Huang Zhenzhen, Xu Zhiming, Wang Xiangyu, Xu Zhaoyi
School of Economics and Trade, Hunan University, Changsha, 410079, China.
Business School, Imperial College London, London, SW7 2BX, UK.
Heliyon. 2024 Oct 11;10(20):e39286. doi: 10.1016/j.heliyon.2024.e39286. eCollection 2024 Oct 30.
This paper aims to provide new avenues for innovation in credit governance in the digital economy to provide more reliable credit evaluation solutions for financial, commercial, and social interactions. This paper integrates the potential value of Internet of Things (IoT) technology in credit governance and proposes a credit governance method that utilizes IoT data and an improved Long Short-Term Memory model. The proposed model introduces an adaptive mechanism to monitor changes in data in real-time and automatically adjust network parameters to improve the model performance. Experimental validation is conducted on the University of California Irvine dataset. It is found that the proposed model performs well in terms of accuracy, F1 value, and Area Under the Curve value, with values of 0.9, 0.91, and 0.94, respectively. This indicates that the proposed model has higher classification accuracy and performance in classification tasks while maintaining efficient training time. This indicates that the presented method has potential application prospects in credit governance research.
本文旨在为数字经济中的信用治理创新提供新途径，为金融、商业和社会互动提供更可靠的信用评估解决方案。本文整合了物联网（IoT）技术在信用治理中的潜在价值，并提出了一种利用物联网数据和改进的长短期记忆模型的信用治理方法。所提出的模型引入了一种自适应机制，以实时监测数据变化并自动调整网络参数，从而提高模型性能。在加州大学欧文分校数据集上进行了实验验证。结果发现，所提出的模型在准确率、F1值和曲线下面积值方面表现良好，分别为0.9、0.91和0.94。这表明所提出的模型在分类任务中具有更高的分类准确率和性能，同时保持了高效的训练时间。这表明所提出的方法在信用治理研究中具有潜在的应用前景。
