Blockchain technology‑based FinTech banking sector involvement using adaptive neuro‑fuzzy‑based K‑nearest neighbors algorithm

The study aims to investigate the financial technology(FinTech)factors influencing Chinese banking performance.Financial expectations and global realities may be changed by FinTech’s multidimensional scope,which is lacking in the traditional financial sector.The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization.The future of FinTech will be shaped by technologies like the Internet of Things,blockchain,and artificial intelligence.The involvement of these platforms in financial services is a major concern for global business growth.FinTech is becoming more popular with customers because of such benefits.FinTech has driven a fundamental change within the financial services industry,placing the client at the center of everything.Protection has become a primary focus since data are a component of FinTech transactions.The task of consolidating research reports for consensus is very manual,as there is no standardized format.Although existing research has proposed certain methods,they have certain drawbacks in FinTech payment systems(including cryptocurrencies),credit markets(including peer-to-peer lending),and insurance systems.This paper implements blockchainbased financial technology for the banking sector to overcome these transition issues.In this study,we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors’algorithm.The chaotic improved foraging optimization algorithm is used to optimize the proposed method.The rolling window autoregressive lag modeling approach analyzes FinTech growth.The proposed algorithm is compared with existing approaches to demonstrate its efficiency.The findings showed that it achieved 91%accuracy,90%privacy,96%robustness,and 25%cyber-risk performance.Compared with traditional approaches,the recommended strategy will be more convenient,safe,and effective in the transition period.

Enhancing environmental quality in the United States by linking biomass energy consumption and load capacity factor

This study analyzes the impact of biomass energy,financial development,and economic growth on environmental quality using the novel Fourier autoregressive distributed lag(ARDL)approach on annual data for the period 1965–2018 in the United States(USA).The study analyzes the impact of related variables on the load capacity factor(LCF)as well as on indicators of environmental degradation such as carbon dioxide emissions and ecological footprint.The LCF is one of the most comprehensive environmental indicators to date,encompassing both biocapacity and ecological footprint.In this regard,this study contributes to the environmental economics literature by examining,for the first time,the impact of biomass energy on the LCF.The results of the cointegration test show that there is only a long-run relationship between the LCF and the independent variables.According to the Fourier ARDL results,biomass energy improves the environmental quality,while financial development has no effect on the LCF.Moreover,the increase in per capita income reduces the LCF.Furthermore,since the income elasticity is larger in the long run than in the short-run,the environmental Kuznets curve is validated.Therefore,the United States government should encourage the use of biomass and investment in this form of energy.