Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation

IF 4.4 4区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Enterprise Information Systems Pub Date : 2023-03-10 DOI:10.1080/17517575.2023.2188123
Prakash Mohan, S. Neelakandan, A. Mardani, Dr Sudhanshu Maurya, N. Arulkumar, K. Thangaraj
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引用次数: 7

Abstract

ABSTRACT Hyper automation is the group of approaches and software companies utilised to automate manual procedures. Financial Technology (FinTech) was processed as a distinctive classification that highly inspects the financial technology sector from a broader group of functions for enterprises with utilise of Information Technology (IT) application. Financial crisis prediction (FCP) is the most essential FinTech technique, defining institutions’ financial status. This study proposes an Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest (ESAOA-ODCF) based FinTech Application for Hyperautomation. The ESAOA-ODCF technique has achieved exceptional performance with maximum accu y of 98.61%, and F score of 98.59%. Extensive experimental research revealed that the ESAOA-ODCF model beat more modern, cutting-edge approaches in terms of overall performance.
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基于深度卷积森林的Eagle策略算法优化算法在超自动化中的应用
摘要超自动化是一组用于自动化手动程序的方法和软件公司。金融技术(FinTech)被视为一个独特的分类,从利用信息技术(IT)应用的企业的更广泛的功能组中高度检查金融技术部门。金融危机预测(FCP)是金融科技中最重要的技术,用来确定金融机构的财务状况。本研究提出了一种基于最优深度卷积森林(ESAOA-ODCF)的Eagle策略算术优化算法,用于超自动化的金融技术应用。ESAOA-ODCF技术取得了卓越的性能,最高准确率为98.61%,F得分为98.59%。广泛的实验研究表明,ESAOA-ODCCF模型在整体性能方面击败了更现代、更前沿的方法。
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来源期刊
Enterprise Information Systems
Enterprise Information Systems 工程技术-计算机:信息系统
CiteScore
11.00
自引率
6.80%
发文量
24
审稿时长
6 months
期刊介绍: Enterprise Information Systems (EIS) focusses on both the technical and applications aspects of EIS technology, and the complex and cross-disciplinary problems of enterprise integration that arise in integrating extended enterprises in a contemporary global supply chain environment. Techniques developed in mathematical science, computer science, manufacturing engineering, and operations management used in the design or operation of EIS will also be considered.
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