Deep Recurrent Learning Through Long Short Term Memory and TOPSIS

• URL: http://arxiv.org/abs/2301.00693v2
• Date: Tue, 19 Mar 2024 16:06:19 GMT
• Title: Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
• Authors: Rossi Kamal, Zuzana Kubincova, Mosaddek Hossain Kamal, Upama Kabir,
• Abstract summary: Cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classification algorithm based on long short term memory (LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption features. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities. Qualitative survey is conducted among users by considering technology, innovation and resistance issues, to formulate hypotheses on key adoption factors.

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