SCHOLARS@TAMU

Yu, M., Pasman, H., Erraguntla, M., Quddus, N., & Kravaris, C. (2022). A framework to identify and respond to weak signals of disastrous process incidents based on FRAM and machine learning techniques. Process Safety and Environmental Protection. 158, 98-114.

Erraguntla, M., Dave, D., Zapletal, J., Myles, K., Adelman, Z. N., Pohlenz, T. D., & Lawley, M. (2021). Predictive model for microclimatic temperature and its use in mosquito population modeling. Scientific Reports. 11(1), 18909.

Dave, D., DeSalvo, D. J., Haridas, B., McKay, S., Shenoy, A., Koh, C. J., Lawley, M., & Erraguntla, M. (2021). Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction.. J Diabetes Sci Technol. 15(4), 842-855.

Yu, M., Erraguntla, M., Quddus, N., & Kravaris, C. (2021). A data-driven approach of quantifying function couplings and identifying paths towards emerging hazards in complex systems. Process Safety and Environmental Protection. 150, 464-477.

Curry, G. L., Moya, H., Erraguntla, M., & Banerjee, A. (2021). Transient Queueing Analysis for Emergency Hospital Management. IISE Transactions on Healthcare Systems Engineering. 1-20.

Qaraqe, M., Erraguntla, M., & Dave, D. (2021). AI and Machine Learning in Diabetes Management: Opportunity, Status, and Challenges. Multiple Perspectives on Artificial Intelligence in Healthcare. 129-141. Springer International Publishing.

Tomasulo, P., Erraguntla, M., & Kamel, H. (2012). Blood Donation: An Approach to Donor Vigilance. Hemovigilance. 75-98. Wiley.

Ramachandran, S., Erraguntla, M., & Benjamin, P. (2002). A Knowledge Based Framework for the Design of Soft-Computing Systems. GRID-BASED PROBLEM SOLVING ENVIRONMENTS. Intelligent Information Processing. 129-140. Springer US.

Islam, M. S., Qaraqe, M. K., Abbas, H. T., Erraguntla, M., & Abdul-Ghani, M. (2020). The Prediction of Diabetes Development: A Machine Learning Framework. 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME), 2020 IEEE 5th Middle East and Africa Conference on Biomedical Engineering (MECBME). 00, 1-6.

Islam, M. S., Qaraqe, M. K., Abbas, H. T., Erraguntla, M., Abdul-Ghani, M., & IEEE. (2020). The Prediction of Diabetes Development: A Machine Learning Framework. 2020 IEEE 5TH MIDDLE EAST AND AFRICA CONFERENCE ON BIOMEDICAL ENGINEERING (MECBME). 154-159.

Aljihmani, L., Abbas, H., Zhu, Y., Mehta, R. K., Sasangohar, F., Erraguntla, M., ... Qaraqe, K. (2019). Features of Physiological Tremor in Diabetic Patients. 2019 IEEE International Smart Cities Conference (ISC2), 2019 IEEE International Smart Cities Conference (ISC2). 00, 268-271.

Aljihmani, L., Zhuz, Y., Abbas, H. T., Mehta, R., Sasangohar, F., Erraguntla, M., Abbasi, Q. H., & Qaraqe, K. (2019). Spectral Analysis of Hand Tremors Induced During a Fatigue Test. 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). 00, 658-663.

Freeze, J., Erraguntla, M., & Verma, A. (2018). Data Integration and Predictive Analysis System for Disease Prophylaxis: Incorporating Dengue Fever Forecasts. Proceedings of the 51st Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences. 913-922.

SDG 3

Ziyadidegan, S., Razavi, M., Pesarakli, H., Javid, A. H., & Erraguntla, M. (2021). Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning.

Ziyadidegan, S., Razavi, M., Pesarakli, H., Javid, A., & Erraguntla, M. (2021). Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning.

SDG 3

Dave, D., Erraguntla, M., Lawley, M., DeSalvo, D., Haridas, B., McKay, S., & Koh, C. (2021). Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study (Preprint).

Zahed, K., Sasangohar, F., Mehta, R., Erraguntla, M., & Qaraqe, K. (2020). Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study (Preprint).

Abbas, H., Alic, L., Erraguntla, M., Ji, J., Abdul-Ghani, M., Abbasi, Q., & Qaraqe, M. (2019). Predicting long-term Type 2 Diabetes with Support Vector Machine using Oral Glucose Tolerance Test.