تقرير
Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
| العنوان: | Deep Recurrent Learning Through Long Short Term Memory and TOPSIS |
|---|---|
| المؤلفون: | Kamal, Rossi, Kubincova, Zuzana, Kamal, Mosaddek Hossain, Kabir, Upama |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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. Comment: This submission has been withdrawn by arXiv administrators as the second author was added without their knowledge or consent |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2301.00693 |
| رقم الأكسشن: | edsarx.2301.00693 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |