
Jing Liang
Articles
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Dec 28, 2024 |
nature.com | Jing Liang
This study proposes a novel artificial intelligence (AI)-assisted design model that combines Variational Autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and efficiency in cultural and creative product design. By introducing AI-driven decision support, the model streamlines the design workflow and significantly improves design quality. The study establishes a comprehensive framework and applies the model to four distinct design tasks, with extensive experiments validating its performance. Key factors, including creativity, cultural adaptability, and practical application, are evaluated through structured surveys and expert feedback. The results reveal that the VAE + RL model surpasses alternative approaches across multiple criteria. Highlights include a user satisfaction rate of 95%, a Structural Similarity Index (SSIM) score of 0.92, model accuracy of 93%, and a loss reduction to 0.07. These findings confirm the model’s superiority in generating high-quality designs and achieving high user satisfaction. Additionally, the model exhibits strong generalization capabilities and operational efficiency, offering valuable insights and data support for future advancements in cultural product design technology.
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Nov 17, 2024 |
dx.doi.org | Xinyue Gu |Jingjing Zhang |Jing Liang |Xinyu Liu
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Nov 15, 2024 |
mdpi.com | Jianyu Wang |Jing Liang |Chao Wang |Wanwei Tang
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
A Multicenter Cohort Study on DNA Methylation for Endometrial Cancer Detection in Cervical Scrapings
Nov 2, 2024 |
onlinelibrary.wiley.com | Xiao Ma |Xiaojun Chen |Jing Liang
1 Introduction Endometrial cancer (EC) is a common gynecological malignancy originating from endometrium. It can be broadly categorized into two types: type I (endometrioid), affecting approximately 80% of patients, and type II (non-endometrioid), affecting the remaining 20% [1]. Abnormal uterine bleeding (AUB) is a common symptom of EC.
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Aug 14, 2024 |
mdpi.com | Jing Liang
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