 4741 KiB  
Article
Liquid Level Detection of Polytetrafluoroethylene Emulsion Rotary Vibrating Screen Device Based on TransResNet
by Wenwu Liu, Xianghui Fan, Meng Liu, Hang Li, Jiang Du and Nianbo Liu
Electronics 2025, 14(5), 913; https://doi.org/10.3390/electronics14050913 - 25 Feb 2025
Viewed by 424
Abstract
The precise real-time detection of polytetrafluoroethylene (PTFE) emulsion rotary vibration sieve levels is critical for improving production efficiency, ensuring product quality, and safeguarding personnel safety. This research presents a deep-learning-oriented video surveillance model for the intelligent level detection of vibrating screens, waste drums, [...] Read more.
The precise real-time detection of polytetrafluoroethylene (PTFE) emulsion rotary vibration sieve levels is critical for improving production efficiency, ensuring product quality, and safeguarding personnel safety. This research presents a deep-learning-oriented video surveillance model for the intelligent level detection of vibrating screens, waste drums, and emulsion outlets, effectively addressing the limitations of traditional methods. With the introduction of TransResNet, which combines Vision Transformer (ViT) with ResNet, we can utilize the advantages of both approaches. ViT has excellent global information capture capability for processing image features, while ResNet excels in local feature extraction. The combined model effectively recognizes level changes in complex backgrounds, enhancing overall detection performance. During model training, synthetic data generation is used to alleviate the marker scarcity problem and generate synthetic images under different liquid level states to further enrich the training dataset, solve the issue of unequal data distribution, and enhance the model’s capacity to generalize. In order to validate the efficacy of our proposed model, we carried out a performance test with real-world data obtained from a material production site. The test results show that the model achieves 96%, 99%, and 99% accuracy at three test points, respectively: the vibrating screen, waste drum, and emulsion. These results not only prove the efficiency of the model but also highlight its significant value in practical applications. Full article
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