| Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network M Ghiassi, J Skinner, D Zimbra Expert Systems with applications 40 (16), 6266-6282, 2013 | 708 | 2013 |
| A dynamic artificial neural network model for forecasting time series events M Ghiassi, H Saidane, DK Zimbra International Journal of Forecasting 21 (2), 341-362, 2005 | 392 | 2005 |
| Urban water demand forecasting with a dynamic artificial neural network model M Ghiassi, DK Zimbra, H Saidane Journal of Water Resources Planning and Management 134 (2), 138-146, 2008 | 326 | 2008 |
| Medium term system load forecasting with a dynamic artificial neural network model M Ghiassi, DK Zimbra, H Saidane Electric power systems research 76 (5), 302-316, 2006 | 249 | 2006 |
| Defining the Internet-based supply chain system for mass customized markets M Ghiassi, C Spera Computers & Industrial Engineering 45 (1), 17-41, 2003 | 210 | 2003 |
| A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach M Ghiassi, S Lee Expert Systems with Applications 106, 197-216, 2018 | 172 | 2018 |
| A dynamic architecture for artificial neural networks M Ghiassi, H Saidane Neurocomputing 63, 397-413, 2005 | 164 | 2005 |
| Pre-production forecasting of movie revenues with a dynamic artificial neural network M Ghiassi, D Lio, B Moon Expert Systems with Applications 42 (6), 3176-3193, 2015 | 159 | 2015 |
| Automated text classification using a dynamic artificial neural network model M Ghiassi, M Olschimke, B Moon, P Arnaudo Expert Systems with Applications 39 (12), 10967-10976, 2012 | 117 | 2012 |
| Brand-related Twitter sentiment analysis using feature engineering and the dynamic architecture for artificial neural networks D Zimbra, M Ghiassi, S Lee 2016 49th Hawaii International Conference on System Sciences (HICSS), 1930-1938, 2016 | 109 | 2016 |
| Targeted twitter sentiment analysis for brands using supervised feature engineering and the dynamic architecture for artificial neural networks M Ghiassi, D Zimbra, S Lee Journal of Management Information Systems 33 (4), 1034-1058, 2016 | 95 | 2016 |
| Classification of Camellia (Theaceae) species using leaf architecture variations and pattern recognition techniques H Lu, W Jiang, M Ghiassi, S Lee, M Nitin PloS one 7 (1), e29704, 2012 | 74 | 2012 |
| A dynamic artificial neural network model for forecasting nonlinear processes M Ghiassi, S Nangoy Computers & Industrial Engineering 57 (1), 287-297, 2009 | 62 | 2009 |
| Estimates of the minimum nondominated criterion values in multiple-criteria decision-making MI Dessouky, M Ghiassi, WJ Davis Engineering Costs and Production Economics 10 (1), 95-104, 1986 | 59 | 1986 |
| Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems M Ghiassi, C Burnley Expert Systems with Applications 37 (4), 3118-3128, 2010 | 50 | 2010 |
| An application of multiple criteria decision making principles for planning machining operations M Ghiassi, RE DeVor, MI Dessouky, BA Kijowski IIE Transactions 16 (2), 106-114, 1984 | 37 | 1984 |
| Large metropolitan water demand forecasting using DAN2, FTDNN, and KNN models: A case study of the city of Tehran, Iran M Ghiassi, F Fa'Al, A Abrishamchi Urban Water Journal 14 (6), 655-659, 2017 | 31 | 2017 |
| Dual programming approach to software testing M Ghiassi, KIS Woldman Software Quality Journal 3, 45-58, 1994 | 19 | 1994 |
| Simulation modeling of a multi product tomato processing plant SA Starbird, M Ghiassi Transactions of the ASAE 29 (1), 324-330, 1986 | 17 | 1986 |
| Sentiment analysis and spam filtering using the YAC2 clustering algorithm with transferability M Ghiassi, S Lee, SR Gaikwad Computers & Industrial Engineering 165, 107959, 2022 | 16 | 2022 |
