Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
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.
Cite
@article{arxiv.2301.00693,
title = {Deep Recurrent Learning Through Long Short Term Memory and TOPSIS},
author = {Rossi Kamal},
journal= {arXiv preprint arXiv:2301.00693},
year = {2025}
}
Comments
This submission has been withdrawn by arXiv administrators one of the authors was added without their knowledge or consent. Authorship has been truncated