Gaurav Nanda

For full list of publications, please see my Google Scholar Page

1. Catchpoole, J., Nanda, G., Vallmuur, K., Nand, G., & Lehto, M. (2022). Application of a Machine Learning–Based Decision Support Tool to Improve an Injury Surveillance System Workflow. Applied clinical informatics, 13(03), 700-710.(link)
2. Nanda, G., Vallmuur, K., & Lehto, M. (2020). Intelligent human-machine approaches for assigning groups of injury codes to accident narratives. Safety science, 125, 104585. (link)
3. Nanda, G., Vallmuur, K., & Lehto, M. (2018). Improving Autocoding Performance of Rare Categories in Injury Classification: Is More Training Data or Filtering the Solution? Accident Analysis and Prevention, 110, 115-127. (link)
4. Nanda, G., Grattan, K. M., Chu, M. T., Davis, L. K., & Lehto, M. R. (2016). Bayesian decision support for coding occupational injury data. Journal of Safety Research, 57, 71-82. (link)

1. Han, Y., Nanda, G., & Moghaddam, M. (2023). Attribute-Sentiment-Guided Summarization of User Opinions From Online Reviews. Journal of Mechanical Design, 145(4), 041401. ( link)
2. Senarathne, L. R., Nanda, G., & Sundararajan, R. (2022). Influence of building parameters on energy efficiency levels: a Bayesian network study. Advances in Building Energy Research, 16(6), 780-805. ( link)
3. Richards, G., Athinarayanan, R., Balakreshnan, B., Bennett, J., Weatherly, A., Zaccaria,J., Zink, P., Yamasaki, J., Newell, B., Nanda, G., & Mao, H. (2021). A Collaboratively Developed Platform to Introduce Fundamentals of IoT and IIoT. SSRN 38596722. (link)
4. Balakreshnan, B., Richards, G., Nanda, G., Mao, H., Athinarayanan, R., & Zaccaria, J. (2020). PPE Compliance Detection using Artificial Intelligence in Learning Factories. Procedia Manufacturing, 45, 277-282. (link)
5. Nanda, G., Tan, J., Auyeung, P., & Lehto, M. (2013). Improving Efficiency of Organizational Reliability Engineering Knowledge using Keywords. Institute of Industrial Engineers (IIE) Annual Conference. San Juan, USA. (link)

1. Nanda, G., Wei, S., Katz, A., Brinton, C., & Ohland, M. (2022, August). Work-in-Progress: Using Latent Dirichlet Allocation to uncover themes in student comments from peer evaluations of teamwork. In 2022 ASEE Annual Conference & Exposition. (link)
2. Nanda, G., Douglas, K. A., Waller, D. R., Merzdorf, H. E., & Goldwasser, D. (2021). Analyzing Large Collections of Open-Ended Feedback from MOOC Learners Using LDA Topic Modeling and Qualitative Analysis. IEEE Transactions on Learning Technologies, vol. 14, no. 2, pp. 146-160. (link)
3. Nanda, G., Douglas, K. A. (2019). Decision Support System for Categorizing MOOC Discussion Forum Posts Using Machine Learning. Educational Data Mining (EDM 2019), Montreal, Canada. (link)
4. Nanda, G., Hicks, N. M., Waller, D. R., Douglas, K. A., Goldwasser, D. (2018).Understanding Learners' Opinion about Participation Certificates in Online Courses using Topic Modeling. Educational Data Mining (EDM 2018), Buffalo, NY, USA. (link)
5. Nanda, G., Lehto, M., & Nof, S. (2014). User Requirement Analysis for an Online Collaboration Tool for Senior Industrial Engineering Design Course. Human Factors and Ergonomics in Manufacturing & Service Industries, Vol. 24, Issue 5, 557-573. (link)

1. Nanda, G., Vallmuur, K., & Lehto, M. (2019). Semi-automated Text Mining Strategies for Identifying Rare Causes of Injuries from Emergency Room Triage Data. IISE Transactions on Healthcare Systems Engineering, 9 (2), 157-171. (link)
2. Li, M., Nanda, G., & Sundararajan, R. (2021). Evaluating Different Machine Learning Models for Predicting the Likelihood of Breast Cancer. Advanced Aspects of Engineering Research Vol. 2, 132-142. (link)