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About the database ScholarIQanswers from OpenAlex & ORCID
How has Md Asifur Rahman's publication output changed over time?
ScholarIQpublication output · 2010–2023
Output grew100% over the shown period — from 1 works in 2010 to 2 in 2023.
1
1
1
1
4
2
201020172019202120222023
What are the most-cited papers on Md Asifur Rahman?
ScholarIQmost cited works
A Pharmacological and Phytochemical Evaluation of Medicinal Plants Used by the Harbang Clan of the Tripura Tribal Community of Mirsharai Area, Chittagong District, Bangladesh
Mohammed Rahmatullah, Md Asifur Rahman, Md Shahadat Hossan, M. Taufiq-ur-Rahman, Rownak Jahan, Md. Ariful Haque Mollik
The Journal of Alternative and Complementary Medicine. 201042 Citations
Plant Disease Detection Based on YOLOv3 and YOLOv4
Apu Shill, Md Asifur Rahman
2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI). 202136 Citations
Augmented Reality for Learning Calculus: A Research Framework of Interactive Learning System
Md Asifur Rahman, Lew Sook Ling, Ooi Shih Yin
Lecture notes in electrical engineering. 201915 Citations
An adaptive background modeling based on modified running Gaussian average method
Md Asifur Rahman, Boshir Ahmed, Md. Ali Hossian, Md. Nazrul Islam Mondal
20179 Citations
A novel policy-graph approach with natural language and counterfactual abstractions for explaining reinforcement learning agents
Tongtong Liu, Joe McCalmon, Thai Le, Md Asifur Rahman, Dongwon Lee, Sarra Alqahtani
S5405189. 20235 Citations
Related on ScholarIQ
American International University-Bangladesh
Institution
A Pharmacological and Phytochemical Evaluation of Medicinal Plants Used by the Harbang Clan of the Tripura Tribal Community of Mirsharai Area, Chittagong District, Bangladesh
Paper
Plant Disease Detection Based on YOLOv3 and YOLOv4
Paper
Augmented Reality for Learning Calculus: A Research Framework of Interactive Learning System
Paper
An adaptive background modeling based on modified running Gaussian average method
Paper
A novel policy-graph approach with natural language and counterfactual abstractions for explaining reinforcement learning agents
Paper