Baidya Saha

Education:

Ph.D, Computing Science, University of Alberta, Canada

Postdoctoral:

Wake Forest School of Medicine, USA
University of Calgary, Canada

Research Interests:

Artificial intelligence
Machine learning
Computer Vision
Industry 4.9

Selected Publications:

A. Bahuguna, D. Yadav, A. Senapati, and B. N. Saha, “A Unified Deep Neuro-fuzzy Approach for COVID-19 Twitter Sentiment Classification,” Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4587-4597, 2022.

A. Bahuguna, D. Yadav, A. Senapati, and B. N. Saha, “kNN-SVM with Deep Features for COVID-19 Pneumonia Detection from Chest X-ray,” In: Rushi Kumar, B., Ponnusamy, S., Giri, D., Thuraisingham, B., Clifton, C.W., Carminati, B. (eds) Mathematics and Computing. ICMC 2022. Springer Proceedings in Mathematics & Statistics, vol 415. Springer, Singapore. https://doi.org/10.1007/978-981-19-9307-7_9

J. Romero-Hdz, B. N. Saha, S. Tstutsumia, R. Fincatoa, and G. Toledo, “Incorporating domain knowledge into reinforcement learning to expedite welding sequence optimization,” in Engineering Applications of Artificial Intelligence, vol. 91, 2020, pp. 1–10.

B. N. Saha, N. Ray, S. McArdle, and K. Ley, “Selecting the optimal sequence for deformable registration of microscopy image sequences using a two-stage mst-based clustering algorithm,” in Medical Image Computing and Computer Assisted Intervention -MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-
13, 2017, Proceedings. Springer International Publishing, 2017.

B. N. Saha, G. Kunapuli, N. Ray, J. A. Maldjian, and S. Natarajan, “AR-Boost: Reducing overfitting by a robust data-driven regularization strategy,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD). Springer, 2013.