| Predictive analytics with Microsoft Azure machine learning R Barga, V Fontama, WH Tok, L Cabrera-Cordon Apress, 2015 | 153 | 2015 |
| Evaluating convective heat transfer coefficients using neural networks K Jambunathan, SL Hartle, S Ashforth-Frost, VN Fontama International Journal of Heat and Mass Transfer 39 (11), 2329-2332, 1996 | 145 | 1996 |
| Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain WJ Walley, VN Fontama Water Research 32 (3), 613-622, 1998 | 85 | 1998 |
| New approaches to river quality classification based upon Artificial Intelligence. WJ Walley, VN Fontama Assessing the biological quality of fresh waters: RIVPACS and other …, 2000 | 30 | 2000 |
| Introducing microsoft azure machine learning R Barga, V Fontama, WH Tok Predictive Analytics with Microsoft Azure Machine Learning, 21-43, 2015 | 25 | 2015 |
| The role of neural networks in fluid mechanics and heat transfer S Ashforth-Frost, VN Fontama, K Jambunathan, SL Hartle Proceedings of 1995 IEEE Instrumentation and Measurement Technology …, 1995 | 19 | 1995 |
| Introducing Microsoft Azure HDInsight A Chauhan, V Fontama, M Hart, WH Tok, B Woody Microsoft press, 2014 | 14 | 2014 |
| Using ART2 networks to deduce flow velocities K Jambunathan, VN Fontama, SL Hartle, S Ashforth-Frost Artificial intelligence in engineering 11 (2), 135-141, 1997 | 10 | 1997 |
| Churn prediction using static and dynamic features F Zhu, X Song, C Zhong, S Fang, R Bouchard, VN Fontama, P Singh, ... US Patent App. 15/446,870, 2018 | 8 | 2018 |
| Cortana analytics R Barga, V Fontama, WH Tok Predictive Analytics with Microsoft Azure Machine Learning, 279-283, 2015 | 8 | 2015 |
| Applications of Artificial Intelligence for the Biological Surveillance of River Quality WJ Walley, VN Fontama, RW Martin Environment Agency, 1998 | 7 | 1998 |
| Bio-monitoring of rivers: an AI approach to data interpretation WJ Walley, VN Fontama TOXICOLOGY AND ECOTOXICOLOGY NEWS REVIEWS 4, 182-184, 1997 | 4 | 1997 |
| Integration with R R Barga, V Fontama, WH Tok Predictive Analytics with Microsoft Azure Machine Learning, 81-101, 2015 | 2 | 2015 |
| Introduction to Data Science R Barga, V Fontama, WH Tok Predictive Analytics with Microsoft Azure Machine Learning, 3-20, 2015 | 2 | 2015 |
| Building Customer Propensity Models R Barga, V Fontama, WH Tok Predictive Analytics with Microsoft Azure Machine Learning, 151-171, 2015 | 1 | 2015 |
| Data Preparation R Barga, V Fontama, WH Tok Predictive Analytics with Microsoft Azure Machine Learning, 45-79, 2015 | 1 | 2015 |
| ART2 networks for particle image velocimetry SL Hartle, VN Fontama, S Ashforth-Frost, K Jambunathan IET Digital Library, 1995 | 1 | 1995 |
| Interconnecting nodes of entity combinations JD Fitzgerald, VN Fontama US Patent 10,931,761, 2021 | | 2021 |
| Storing parseable entity combinations JD Fitzgerald, VN Fontama US Patent App. 15/430,246, 2018 | | 2018 |
| Recommendation Systems R Barga, V Fontama, WH Tok Predictive Analytics with Microsoft Azure Machine Learning, 243-262, 2015 | | 2015 |
Wee Hyong TOKMicrosoftVerified email at microsoft.com
