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Selected Papers from CCF 39th China Computer Application Conference (CCF NCCA 2024)

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

Deadline for manuscript submissions: 30 December 2024 | Viewed by 4137

Special Issue Editor

Prof. Dr. Guangjie Han

E-Mail Website
Guest Editor
Information Department, Hohai University, Changzhou 213022, China
Interests: Internet of Things; machine learning and artificial intelligence; smart ocean; industrial internet; security and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue comprises selected papers presented at the 39th China Computer Application Conference (CCF NCCA 2024), organized by the China Computer Federation (CCF). The conference provided a platform for researchers, practitioners, and industry experts to exchange insights and innovations in the field of computer applications. The selected papers cover a wide range of topics, including artificial intelligence, data mining, computer vision, cybersecurity, and human–computer interactions. These contributions reflect the latest advancements and challenges in computer application research and highlight the diverse perspectives shaping the future of technology.

Prof. Dr. Guangjie Han
Guest Editor

Manuscript Submission Information

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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

  • artificial intelligence
  • data mining
  • computer vision
  • cybersecurity
  • human–computer interactions
  • natural language processing (NLP)

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Published Papers (2 papers)

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Research

18 pages, 4262 KiB  
Article
Cyclic Consistent Image Style Transformation: From Model to System
by Jun Peng, Kaiyi Chen, Yuqing Gong, Tianxiang Zhang and Baohua Su
Appl. Sci. 2024, 14(17), 7637; https://doi.org/10.3390/app14177637 - 29 Aug 2024
Viewed by 898
Abstract
Generative Adversarial Networks (GANs) have achieved remarkable success in various tasks, including image generation, editing, and reconstruction, as well as in unsupervised and representation learning. Despite their impressive capabilities, GANs are often plagued by challenges such as unstable training dynamics and limitations in [...] Read more.
Generative Adversarial Networks (GANs) have achieved remarkable success in various tasks, including image generation, editing, and reconstruction, as well as in unsupervised and representation learning. Despite their impressive capabilities, GANs are often plagued by challenges such as unstable training dynamics and limitations in generating complex patterns. To address these challenges, we propose a novel image style transfer method, named C3GAN, which leverages CycleGAN architecture to achieve consistent and stable transformation of image style. In this context, “image style” refers to the distinct visual characteristics or artistic elements, such as the color schemes, textures, and brushstrokes that define the overall appearance of an image. Our method incorporates cyclic consistency, ensuring that the style transformation remains coherent and visually appealing, thus enhancing the training stability and overcoming the generative limitations of traditional GAN models. Additionally, we have developed a robust and efficient image style transfer system by integrating Flask for web development and MySQL for database management. Our system demonstrates superior performance in transferring complex styles compared to existing model-based approaches. This paper presents the development of a comprehensive image style transfer system based on our advanced C3GAN model, effectively addressing the challenges of GANs and expanding application potential in domains such as artistic creation and cinematic special effects. Full article
(This article belongs to the Special Issue Selected Papers from CCF 39th China Computer Application Conference (CCF NCCA 2024))
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21 pages, 3662 KiB  
Article
Empirical Research on AI Technology-Supported Precision Teaching in High School Science Subjects
by Miaomiao Hao, Yi Wang and Jun Peng
Appl. Sci. 2024, 14(17), 7544; https://doi.org/10.3390/app14177544 - 26 Aug 2024
Viewed by 2353
Abstract
The empowerment of educational reform and innovation through AI technology has become a topic of increasing interest in the field of education. The advent of AI technology has made comprehensive and in-depth teaching evaluation possible, serving as a significant driving force for efficient [...] Read more.
The empowerment of educational reform and innovation through AI technology has become a topic of increasing interest in the field of education. The advent of AI technology has made comprehensive and in-depth teaching evaluation possible, serving as a significant driving force for efficient and precise teaching. There were few empirical studies on the application of high-quality precision teaching models in the field of compulsory education, and the learning difficulty of technology and the teaching burden on teachers have become significant factors hindering the use of technology to support education. This study analyzed teaching models from the perspectives of teachers’ teaching burdens and students’ learning obstacles, and was committed to relying on intelligent technology to construct a new precision teaching model, an educational diagnosis–feedback–intervention path that covered the entire teaching process, from the dimensions of teacher behavior, student behavior, and parent behavior, aiming to assist teachers in efficient teaching and students in personalized learning. This study was conducted with nine science classes, including about 540 people in the second year of high school at a Middle School in China; six classes were the intervention groups while the last three classes were control groups, and a survey of 19 teachers from the intervention classes was carried out. The results showed that this model can significantly improve students’ academic performance in science subjects, especially in mathematics and chemistry. It has increased the proportion of high-achieving students, reduced the proportion of low-achieving students, stimulated students’ self-directed learning ability, cultivated a positive attitude towards science learning, and explained the key points of using a precision teaching model in different disciplines. It has achieved a deep integration of education and technology, helping to increase the efficiency and reduce the burden of teaching. Full article
(This article belongs to the Special Issue Selected Papers from CCF 39th China Computer Application Conference (CCF NCCA 2024))
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