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Enterprise resource planning (ERP) software brings resources, data togetherto keep software-flow within business processes in a company. However, cloudcomputing's cheap, easy and quick management promise pushes business-owners fora transition from monolithic to a data-center/cloud based ERP. Since cloud-ERPdevelopment involves a cyclic process, namely planning, implementing, testingand upgrading, its adoption is realized as a deep recurrent neural networkproblem. Eventually, a classification algorithm based on long short term memory(LSTM) and TOPSIS is proposed to identify and rank, respectively, adoptionfeatures. Our theoretical model is validated over a reference model byarticulating key players, services, architecture, functionalities. Qualitativesurvey is conducted among users by considering technology, innovation andresistance issues, to formulate hypotheses on key adoption factors.

Deep Recurrent Learning Through Long Short Term Memory and TOPSIS