| A fuzzy twin support vector machine based on information entropy for class imbalance learning D Gupta, B Richhariya, P Borah Neural Computing and Applications 31 (11), 7153-7164, 2019 | 81 | 2019 |
| An intuitionistic fuzzy kernel ridge regression classifier for binary classification BB Hazarika, D Gupta, P Borah Applied Soft Computing 112, 107816, 2021 | 46 | 2021 |
| Analysis of machine learning techniques based intrusion detection systems RK Sharma, HK Kalita, P Borah Proceedings of 3rd International Conference on Advanced Computing …, 2016 | 45 | 2016 |
| Robust twin bounded support vector machines for outliers and imbalanced data P Borah, D Gupta Applied Intelligence 51 (8), 5314-5343, 2021 | 34 | 2021 |
| Functional iterative approaches for solving support vector classification problems based on generalized Huber loss P Borah, D Gupta Neural computing and applications 32 (13), 9245-9265, 2020 | 27 | 2020 |
| Data-driven mechanism based on fuzzy Lagrangian twin parametric-margin support vector machine for biomedical data analysis D Gupta, P Borah, UM Sharma, M Prasad Neural Computing and Applications 34 (14), 11335-11345, 2022 | 22 | 2022 |
| A fuzzy based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) D Gupta, P Borah, M Prasad 2017 IEEE symposium series on computational intelligence (SSCI), 1-7, 2017 | 21 | 2017 |
| Affinity and transformed class probability-based fuzzy least squares support vector machines P Borah, D Gupta Fuzzy Sets and Systems 443, 203-235, 2022 | 20 | 2022 |
| Unconstrained convex minimization based implicit Lagrangian twin extreme learning machine for classification (ULTELMC) P Borah, D Gupta Applied Intelligence 50 (4), 1327-1344, 2020 | 17 | 2020 |
| Unconstrained convex minimization based implicit Lagrangian twin random vector Functional-link networks for binary classification (ULTRVFLC) P Borah, D Gupta Applied Soft Computing 81, 105534, 2019 | 16 | 2019 |
| Improved 2-norm based fuzzy least squares twin support vector machine P Borah, D Gupta, M Prasad 2018 IEEE symposium series on computational intelligence (SSCI), 412-419, 2018 | 14 | 2018 |
| Review: support vector machines in pattern recognition P Borah, D Gupta International Journal of Engineering & Technology 9 (3S), 43-48, 2017 | 12 | 2017 |
| A two-norm squared fuzzy-based least squares twin parametric-margin support vector machine P Borah, D Gupta Machine Intelligence and Signal Analysis, 119-134, 2019 | 9 | 2019 |
| Fuzzy twin support vector machine based on affinity and class probability for class imbalance learning BB Hazarika, D Gupta, P Borah Knowledge and Information Systems 65 (12), 5259-5288, 2023 | 5 | 2023 |
| Kernelized random vector functional-link network P Borah, D Gupta, SSS Mishra Pattern Recognition and Data Analysis with Applications, 743-750, 2022 | 4 | 2022 |
| Robust support vector quantile regression with truncated pinball loss (RSVQR) BB Hazarika, D Gupta, P Borah Computational and Applied Mathematics 42 (6), 283, 2023 | 2 | 2023 |
| A Twin Kernel Ridge Regression Classifier for Binary Classification B Bikash Hazarika, D Gupta, P Borah Pattern Recognition and Data Analysis with Applications, 715-727, 2022 | 1 | 2022 |
| On Lagrangian twin parametric-margin support vector machine P Borah, D Gupta Smart and Innovative Trends in Next Generation Computing Technologies: Third …, 2018 | 1 | 2018 |
| Fuzzy twin kernel ridge regression classifiers for liver disorder detection D Gupta, BB Hazarika, P Borah International Journal of Business Intelligence and Data Mining 24 (2), 131-145, 2024 | | 2024 |
| Fittest Secret Key Selection Using Genetic Algorithm in Modern Cryptosystem C Chunka, A Maurya, P Borah Intelligent and Cloud Computing: Proceedings of ICICC 2021, 227-241, 2022 | | 2022 |
