An Institute of EminenceProfessor, Department of Computer Science and Information Systems & Co-ordinator-APPCAIR
1. N.Arya, A. Mathur, Sriparna Saha, S.Saha; Improving the Robustness and Stability of a Machine Learning Model for Breast Cancer Prognosis through the use of Multi-Modal Classifiers; Scientific Reports (Nature) [Accepted] (ScimagoJR Q1, I.F: 5.1); Jan 2023
2. N.Arya, A. Mathur, S.Saha, Sriparna Saha; Proposal of SVM Utility Kernel for Breast Cancer Survival Estimation; IEEE/ACM Transactions on Computational Biology and Bioinformatics (ScimagoJR Q1, I.F: 3.71), PMID: 35994556 DOI: 10.1109/TCBB.2022.3198879; Dec 2022
3. J.Sarkar. S.Saha, S.Sarkar; Efficient Anomaly Identification in Temporal and Non-Temporal Industrial Data using Tree Based Approaches; Applied Intelligence (ScimagoJR Q1, Springer Nature-I.F: 5.44); DOI: 10.1007/s10489-022-03940-3; Sept 2022
4. Haoxiang Yu, Vaskar Raychoudhury, Snehanshu Saha, Janick Edinger, Roger O. Smith, Md Osman Gani; Automated Surface Classification System using Vibration Patterns - A Case Study with Wheelchairs, the IEEE Transactions on Artificial Intelligence (2022); DOI: 10.1109/TAI.2022.3190828
5. Aishwarya. M, V.Raychoudhury, S. Saha, S.Kar, Anusha. K, CARE-Share: A Cooperative and Adaptive Ride Strategy for Distributed Taxi Ride Sharing, IEEE Transactions on Intelligent Transportation Systems (I.F: 9.995-ScimagoJR Q1); 23 (7), 7028-7044, July 2022; DOI: 10.1109/TITS.2021.3066439
6. Jyotirmoy sarkar, Kartik Bhatia, S. Saha, Margarita Safonova and Santonu Sarkar; Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method; Monthly Notices of the Royal Astronomical Society (ScimagoJR Q1, I.F: 5.287); 510 (4), 6022–6032 https://doi.org/10.1093/mnras/stab3556, March 2022.
7. A. Tambewkar, A. Maiya, Soma.S. Dhavala and S.Saha; Estimation and Applications of Quantiles in Deep Binary Classification; IEEE Transactions on Artificial Intelligence; 3 (2), 275-286 DOI: 10.1109/TAI.2021.3115078; April 2022
8. Tejas Prashanth, Snehanshu Saha, Sumedh Basarkod, Suraj Aralihalli, Soma S Dhavala, Sriparna Saha, and Raviprasad Aduri; LipGene: Lipschitz continuity guided adaptive learning rates for fast convergence on Microarray Expression Data Sets’; IEEE/ACM Transactions on Computational Biology and Bioinformatics (ScimagoJR Q1, I.F: 3.71); Online: https://ieeexplore.ieee.org/document/9531348
9. Rohan Mohapatra, Snehanshu Saha, Carlos A. Coello Coello, Anwesh Bhattacharya, Soma S. Dhavala and Sriparna Saha; AdaSwarm: Augmenting gradient-based optimizers in Deep Learning with Swarm Intelligence, the IEEE Transactions on Emerging Topics in Computational Intelligence; 6 (2), 329 - 340, April 2022; (ScimagoJR Q1, IF: 4.9); DOI: 10.1109/TETCI.2021.3083428
10. R.Yedida & S.Saha, Beginning with Machine Learning: A Comprehensive Primer; European Physical Journal-Special Topics, Springer,(I.F: 2.7, ScimagoJR Q2); 230: 2363-2444; https://doi.org/10.1140/epjs/s11734- 021-00209-7
11. Yedida.R, Saha.S,, Tejas.P; LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence; Applied Intelligence (Springer-I.F: 5.1, ScimagoJR Q1), Volume: 51 (3), 1460-1478 March 2021; https://doi.org/10.1007/s10489-020-01892-0
