| Practical convolutional neural networks: implement advanced deep learning models using Python M Sewak, MR Karim, P Pujari Packt Publishing Ltd, 2018 | 153 | 2018 |
| Deep reinforcement learning M Sewak Springer Singapore, 2019 | 113 | 2019 |
| Malware Detection Using Machine Learning and Deep Learning H Rathore, S Agarwal, SK Sahay, M Sewak | 95 | 2019 |
| Comparison of deep learning and the classical machine learning algorithm for the malware detection M Sewak, SK Sahay, H Rathore 2018 19th IEEE/ACIS international conference on software engineering …, 2018 | 71 | 2018 |
| Deep Q Network (DQN), Double DQN, and Dueling DQN: A Step Towards General Artificial Intelligence M Sewak, M Sewak Deep Reinforcement Learning: Frontiers of Artificial Intelligence, 95-108, 2019 | 43 | 2019 |
| An investigation of a deep learning based malware detection system M Sewak, SK Sahay, H Rathore Proceedings of the 13th International Conference on Availability …, 2018 | 43 | 2018 |
| Robust android malware detection system against adversarial attacks using q-learning H Rathore, SK Sahay, P Nikam, M Sewak Information Systems Frontiers 23, 867-882, 2021 | 40 | 2021 |
| An overview of deep learning architecture of deep neural networks and autoencoders M Sewak, SK Sahay, H Rathore Journal of Computational and Theoretical Nanoscience 17 (1), 182-188, 2020 | 40 | 2020 |
| Winning in the Era of Serverless Computing and Function as a Service M Sewak, S Singh 2018 3rd International Conference for Convergence in Technology (I2CT), 1-5, 2018 | 40 | 2018 |
| IoT and distributed machine learning powered optimal state recommender solution M Sewak, S Singh 2016 International Conference on Internet of Things and Applications (IOTA …, 2016 | 20 | 2016 |
| Identification of significant permissions for efficient android malware detection H Rathore, SK Sahay, R Rajvanshi, M Sewak Broadband Communications, Networks, and Systems: 11th EAI International …, 2021 | 17 | 2021 |
| Android Malicious Application Classification Using Clustering H Rathore, SK Sahay, P Chaturvedi, M Sewak | 16 | 2019 |
| DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS M Sewak, SK Sahay, H Rathore Proceedings of the 2020 ACM International Joint Conference on Pervasive and …, 2020 | 15 | 2020 |
| Detection of malicious android applications: Classical machine learning vs. deep neural network integrated with clustering H Rathore, SK Sahay, S Thukral, M Sewak Broadband Communications, Networks, and Systems: 11th EAI International …, 2021 | 13 | 2021 |
| Predicting customer value YJ Chu, M Sewak, JY Shyr US Patent App. 14/281,277, 2015 | 13 | 2015 |
| DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture M Sewak, SK Sahay, H Rathore 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile …, 2020 | 12 | 2020 |
| Policy-Based Reinforcement Learning Approaches: Stochastic Policy Gradient and the REINFORCE Algorithm M Sewak, M Sewak Deep Reinforcement Learning: Frontiers of Artificial Intelligence, 127-140, 2019 | 10 | 2019 |
| Trend-factored RFM scores to improve campaign performance M Sewak US Patent 10,152,726, 2018 | 9 | 2018 |
| IBM predictive maintenance and quality 2.0 technical overview V Negandhi, L Sreenivasan, R Giffen, M Sewak, A Rajasekharan IBM Redbooks, 2015 | 9 | 2015 |
| DRLDO: A Novel DRL based De obfuscation System for Defense Against Metamorphic Malware M Sewak, SK Sahay, H Rathore Defence Science Journal 71 (1), 55-65, 2021 | 8 | 2021 |
Sanjay K. SahayAssociate Professor, Department of Computer Science and Information Systems, BITS, Pilani, GoaVerified email at goa.bits-pilani.ac.in
