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              <text>Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation</text>
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              <text>arithmetic optimisation algorithm; feature selection; financial crisis prediction; FinTech; hyperautomation; invasive weed optimisation</text>
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              <text>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.  2023 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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              <text>Mohan P.; Neelakandan S.; Mardani A.; Maurya S.; Arulkumar N.; Thangaraj K.</text>
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              <text>Enterprise Information Systems, Vol-17, No. 10</text>
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              <text>Taylor and Francis Ltd.</text>
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              <text>&lt;a href="https://doi.org/10.1080/17517575.2023.2188123" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1080/17517575.2023.2188123&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISSN: 17517575</text>
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              <text>Mohan P., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; Neelakandan S., Department of Computer Science and Engineering, R.M.K Engineering College, Chennai, India; Mardani A., Business School, Worcester Polytechnic Institute, Worcester, MA, United States; Maurya S., School of Computing, Graphic Era Hill University, Dehradun, India; Arulkumar N., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India; Thangaraj K., Department of Information Technology, Sona College of Technology, Salem, India</text>
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