12. S.Saha, N.Nagaraj, A.Mathur, R.Yedida, Sneha.H; Evolution of Novel Activation Functions in Neural Network Training for Astronomy Data: Habitability Classification of Exoplanets; EPJ Special Topics (Springer-I.F: 2.7, ScimagoJR Q2), 229, 2629–2738 (2020); DOI: 10.1140/epjst/e2020-000098-9
13. Harikrishnan Nellippallil Balakrishnan, Aditi Kathpalia, Snehanshu Saha and Nithin Nagaraj; ChaosNet: A chaos based Artificial Neural Network Architecture for Classification; Chaos: An Interdisciplinary Journal of Nonlinear Science (AIP-I.F: 3.85, ScimagoJR Q1); 113-125, 29(11), June 2020 (Editor’s collection)
14. S.Saha, N.Nagaraj, A.Mathur, R.Yedida, Sneha.H; Evolution of Novel Activation Functions in Neural Network Training for Astronomy Data: Habitability Classification of Exoplanets; EPJ Special Topics (Springer-I.F: 2.7, ScimagoJR Q2), 229, 2629–2738 (2020); DOI: 10.1140/epjst/e2020-000098-9
15. Yedida.R, Saha.S,, Tejas.P; LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence; Applied Intelligence (Springer-I.F: 4.325, ScimagoJR Q1), August 2020; https://doi.org/10.1007/s10489- 020-01892-0
16. Luckyson Khaidema, S.Saha, Saibal Kar, Archana Mathur, Sriparna Saha, Expert Habitat: A Colonization Conjecture for Exoplanetary Habitability via penalized multi-objective optimization based candidate validation; 230, 2265–2283 (2021) (European Physical Journal-Special Topics, Springer, I.F: 2.7, ScimagoJR Q2)
17. R.Yedida & S.Saha, Beginning with Machine Learning: A Comprehensive Primer for Astronomers; 230, pages2265–2283 (2021), European Physical Journal-Special Topics (Springer,I.F: 2.7, ScimagoJR Q2)
18. Suryoday Basak, Snehanshu Saha, Archana Mathur, Kakoli Bora, Simran Makhija, Margarita Safonova, Surbhi Agrawal; CESSA Meets Machine Learning: From Earth Similarity to Habitability Classification of Exoplanets, Astronomy and Computing (Elsevier–I.F: 3.1, ScimagoJR Q1), 30, January 2020; 10.1016/j.ascom.2019.100335
19. Simran Makhija, S.Saha, Suryoday Basak, Mousumi Das; Separating Stars from Quasars: Machine Learning Investigation Using Photometric Data, Astronomy and Computing (Elsevier–I.F: 3.1, ScimagoJR Q1), 29,September 2019, https://doi.org/10.1016/j.ascom.2019.100313
20. Dey, Sudeepa; Mathur, Archana ; DAYASAGAR, B.S; Saha, S; ALIS: A Novel Metric in Lineage Independent Evaluation of Scholars; Journal of Information Science (SciMagoJR Q1, I.F: 6.8), March 2022; DOI: https://doi.org/10.1177/01655515211039188
21. S.R.Dey & S.Saha, Recruitment boosted Epidemiological model for qualitative study of scholastic influence network, Journal of Scientometric Research (Wolter-Kluwer, ScimagoJR Q2), March 2021
1. Snigdha Sen, S. Saha, Pavan Chakraborty and Krishna Pratap Singh; A fast and robust Photometric redshift forecasting method using Lipschitz adaptive learning rate; ICONIP 2022 (accepted) (CORE B)
2. Ishita Mediratta, S.Saha and Shubhad Mathur; LipARELU: ARELU Networks aided by Lipschitz Acceleration (IJCNN 2021-CORE A)
3. Kanchan Jha, Sriparna Saha and S.Saha; Prediction of Protein-Protein Interactions using Deep Multi-Modal Representations (IJCNN 2021-CORE A)
4. S.Saha, Archana Mathur, Aditya Pandey and Harshith Arun Kumar; DiffAct: A Unifying framework for activation functions (IJCNN 2021-CORE A)
5. Urvil Nileshbhai Jivani, Omatharv Bharat Vaidya, Anwesh Bhattacharya and S.Saha; A Swarm Variant for the Schrodinger Solver; (IJCNN 2021-CORE A)
