Realization of Business Intelligence using Machine Learning

Rath, Mamata

doi:10.1002/9781119711148.ch10

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…SLD-BCT, IoT-HLF, BCHS-EPS-LNA. Rath, M. et al(Rath, 2021) proposed the study was the groundwork for establishing a Business Intelligence methodology (BIM). BI frameworks combine operational and recorded data with organized and systematic instruments to present cost-effective and consistent data to business employees and executives.…”

Section: Related Workmentioning

confidence: 99%

Distributed hyperledger technology in FinTech with artificial intelligence assisted internet of things platform

Zhou

et al. 2022

Expert Systems

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Artificial intelligence (AI) and distributed hyper ledger technology are both at the height of their hype cycles, which means they have the potential to be disruptive. This raises issues about new technologies' influence on future business structures, particularly in service‐based industries like finance. Even though numerous assumptions in reality point to the complementary use of these two technologies to produce new value creation potentials, there is little contemporary literature and study on the subject. Different perspectives on any of the two technologies mentioned above are insufficient for understanding potential synergies in financial services. Similarly, the internet of things (IoT) assistance for FinTech solutions has risen its significance in the upcoming transaction world. Therefore, this research proposes a distributed hyper ledger technology and AI for IoT‐based FinTech platform (DHLT‐AI‐FTP), which incorporates the benefits of each technology for secured and energy‐efficient financial transactions and communication. The AI technology and distributed hyper ledger technology handle the security part, and the communication part holds the efficient design of IoT implementation. The simulation evaluation of the proposed model ensures the improved security and efficiency of FinTech management and processing for real‐time business scenarios. The experimental analysis shows the significant performance of the proposed DHLT‐AI‐FTP with a better adaptability ratio of 98.56% compared to other existing approaches.

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Section: Related Workmentioning

confidence: 99%