An Equivalent Exchange Based Data Forwarding Incentive Scheme for Socially Aware Networks

Xiong, Zenggang; Xiao, Nan; Xu, Fang; Zhang, Xuemin; Xu, Qiong; Kaibin, Zhang; Ye, Conghuan

doi:10.1007/s11265-020-01610-6

Cited by 53 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e fitness value is used as a measure of the evolutionary behavior of individuals in a population. Genetic algorithms establish fitness functions to determine the fitness value of individuals and reproduce well-adapted individuals based on the fitness value to find the optimal solution [19]. Commonly used methods include the roulette wheel method and ranked selection method.…”

Section: Financing Risk Investment Riskmentioning

confidence: 99%

Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Zhai¹

2021

Complexity

View full text Add to dashboard Cite

This paper mainly analyzes the theories related to the financial risk of the company and combines the principles of principal component analysis, particle swarm optimization algorithm, and artificial neural network to derive the financial risk index system of the company. To improve the accuracy of financial risk prediction, principal component analysis and particle swarm algorithm are applied to optimize the BP neural network model, the input data of the prediction model is improved, and the optimal initial weights and thresholds are given to the BP neural network by using particle swarm algorithm search, whereby the financial risk prediction model of particle swarm optimization BP neural network is constructed. The empirical results show that the model constructed by BP neural network not only has a high accuracy rate for static financial risk evaluation but also has a better prediction effect. After training and testing, the BP neural network-based enterprise financial risk evaluation model can accurately determine the existing financial situation of enterprise financial management and has a good prediction effect. Our research method is a fusion of the processing of the two methods, which belongs to the first integration of results.

show abstract

Section: Financing Risk Investment Riskmentioning

confidence: 99%

Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Zhai¹

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Proof of Proposition 6. From Appendix A, we get p NR r2 = 1+c+2β+ϕη 2 (4β+ϕ(−1+β)η 2 ) 4+4ϕη 2 = 1+c+2β+ 16 25 ϕ(ϕ(−1+β)+5β) 4+ 16ϕ 5…”

Section: Appendix Bmentioning

confidence: 99%

“…Extant research on the sharing economy has been conducted by scholars in recent years [10][11][12][13]. According to Ranjbari et al [14], the sharing economy was an economic system, whose intermediary companies utilized online platforms to facilitate and lower the cost of the for-profit transactions of giving temporary access-without the transfer of ownership-to idle resources of consumers in the peer-to-peer networks that it had created, because of the trust built among its members, who might be individuals or businesses [15,16]. Besides idle resource sharing, we also considered new product sharing as a kind of sharing economy, the sharing of new products shows consumers the functions of the products and provides services in exchange for profit.…”

Section: Literature Reviewmentioning

confidence: 99%

Manufacturer’s Sharing Servitization Transformation and Product Pricing Strategy

Liu

Jian

2021

Sustainability

View full text Add to dashboard Cite

The sharing market includes the idle product sharing by the owner and the firm’s new product sharing. Companies participating in the sharing economy choose to withdraw from the market because it is always difficult to make a profit, due to heavy asset investment, but there is no such worry for owners who do not need cost input. At the same time, we have observed that the sharing products launched by companies are difficult to meet the rental needs of consumers. Based on the above findings, we have constructed a model where there is a market where a monopolistic manufacturer sells and rents out at the same time, and owners who purchased new products can choose to rent out products when they are idle. Because of the uncoordinated supply and demand matching of the sharing market and the excessively high unit cost input, our research found that: (1) the barriers for the manufacturer to enter the sharing market are always high—for example, the manufacturer will choose to enter the sharing market only when consumers have a high rate of availability of sharing products. Only when the cost of products in the sharing market is not low will the manufacturer choose to provide sharing services; (2) the competition between the two products in the sharing market weakens the demand cannibalize in the sales market; (3) the manufacturer enters the sharing market to promote the owner’s income. The owner’s earnings will increase with the rising of sharing products’ availability.

show abstract

“…Qualitative research methods are mainly used to analyze the evolution process of online public opinion from related fields such as sociology, psychology, and communication to elaborate its evolution law [24,25]. The quantitative research methods mainly include cellular automata model, system dynamics method, social network analysis method, and statistical analysis method [26][27][28]. From different angles and disciplines, the network public opinion is discussed and studied.…”

Section: Related Workmentioning

confidence: 99%

Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Zhu

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing service placement problem from the edge computing dimension. First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. Secondly, it is necessary to use the main index method to judge whether the time series has the chaotic characteristics of the network public opinion data. The simulation results show that network public opinion is the development characteristic of chaotic time series. Finally, the prediction model is improved by using complex network topology. Through the simulation experiment of network public opinion and chaotic time series, the results show that the improved model has the advantages of accuracy, rapidity, and self-adaptability and can be applied to other fields.

show abstract

An Equivalent Exchange Based Data Forwarding Incentive Scheme for Socially Aware Networks

Cited by 53 publications

References 27 publications

Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Manufacturer’s Sharing Servitization Transformation and Product Pricing Strategy

Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Contact Info

Product

Resources

About