مشخصات مقاله | |
ترجمه عنوان 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 |
انتشار | مقاله سال 2022 |
تعداد صفحات مقاله انگلیسی | 11 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
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 |
فرضیه | ندارد |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی فناوری اطلاعات، مهندسی صنایع |
گرایش های مرتبط | اینترنت و شبکه های گسترده، شبکه های کامپیوتری، لجستیک و زنجیره تامین |
نوع ارائه مقاله |
ژورنال |
مجله | ارتباطات کامپیوتری – Computer Communications |
دانشگاه | School of Business Management, Zhuhai College of Science and Technology, Zhuhai, China |
کلمات کلیدی | 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 |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.comcom.2021.10.026 |
کد محصول | E15920 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
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 متن مقاله: |
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]. |