| Fuzzy grey relational analysis for software effort estimation M Azzeh, D Neagu, PI Cowling Empirical Software Engineering 15 (1), 60-90, 2010 | 109 | 2010 |
| Information quality framework for e-learning systems M Alkhattabi, D Neagu, A Cullen Knowledge Management & E-Learning: An International Journal 2 (4), 340-362, 2010 | 88 | 2010 |
| Assessing information quality of e-learning systems: a web mining approach M Alkhattabi, D Neagu, A Cullen Computers in Human Behavior 27 (2), 862-873, 2011 | 87 | 2011 |
| Analogy-based software effort estimation using Fuzzy numbers M Azzeh, D Neagu, PI Cowling Journal of Systems and Software 84 (2), 270-284, 2011 | 76 | 2011 |
| Interpreting random forest classification models using a feature contribution method A Palczewska, J Palczewski, RM Robinson, D Neagu Integration of reusable systems, 193-218, 2014 | 71 | 2014 |
| Improving analogy software effort estimation using fuzzy feature subset selection algorithm M Azzeh, D Neagu, P Cowling Proceedings of the 4th international workshop on Predictor models in …, 2008 | 57 | 2008 |
| Machine learning algorithms: a study on noise sensitivity E Kalapanidas, N Avouris, M Craciun, D Neagu Proc. 1st Balcan Conference in Informatics, 356-365, 2003 | 57 | 2003 |
| The importance of scaling in data mining for toxicity prediction P Mazzatorta, E Benfenati, D Neagu, G Gini Journal of chemical information and computer sciences 42 (5), 1250-1255, 2002 | 57 | 2002 |
| Data governance in predictive toxicology: A review X Fu, A Wojak, D Neagu, M Ridley, K Travis Journal of cheminformatics 3 (1), 24, 2011 | 52 | 2011 |
| Social media analysis for product safety using text mining and sentiment analysis H Isah, P Trundle, D Neagu 2014 14th UK workshop on computational intelligence (UKCI), 1-7, 2014 | 51 | 2014 |
| Computational intelligence: engineering of hybrid systems MG Negoita, D Neagu, V Palade Springer Science & Business Media, 2005 | 49 | 2005 |
| Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a case study M Vračko, V Bandelj, P Barbieri, E Benfenati, Q Chaudhry, M Cronin, ... SAR and QSAR in Environmental Research 17 (3), 265-284, 2006 | 47 | 2006 |
| Interpreting random forest models using a feature contribution method A Palczewska, J Palczewski, RM Robinson, D Neagu 2013 IEEE 14th International Conference on Information Reuse & Integration …, 2013 | 45 | 2013 |
| Interpretation of trained neural networks by rule extraction V Palade, DC Neagu, RJ Patton International Conference on Computational Intelligence, 152-161, 2001 | 35 | 2001 |
| Predictive model representation and comparison: Towards data and predictive models governance M Makhtar, DC Neagu, M Ridley 2010 UK Workshop on Computational Intelligence (UKCI), 1-6, 2010 | 30 | 2010 |
| Towards a fuzzy expert system on toxicological data quality assessment L Yang, D Neagu, MTD Cronin, M Hewitt, SJ Enoch, JC Madden, ... Molecular informatics 32 (1), 65-78, 2013 | 26 | 2013 |
| Software effort estimation based on weighted fuzzy grey relational analysis M Azzeh, D Neagu, P Cowling Proceedings of the 5th International Conference on Predictor Models in …, 2009 | 26 | 2009 |
| Fusing integrated visual vocabularies-based bag of visual words and weighted colour moments on spatial pyramid layout for natural scene image classification Y Alqasrawi, D Neagu, PI Cowling Signal, Image and Video Processing 7 (4), 759-775, 2013 | 25 | 2013 |
| Software project similarity measurement based on fuzzy C-means M Azzeh, D Neagu, P Cowling International Conference on software process, 123-134, 2008 | 25 | 2008 |
| Data retrieval from online social network profiles for social engineering applications S Alim, R Abdul-Rahman, D Neagu, M Ridley 2009 International Conference for Internet Technology and Secured …, 2009 | 24 | 2009 |
Mohammad AzzehApplied Science Private UniversityVerified email at asu.edu.jo
