 | Up a level |
Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Berton, B., Bouzekri, E., Singh, A., & Doyon-Poulin, P. (2026). Human–Autonomy Teaming for Future Commercial Aviation Concepts of Operations. In Boy, G. A. (ed.), Handbook of Sociotechnical Systems (pp. 428-446). External link
Lescieux, C., Singh, A., & Doyon-Poulin, P. (2025). When It Goes Right, When It Goes Wrong: Lessons Learned from Automation in Aviation. In Coursaris, C. K., Beringer, J., Léger, P.-M., & Öz, B. (eds.), The Design of Human-Centered Artificial Intelligence for the Workplace (pp. 329-350). External link
Mahu, A.-M., Singh, A., Tambon, F., Ouellette, B., Delisle, J.-F., Paul, T., Khomh, F., Marois, A., & Doyon-Poulin, P. (2024, June). Validation of vigilance decline capability in a simulated test environment: a preliminary step towards neuroadaptive control [Paper]. 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024), Nice, France. Available
Singh, A., Nawayseh, N., Doyon-Poulin, P., Milosavljevic, S., Rakheja, S., Kumar, Y., Dewangan, K. N., Trask, C., & Samuel, S. (2026). Multi-model machine learning for predicting tractor operator discomfort caused by whole-body vibration. Computers and Electronics in Agriculture, 243, 111375 (15 pages). External link
Singh, A., Nawayseh, N., Doyon-Poulin, P., Milosavljevic, S., Dewangan, K. N., Kumar, Y., & Samuel, S. (2025). Comparative analysis of classical and ensemble models for predicting whole body vibration induced lumbar spine stress. A case study of agricultural tractor operators. International Journal of Industrial Ergonomics, 108, 103775 (12 pages). Available
Singh, A., Nawayseh, N., & Rakheja, S. (2025). Ensemble modeling for predicting head vibration based on driving seating conditions: Towards adaptive seating systems. Engineering Applications of Artificial Intelligence, 145, 110174 (13 pages). External link
Singh, J., Singh, A., Singh, H., & Doyon-Poulin, P. (2025). Implementation and evaluation of a smart machine monitoring system under industry 4.0 concept. Journal of Industrial Information Integration, 43, 15 pages. External link
Singh, A., Nawayseh, N., Dhabi, Y. K., & Samuel, S. (2024). Transforming farming with intelligence : smart vibration monitoring and alert system. Journal of Engineering Research, 12(2), 190-199. Available
Singh, J., Ahuja, I. S., Singh, H., & Singh, A. (2023). Application of Quality 4.0 (Q4.0) and Industrial Internet of Things (IIoT) in Agricultural Manufacturing Industry. Agriengineering, 5(1), 537-565. External link
Singh, A., Nawayseh, N., Singh, H., Dhabi, Y. K., & Samuel, S. (2023). Internet of agriculture: Analyzing and predicting tractor ride comfort through supervised machine learning. Internet of agriculture: Analyzing and predicting tractor ride comfort through supervised machine learning, 125, 16 pages. External link
