مقاله انگلیسی مدل ریسک زنجیره تامین مالی شرکت های اینترنت اشیا
ترجمه نشده

مقاله انگلیسی مدل ریسک زنجیره تامین مالی شرکت های اینترنت اشیا

عنوان فارسی مقاله: مدل ریسک زنجیره تامین مالی شرکت های اینترنت اشیا: تحقیقی مبتنی بر شبکه عصبی کانولوشنال
عنوان انگلیسی مقاله: Risk model of financial supply chain of Internet of Things enterprises: A research based on convolutional neural network
مجله/کنفرانس: ارتباطات کامپیوتری - Computer Communications
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات، مهندسی صنایع
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده، شبکه های کامپیوتری، لجستیک و زنجیره تامین
کلمات کلیدی فارسی: شرکت اینترنت اشیا، ریسک زنجیره تامین، الگوریتم پیچیدگی، مدل ریسک، داده های غیر عادی
کلمات کلیدی انگلیسی: Enterprise of the Internet of Things - Supply chain risk - Convolution algorithm - Risk model - Abnormal data
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.comcom.2021.10.026
دانشگاه: School of Business Management, Zhuhai College of Science and Technology, Zhuhai, China
صفحات مقاله انگلیسی: 11
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2022
ایمپکت فاکتور: 4.084 در سال 2020
شاخص H_index: 105 در سال 2021
شاخص SJR: 0.627 در سال 2020
شناسه ISSN: 0140-3664
شاخص Quartile (چارک): Q1 در سال 2020
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
آیا این مقاله فرضیه دارد: ندارد
کد محصول: E15920
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

Keywords

1. Introduction

2. Related work

3. Enterprise financial supply chain model based on Internet of things

4. Evolution risk anomaly analysis

5. Simulation experiment analysis

6. Conclusion

Declaration of Competing Interest

Acknowledgments

References

بخشی از مقاله (انگلیسی)

Abstract

The emergence of the financial supply chain provides assistance for small, medium and micro enterprises in the supply chain through a secured credit model based on real trade. Moreover, in the multi-level structure of the financial supply chain of the Internet of Things enterprise, there are information barriers and information islands. Besides, data is often not transmitted smoothly, and the intermediate offline process is complicated. What is worse, the efficiency is low, and the verification cost is high. Therefore, based on supply chain finance, an evolutionary risk model is constructed in this paper. Firstly, the income matrix of the regulatory risk model is established, and the convolutional neural network used will pool the training data to the maximum and set the local corresponding normalization layer. With the help of the evolutionary risk theory, the dynamic equation of the financial supply chain is obtained, forming the dynamic path and abnormal model of strategy selection. Then, a compact pattern tree is added to the knowledge granularity method to mine data anomalies. Finally, an experimental platform is built to verify the effectiveness of the method proposed in this paper, and experiments are performed on the accuracy of model evolution conditions, abnormal data identification, and abnormal numerical examples. The experimental results prove that the algorithm in this paper is consistent with the set parameters, and the effect is significantly higher than other comparison methods.

 

1. Introduction

As the development scale of the Internet of Things expands, the economic development of Internet of Things enterprises face many challenges like the great downward pressure on the economy. The development of corporate financial supply chains is becoming more and more important. However, there are many problems that need to be solved in the traditional corporate financial supply chain. For example, the high-quality credit of core enterprises has not been fully utilized, causing a lot of financial expenses. Small and medium-sized enterprises are of insufficient credit qualifications, so that the financing needs of enterprises will be large. Meanwhile, the bargaining power of the industry chain is weak, with many sales on credit. In addition, enterprises cannot maintain the continuous growth of the financing scale only with the help of their own funds. Moreover, the traditional credit model cannot meet the requirements of upstream and downstream enterprises in the supply chain [1].