Research
Below, you’ll find publications and under review papers by year. For questions on any specific publication, feel free to email me.
Published Research
2024:
S.Dakshit, “A Discussion on Potential of RAG in Computer Science Higher Education”; ACM SIGITE, 2024
A.Morales, B.Baber, M.Castillo, C.Gallegos, S.Dakshit, “Functional Movement (FMove) Tele-Screening Application”; Journal of Biomedical Science Instrumentation, 2024
A.Morales, B.Baber, M.Castillo, C.Gallegos, S.Dakshit, “Functional Movement (FMove) Tele-Screening Application Abstract”; SBEC, 2024
Multiview Outlier Filtered Pediatric Heart Sound Classification, International Journal of Advanced Computer Science and Applications, May 2024.
Abstaining ECG Classifiers Through Explainable Prototypical Spaces; S. Dakshit; IEEE International Conference on Healthcare Informatics, 2024
Investigation Of Augmentation Methods For Deep Learning ECG Classification; N. Balasubramania (M.S. Student), S.Dakshit; International Conference on Artificial Intelligence in Medicine, 2024
2023:
“Bias Analysis in Healthcare Time-Series (BAHT) Decision Support Systems from Meta-Data”, S. Dakshit, S. Dakshit, N. Khargonkar and B. Prabhakaran; Journal of Healthcare Informatics, 2023
CVAE-based Generator for Variable Length Synthetic ECG; S. Dakshit and B. Prabhakaran; IEEE International Conference on Healthcare Informatics, June 2023
Twelve Lead Double Stacked Generalization for ECG Classification; S. Dakshit and B. Prabhakaran; IEEE International Conference on Healthcare Informatics, June 2023
2022:
“Core-set Selection Using Metrics-based Explanations (CSUME) for multiclass ECG”, S. Dakshit, B. M. Maweu, S. Dakshit, and B. Prabhakaran, IEEE International Conference on Healthcare Informatics, June 2022
2021:
“CEFEs: A CNN Explainable Framework for ECG Signals”, B. M. Maweu1, S. Dakshit1, R. Shamsuddin, and B. Prabhakaran, Artificial Intelligence in Medicine, Volume 115 (102509), May 2021. https://doi.org/10.1016/j.artmed.2021.102059 (Joint First Author)
“Reinforcement Learning Framework for Navigation problems using LiDAR Scan-Based Virtual Reality”, Sagnik Dakshit, Hiranya Kumar, Chris Young Jin Jung, Ammar Hasan Mehboob Nanjiani, Marshal Renfrow, Brian To, Briscoe Fletcher, Liam Heffernan, and Balakrishnan Prabhakaran, Machine Learning for Mobile Robot Navigation in the Wild (ML4NAV) Symposium as part of the AAAI Spring Symposium, 2021. (Peer-reviewed Short Paper).
"Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks," B. M. Maweu, R. Shamsuddin, S. Dakshit and B. Prabhakaran, IEEE Transactions on Instrumentation and Measurement, https://doi.org/10.1109/TIM.2021.3077049 2.
2020:
“SSBC 2020: Sclera segmentation benchmarking competition in the mobile environment.”, Vitek, M., Das, A., Pourcenoux, Y., Missler, A., Paumier, C., Das, S., ... & Štruc, V. (2020, September). In 2020 IEEE International Joint Conference on Biometrics (IJCB) (pp. 1-10). IEEE. (Benchmarking Competition)
Preprints
N. Balasubramania, S.Dakshit, “Investigation in the Use of LLM for Self-Diagnosis of Medical Conditions”; ACM Transactions on Healthcare, 2024 (Under Review and Preprint)
“Near Real-time Forgery Detection and Localization in RGB and 3D LiDAR Data from Autonomous Vehicles”, S. Mohammadpour, S. Dakshit, and B. Prabhakaran, Under Review, ACM Transactions on Multimedia Computing, Communications, and Applications 2022