Influences of mobile edge computing-based service preloading on the early-warning of financial risks

Zeng, Hui

doi:10.1007/s11227-022-04329-2

Cited by 23 publications

(10 citation statements)

References 27 publications

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“…An early warning model for financial risk prediction of the enterprise based on MEC is proposed in [ 70 ]. The authors used an optimized BPNN with an edge service preloading optimization model which is applied based on the information obtained about the geographical information related to points of interest and BPNN.…”

Section: Artificial Intelligence In Edge-based Iot Applications: Lite...mentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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Section: Artificial Intelligence In Edge-based Iot Applications: Lite...mentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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“…That is to evaluate and calculate the initial cost of low-carbon investment and the economic benefits it will bring to the enterprise in the future. In order to effectively warn the investment risks arising from blind low-carbon investment, enterprises need to carry out targeted management of carbon (Zeng, 2022). The concept of low-carbon development involves several interrelated tasks: improving energy efficiency, using renewable energy, etc.…”

Section: Related Workmentioning

confidence: 99%

Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

Wan

Yu²

2023

Front. Energy Res.

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The concept of low-carbon economic development has led to changes in the business environment and financial environment of enterprises, leading to increased financial risks faced by enterprises. How to help enterprises better warn, prevent and control financial risks from the perspective of low-carbon economy has become a hot issue worth studying. Based on this, this paper is based on the perspective of low carbon economy, on the basis of analyzing the financing risk, investment risk, capital operation risk and growth risk faced by enterprises under the requirements of low carbon economy development. A set of financial risk management framework with clear hierarchy and strict vertical logic has been constructed. Ten financial early-warning indicators are constructed from four aspects. The risk prediction model of the indicator system is established using the research method of BPNN (Back Propagation Neural Network). The model is trained and simulated through the MATLAB neural network toolbox. After 10 indicators passed Bartlett’s correlation test, the BPNN financial early warning model was programmed using MATLAB software. The accuracy rate was 84.3%. The neural network training results show that when the layer node is 8, the best correct recognition rate can be obtained. Incorporate “low carbon” into the financial risk early warning indicator system that meets the requirements of low carbon economic development in the design of enterprise financial risk early warning indicators. This paper is expected to provide reference and reference for low-carbon economy enterprises to deal with financial risks under the new situation.

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“…In terms of the overall development of the current domestic market, the application of Internet technology is developing very rapidly, and the financial management of enterprises is more urgent to be deeply integrated with modern Internet technology [1] . The level of electronic and information technology is bound to be further improved, because it has an important impact on improving the comprehensive work and efficiency of the management team, but also on the technical development of domestic enterprise financial management [2] .…”

Section: Introductionmentioning

confidence: 99%

Reflections on the Innovation of Enterprise Financial Management in the Internet Era

Shi

2022

L-E

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In the current situation, our country has fully entered the Internet era. Many emerging application technologies change people’s traditional way of life, which also promotes the sustainable development of modern enterprises. In this era of a big business environment and development background, the traditional single working model of corporate financial management cannot meet the needs of the rapid development of our current economy. This paper mainly studies the financial innovation and role mentioned in the daily financial operation and management of modern enterprises based on Internet finance and summarizes and analyzes some innovative measures, hoping to help improve the financial management level of enterprises.

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Influences of mobile edge computing-based service preloading on the early-warning of financial risks

Cited by 23 publications

References 27 publications

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

Reflections on the Innovation of Enterprise Financial Management in the Internet Era

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