"Predicting ERP User Satisfaction―an Adaptive Neuro Fuzzy Inference System (ANFIS) Approach"
written by Chengaleth Venugopal, Siva Prasanna Devi, Kavuri Suryaprakasa Rao,
published by Intelligent Information Management, Vol.2 No.7, 2010
has been cited by the following article(s):
  • Google Scholar
  • CrossRef
[1] How to Disable Mortal Loops of Enterprise Resource Planning (ERP) Implementation: A System Dynamics Analysis
Systems, 2018
[2] High Prediction Accuracy and Low Error for ERP User Satisfaction by Hybrid of ANFIS and KNN Classification Pinky Kumawat Department of Computer …
2018
[3] User Satisfaction Prediction in ERP using KNN Classifier for high Prediction Accuracy
Sandip Foundation's International Journal on Emerging Trends in Technology (IJETT), 2018
[4] Risks Assessment using Fuzzy Petri Nets for ERP Extension in Small and Medium Enterprises
Information Resources Management Journal (IRMJ), 2017
[5] Modeling of Water Quality Parameters in Manzala Lake Using Adaptive Neuro-Fuzzy Inference System and Stochastic Models
The Handbook of Environmental Chemistry book series, 2017
[6] Prediction of ERP Outcome Measurement and User Satisfaction Using Adaptive Neuro-Fuzzy Inference System and SVM Classifiers Approach
Proceedings of the International Congress on Information and Communication Technology, 2016
[7] Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt
Ain Shams Engineering Journal, 2016
[8] Employee Commitment Prediction in Civil Projects Using Adaptive Neuro-Fuzzy Inference System
Journal of Current Research in Science, 2016
[9] Fuzzy inference model for assessing occupational risks in construction sites
International Journal of Industrial Ergonomics, 2016
[10] Data-driven stochastic modeling for multi-purpose reservoir simulation
Journal of Toxicology and Environmental Health Part A, 2016
[11] How to disable mortal loops of ERP implementation: A System Dynamics analysis
2015
[12] 信息系统用户满意度研究文献综述——以ERP 系统为例
技术经济, 2014
[13] Measuring e-learning systems success
2013
[14] Measuring e-learning system success
2013
[15] E—Learning Service Delivery Quality
2013
[16] E-learning service delivery quality: A determinant of user satisfaction
Learning management systems and instructional design: Best practices in online education, 2013
[17] Assessing Morningness of a Group of People by Using Fuzzy Expert System and Adaptive Neuro Fuzzy Inference Model
Control, Computation and Information Systems. Springer Berlin Heidelberg, 2011
[18] RESEARCH METHOD
2008
[19] DATA-DRIVEN MODELING FOR WATER QUALITY PARAMETERS PREDICTION OF THE DRAINAGE SYSTEM ASSOCIATED WITH LAKE MANZALA, EGYPT
2001