| Sentiment analysis using common‐sense and context information B Agarwal, N Mittal, P Bansal, S Garg Computational intelligence and neuroscience 2015 (1), 715730, 2015 | 276 | 2015 |
| Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy A Agrawal, N Mittal The Visual Computer 36 (2), 405-412, 2020 | 268 | 2020 |
| Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges C Prakash, R Kumar, N Mittal Artificial Intelligence Review 49, 1-40, 2018 | 266 | 2018 |
| Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach B Agarwal, S Poria, N Mittal, A Gelbukh, A Hussain Cognitive Computation 7, 487-499, 2015 | 170 | 2015 |
| Machine learning approach for sentiment analysis B Agarwal, N Mittal, B Agarwal, N Mittal Prominent feature extraction for sentiment analysis, 21-45, 2016 | 135 | 2016 |
| Sentiment analysis of hindi reviews based on negation and discourse relation N Mittal, B Agarwal, G Chouhan, N Bania, P Pareek Proceedings of the 11th workshop on Asian language resources, 45-50, 2013 | 135 | 2013 |
| Hybrid approach for detection of anomaly network traffic using data mining techniques B Agarwal, N Mittal Procedia Technology 6, 996-1003, 2012 | 126 | 2012 |
| Text classification using machine learning methods-a survey B Agarwal, N Mittal Proceedings of the Second International Conference on Soft Computing for …, 2014 | 123 | 2014 |
| Prominent feature extraction for sentiment analysis B Agarwal, N Mittal Springer International Publishing, 2016 | 113 | 2016 |
| Optimal feature selection for sentiment analysis B Agarwal, N Mittal Computational Linguistics and Intelligent Text Processing: 14th …, 2013 | 97 | 2013 |
| Prominent feature extraction for review analysis: an empirical study B Agarwal, N Mittal Journal of Experimental & Theoretical Artificial Intelligence 28 (3), 485-498, 2016 | 84 | 2016 |
| Deep learning-based approaches for sentiment analysis B Agarwal, R Nayak, N Mittal, S Patnaik Springer 12, 319, 2020 | 72 | 2020 |
| Sentiment classification using rough set based hybrid feature selection B Agarwal, N Mittal Proceedings of the 4th workshop on computational approaches to subjectivity …, 2013 | 61 | 2013 |
| Modeling Indian general elections: sentiment analysis of political Twitter data K Singhal, B Agrawal, N Mittal Information Systems Design and Intelligent Applications: Proceedings of …, 2015 | 57 | 2015 |
| Texture‐based feature extraction of smear images for the detection of cervical cancer M Arya, N Mittal, G Singh IET Computer Vision 12 (8), 1049-1059, 2018 | 54 | 2018 |
| Image sentiment analysis using deep learning N Mittal, D Sharma, ML Joshi 2018 IEEE/WIC/ACM international conference on web intelligence (WI), 684-687, 2018 | 49 | 2018 |
| Semantic orientation-based approach for sentiment analysis B Agarwal, N Mittal, B Agarwal, N Mittal Prominent feature extraction for sentiment analysis, 77-88, 2016 | 47 | 2016 |
| Categorical probability proportion difference (CPPD): a feature selection method for sentiment classification B Agarwal, N Mittal Proceedings of the 2nd workshop on sentiment analysis where ai meets …, 2012 | 45 | 2012 |
| Removal of tungsten and other impurities from spent HDS catalyst leach liquor by an adsorption route RR Srivastava, NK Mittal, B Padh, BR Reddy Hydrometallurgy 127, 77-83, 2012 | 40 | 2012 |
| Hybrid recommender system based on fuzzy clustering and collaborative filtering SK Verma, N Mittal, B Agarwal 2013 4th international conference on computer and communication technology …, 2013 | 37 | 2013 |
Basant AgarwalCentral University of Rajasthan在 curaj.ac.in 的电子邮件经过验证
