Integration of Convolutional Neural Networks and Human-Computer Interaction for Advancing Art Design

Fan Wang
Additional contact information
Fan Wang: Yulin Normal University, China

International Journal of Web Services Research (IJWSR), 2025, vol. 22, issue 1, 1-26

Abstract: This paper explores the innovative application of convolutional neural networks (CNNs) and human-computer interaction (HCI) in art design, facilitated by advanced web-services technology. The integration of these technologies aims to streamline the creative process, making it more accessible and cost-effective for artists. By leveraging deep learning algorithms for style transfer and visual content processing, this research demonstrates how CNNs can transform user inputs into unique artworks. Furthermore, HCI principles ensure a seamless interface between users and digital tools, enhancing usability and efficiency. This study also addresses the challenges associated with artificial intelligence in art creation, including ethical considerations and the preservation of cultural heritage. Through practical implementations and case studies, the paper highlights the potential for web-based platforms to democratize art creation while maintaining artistic integrity.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWSR.382378 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:22:y:2025:i:1:p:1-26

Access Statistics for this article

International Journal of Web Services Research (IJWSR) is currently edited by Liang-Jie Zhang

More articles in International Journal of Web Services Research (IJWSR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().