| A review of some techniques for inclusion of domain-knowledge into deep neural networks T Dash, S Chitlangia, A Ahuja, A Srinivasan Scientific Reports 12 (1), 1040, 2022 | 138* | 2022 |
| A study on intrusion detection using neural networks trained with evolutionary algorithms T Dash Soft Computing 21 (10), 2687-2700, 2017 | 99 | 2017 |
| Multifault diagnosis in WSN using a hybrid metaheuristic trained neural network RR Swain, PM Khilar, T Dash Digital Communications and Networks 6 (1), 86-100, 2020 | 43 | 2020 |
| A complete diagnosis of faulty sensor modules in a wireless sensor network RR Swain, T Dash, PM Khilar Ad Hoc Networks 93, 101924, 2019 | 32 | 2019 |
| Hybrid gravitational search and particle swarm based fuzzy MLP for medical data classification T Dash, SK Nayak, HS Behera Computational Intelligence in Data Mining-Volume 1: Proceedings of the …, 2015 | 29 | 2015 |
| Neural network based automated detection of link failures in wireless sensor networks and extension to a study on the detection of disjoint nodes RR Swain, PM Khilar, T Dash Journal of Ambient Intelligence and Humanized Computing 10, 593-610, 2019 | 28 | 2019 |
| Large-scale assessment of deep relational machines T Dash, A Srinivasan, L Vig, OI Orhobor, RD King Inductive Logic Programming: 28th International Conference, ILP 2018 …, 2018 | 28 | 2018 |
| Time efficient approach to offline hand written character recognition using associative memory net T Dash arXiv preprint arXiv:1306.4592, 2013 | 26 | 2013 |
| An effective graph‐theoretic approach towards simultaneous detection of fault (s) and cut (s) in wireless sensor networks RR Swain, T Dash, PM Khilar International Journal of Communication Systems 30 (13), e3273, 2017 | 25 | 2017 |
| Controlling wall following robot navigation based on gravitational search and feed forward neural network T Dash, T Nayak, RR Swain Proceedings of the 2nd international conference on perception and machine …, 2015 | 25 | 2015 |
| Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance RR Swain, PM Khilar, T Dash International Journal of Communication Systems 31 (14), e3769, 2018 | 24 | 2018 |
| Incorporating symbolic domain knowledge into graph neural networks T Dash, A Srinivasan, L Vig Machine Learning 110 (7), 1609-1636, 2021 | 23 | 2021 |
| Offline handwritten signature verification using Associative Memory Net T Dash, T Nayak, S Chattopadhyay International Journal of Advanced Research in Computer Engineering …, 2012 | 21 | 2012 |
| Offline verification of hand written signature using adaptive resonance theory net (type-1) T Dash, T Nayak, S Chattopadhyay Proc: IEEE Int. Conf. Electronics Computer Technology (ICECT) 2, 205-210, 2012 | 21 | 2012 |
| Transformational machine learning: Learning how to learn from many related scientific problems I Olier, OI Orhobor, T Dash, AM Davis, LN Soldatova, J Vanschoren, ... Proceedings of the National Academy of Sciences 118 (49), e2108013118, 2021 | 20 | 2021 |
| Automatic navigation of wall following mobile robot using adaptive resonance theory of type-1 T Dash Biologically Inspired Cognitive Architectures 12, 1-8, 2015 | 20 | 2015 |
| English character recognition using artificial neural network T Dash, T Nayak arXiv preprint arXiv:1306.4621, 2013 | 20 | 2013 |
| Gradient gravitational search: an efficient metaheuristic algorithm for global optimization T Dash, PK Sahu Journal of computational chemistry 36 (14), 1060-1068, 2015 | 19 | 2015 |
| Performance evaluation of deep neural networks for forecasting time‐series with multiple structural breaks and high volatility R Kaushik, S Jain, S Jain, T Dash CAAI Transactions on Intelligence Technology 6 (3), 265-280, 2021 | 18 | 2021 |
| Neural network approach to control wall-following robot navigation T Dash, SR Sahu, T Nayak, G Mishra 2014 IEEE International Conference on Advanced Communications, Control and …, 2014 | 17 | 2014 |
Ashwin SrinivasanSenior Professor, Computer Science, BITS-Pilani, GoaVerified email at wolfson.oxon.org
