Innovative Research Award
Chetanpal Singh
RMIT, Australia
Chetanpal Singh is affiliated with RMIT in Australia. The profile records eight documents, 126 citations, and an h-index of four, with machine learning listed as the principal subject area. The highlighted research spans medical imaging, agricultural disease detection, and data visualization, with publications addressing applied computational methods across different domains.[1][2][3]
Abstract
Chetanpal Singh is an RMIT-affiliated researcher in Australia whose supplied profile identifies machine learning as a subject area. The record lists eight documents, 126 citations, and an h-index of four. His highlighted publications cover multimodal learning for cancer analysis, hybrid deep learning for cotton disease detection, and data visualization for audit efficiency and risk management. These works illustrate computational methods across healthcare, agriculture, and auditing. The profile is supported by Scopus and ORCID identifiers, while the cited publications provide traceable evidence of research activity. The information presented here is descriptive and should be considered alongside official award criteria and bibliographic records.[1][2][3]
Keywords
Machine learning; deep learning; multimodal learning; medical imaging; cancer detection; agricultural artificial intelligence; cotton disease detection; BERT; ResNet; particle swarm optimization; data visualization; audit efficiency; risk management; computer vision; research impact.
Introduction
Chetanpal Singh’s research profile is associated with machine learning and applied data-driven methods across medical imaging, agricultural disease detection, and data visualization. His listed publications include studies combining deep learning architectures, optimization, and analytical visualization. These works span healthcare, agriculture, and auditing applications, illustrating cross-domain use of computational methods research.[1][2][3]
Research Profile
Chetanpal Singh is affiliated with RMIT in Australia and is identified by Scopus Author ID 57197208396 and ORCID 0000-0001-6246-444X. The supplied profile records eight documents, 126 citations, and an h-index of four. His stated subject area is machine learning, with publications addressing artificial intelligence, deep learning, computer vision, and analytics.[1][2][3]
Research Contributions
The documented contributions represented by the supplied publications include multimodal deep learning for medical imaging, hybrid BERT-ResNet-PSO modelling for cotton disease recognition, and visualization-based approaches to audit efficiency and risk management. Together, these studies demonstrate applications of machine learning, neural architectures, optimization, and visual analytics to domain-specific problems and decision-support contexts.[1][2][3]
Publications
The publication record supplied for this profile contains three works. Singh and colleagues reported a graph-aware and sequence-aware multimodal framework for cancer analysis in Journal of Imaging in 2026. A 2025 Applied Sciences article addressed cotton plant disease detection using BERT-ResNet-PSO. A 2023 handbook chapter examined data visualization for auditing.[1][2][3]
Research Impact
The cited publications indicate research activity across several application domains rather than a single narrowly defined problem. The medical imaging study addresses cancer detection using graph, sequence, and multimodal learning; the agricultural study addresses cotton disease classification; and the auditing chapter discusses visualization for analytical procedures and risk management practices.[1][2][3]
Award Suitability
For an academic recognition profile, the documented record provides identifiable evidence through publications, author identifiers, citation information, and research topics. The listed work demonstrates application-oriented machine learning across healthcare, agriculture, and auditing. Any award assessment should additionally consider the event’s published eligibility criteria, nomination requirements, evidence standards, and comparison process.[1][2][3]
Conclusion
Chetanpal Singh’s research record presents a cross-domain machine learning profile supported by publications, author identifiers, and bibliometric information. The highlighted studies cover medical imaging, agricultural disease detection, and audit visualization. These materials can support a academic recognition profile, while final recognition depends on criteria and process of the awarding organization.[1][2][3]
External Links
References
- Singh, C., Wibowo, S., Grandhi, S., & Mandala, S. (2026). Graph-Aware and Sequence-Aware Multimodal Deep Learning Framework for Cancer Detection and Risk Analysis from Medical Imaging. Journal of Imaging, 12(9), 431.
https://doi.org/10.3390/jimaging12090431https://www.mdpi.com/2313-433X/12/9/431
- Singh, C., Wibowo, S., & Grandhi, S. (2025). A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO. Applied Sciences, 15(13), 7075.
https://doi.org/10.3390/app15137075https://www.mdpi.com/2076-3417/15/13/7075
- Ferdous, L. T., Singh, C., & Rana, T. (2023). A Picture Is Worth a Thousand Words: Audit Efficiency and Risk Management Through Data Visualization. In Handbook of Big Data and Analytics in Accounting and Auditing (pp. 17–39). Springer.
https://doi.org/10.1007/978-981-19-4460-4_2Scopus record