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

Prakash MohanSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaS. NeelakandanDepartment of Computer Science and Engineering, R.M.K Engineering College, Chennai, IndiaAbbas MardaniBusiness School, Worcester Polytechnic Institute, Worcester, MA, USASudhanshu MauryaSchool of Computing, Graphic Era Hill University, Dehradun, IndiaN. ArulkumarDepartment of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, IndiaK. Thangaraj

2023en

ABI

Annotatsiya

ABSTRACTHyper 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.KEYWORDS: FinTechfinancial crisis predictionfeature selectionarithmetic optimisation algorithminvasive weed optimisationhyperautomation Disclosure statementNo potential conflict of interest was reported by the authors.

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Identifikatorlar

DOI: 10.1080/17517575.2023.2188123

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