| Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification S Yadav, S Shukla 2016 IEEE 6th International conference on advanced computing (IACC), 78-83, 2016 | 760 | 2016 |
| Dynamic selection of normalization techniques using data complexity measures S Jain, S Shukla, R Wadhvani Expert Systems with Applications 106, 252-262, 2018 | 207 | 2018 |
| SMOTE based class-specific extreme learning machine for imbalanced learning BS Raghuwanshi, S Shukla Knowledge-Based Systems 187, 104814, 2020 | 116 | 2020 |
| Class imbalance learning using UnderBagging based kernelized extreme learning machine BS Raghuwanshi, S Shukla Neurocomputing 329, 172-187, 2019 | 90 | 2019 |
| Pre‐trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology S Saxena, S Shukla, M Gyanchandani International Journal of Imaging Systems and Technology 30 (3), 577-591, 2020 | 84 | 2020 |
| Class-specific extreme learning machine for handling binary class imbalance problem BS Raghuwanshi, S Shukla Neural Networks 105, 206-217, 2018 | 75 | 2018 |
| Underbagging based reduced kernelized weighted extreme learning machine for class imbalance learning BS Raghuwanshi, S Shukla Engineering Applications of Artificial Intelligence 74, 252-270, 2018 | 48 | 2018 |
| Breast cancer histopathology image classification using kernelized weighted extreme learning machine S Saxena, S Shukla, M Gyanchandani International Journal of Imaging Systems and Technology 31 (1), 168-179, 2021 | 46 | 2021 |
| Class-specific kernelized extreme learning machine for binary class imbalance learning BS Raghuwanshi, S Shukla Applied Soft Computing 73, 1026-1038, 2018 | 45 | 2018 |
| Online sequential class-specific extreme learning machine for binary imbalanced learning S Shukla, BS Raghuwanshi Neural Networks 119, 235-248, 2019 | 37 | 2019 |
| Spam filtering using support vector machine P Chhabra, R Wadhvani, S Shukla Special Issue IJCCT 1 (2), 3, 2010 | 37 | 2010 |
| A clustering based ensemble of weighted kernelized extreme learning machine for class imbalance learning R Choudhary, S Shukla Expert Systems with Applications 164, 114041, 2021 | 34 | 2021 |
| Analysis of statistical features for fault detection in ball bearing S Shukla, RN Yadav, J Sharma, S Khare 2015 IEEE international conference on computational intelligence and …, 2015 | 34 | 2015 |
| Regularized weighted circular complex-valued extreme learning machine for imbalanced learning S Shukla, RN Yadav IEEE Access 3, 3048-3057, 2015 | 30 | 2015 |
| Generalized class-specific kernelized extreme learning machine for multiclass imbalanced learning BS Raghuwanshi, S Shukla Expert Systems with Applications 121, 244-255, 2019 | 29 | 2019 |
| Intrusion detection using clustering KK Bharti, S Shukla, S Jain Proceeding of the Association of Counseling Center Training Agencies (ACCTA) 1, 2010 | 29 | 2010 |
| Classifying imbalanced data using SMOTE based class-specific kernelized ELM BS Raghuwanshi, S Shukla International Journal of Machine Learning and Cybernetics 12 (5), 1255-1280, 2021 | 25 | 2021 |
| An analysis of “A feature reduced intrusion detection system using ANN classifier” by Akashdeep et al. expert systems with applications (2017) T Chandak, S Shukla, R Wadhvani Expert Systems with applications 130, 79-83, 2019 | 19 | 2019 |
| Classifying imbalanced data using BalanceCascade-based kernelized extreme learning machine BS Raghuwanshi, S Shukla Pattern Analysis and Applications 23 (3), 1157-1182, 2020 | 17 | 2020 |
| Fuzzy K-mean clustering via J48 for intrusion detection system K Bharti, S Jain, S Shukla International Journal of Computer Science and Information Technologies 1 (4 …, 2010 | 16 | 2010 |
Dr. Bhagat Singh RaghuwanshiAssistant Professor MITS Gwalior, Formerly (Assistant Prog., Department of Commercial Tax Govt. MPVerified email at mitsgwalior.in