6. S. Sen, S.Saha, P.Chakraborty, K.P Singh; Implementation of neural network regression model for faster redshift analysis on cloud-based spark platform; 34th IEEE International Conference on Industrial, Engineering Other Applications of Applied Intelligent Systems (IEA/AIE-CORE B), 2021
7. J.Sarkar, S.Sarkar, S.Saha, S.Das; d-BTAI: the dynamic-Binary Tree Based Anomaly Identification Algorithm for Industrial Systems; 34th IEEE International Conference on Industrial, Engineering Other Applications of Applied Intelligent Systems (IEA/AIE-CORE B), 2021
8. Rohan Mohapatra, Snehanshu Saha, Soma S. Dhavala, AdaSwarm: A Novel PSO optimization Method for the Mathematical Equivalence of Error Gradients; Sept 2020; https://arxiv.org/abs/2006.09875
9..Snehanshu Saha, Tejas Prashanth, Suraj Aralihalli, Sumedh Basarkod, T.S.B Sudarshan and Soma S Dhavala; LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks; International Joint Conference on Neural Networks, July 2020; DOI: 10.1109/IJCNN48605.2020.9207650
10. Shailesh Sridhar, Snehanshu Saha, Azhar Shaikh, Rahul Yedida and Sriparna Saha; Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets; International Joint Conference on Neural Networks , July 2020; DOI: 10.1109/IJCNN48605.2020.9207083
11. R, Reddy, Snehanshu Saha, S. Roy Dey, V. Raychoudhury; Recruitment Boosted Epidemiological Model for Qualitative Study of Scholastic Influence Network; SIAM Conference on Mathematics of Data Science, May 2020, Cincinnati, USA.
12. Snehanshu Saha, Nithin Nagaraj, Archana Mathur, Rahul Yedida; Evolution of Novel Activation Functions; SIAM Conference on Mathematics of Data Science, May 2020, Cincinnati, USA.
13. Shashank Sanjay Bhat, Prabu T, S Saha; RaFIDe: A Machine Learning based RFI free observation planner for the SKA Era; URSI GASS, Rome, Italy, 29 August - 5 September 2020
14. S.Silawal, V.Raychouri, S.Saha, Md Osman Gani, A Dynamic Taxi Ride Sharing System Using Particle Swarm Optimization, IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS-CORE B), Feb-2021
15. Abhijeet Swain, Vaibhav Ganatra, S.Saha, Archana Mathur and Rekha Phadke; p-LSTM: An explainable LSTM architecture for Glucose Level Prediction; ICONIP 2022 (CORE B)
16. A. Bhattacharya, S.Saha, N. Nagaraj; Fairly Constricted Multi-Objective Particle Swarm Optimization; ICONIP 2022 (CORE B)
17. Vaidya, O, D’Souza,R., S.Saha, S. Dhavala, S. Das; HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer; ICONIP 2022 (CORE B)
Margarita Safonova, Snehanshu Saha, Jayant Murthy, Madhu Kashyap, C. Sivaram, Suryoday Basak, Surbhi Agrawal, Kakoli Bora, Pros and Cons of Classification of Exoplanets: in Search for the Right Habitability Metric, Astrobiology Newsletter, Vol 11, 2018.
Surbhi Agrawal, Margarita Safonova, Kakoli Bora, Suryoday Basak and Snehanshu Saha, Note on Proxima Centauri b: Theoretical validation of potential habitability via CD-HPF, Astrobiology Newsletter, Vol 10(4), 2017.
Basak, Saha et al., Star Galaxy Separation using and Asymmetric Adaboost, October 2016, DOI: 10.13140/RG.2.2.20538.59842, 10/2016
Agrawal, Saha et al., Exoplanets: Machine Learning Exploration via Mining and Automatic Labeling of the Habitability Catalog, October 2016, DOI: 10.13140/RG.2.2.17883.16165, 10/2016
Campus
BITSAT
Research & Innovation
Departments
Important Contacts
Divisions
Explore BITS
