Upload Records Snowball Search Search OpenAlex
About the database ScholarIQanswers from OpenAlex & ORCID
How has Aakash Shah's publication output changed over time?
ScholarIQpublication output · 2011–2022
Output grew0% over the shown period — from 1 works in 2011 to 1 in 2022.
1
1
1
1
2
2
1
1
20112014201720182019202020212022
What are the most-cited papers on Aakash Shah?
ScholarIQmost cited works
ResTS: Residual Deep interpretable architecture for plant disease detection
Dhruvil Shah, Vishvesh Trivedi, Vinay Sheth, Aakash Shah, Uttam Chauhan
Information Processing in Agriculture. 2021107 CitationsOPEN ACCESS
Descriptive Analysis of State and Federal Spine Surgery Malpractice Litigation in the United States
Nitin Agarwal, Raghav Gupta, Prateek Agarwal, Pravin Matthew, Richard Wolferz, Aakash Shah, Nimer Adeeb, Arpan V. Prabhu, Adam S. Kanter, David O. Okonkwo, D. Kojo Hamilton
Spine. 201763 Citations
Maximum power point tracking in wind energy conversion system using radial basis function based neural network control strategy
Ravinder Kumar, Hanuman P. Agrawal, Aakash Shah, Hari Om Bansal
S2764877105. 201950 Citations
Self-folding immunoprotective cell encapsulation devices
Christina L. Randall, Yevgeniy V. Kalinin, Mustapha Jamal, Aakash Shah, David H. Gracias
S80729884. 201141 Citations
Early Detection of Alzheimer's Disease Using Various Machine Learning Techniques: A Comparative Study
Aakash Shah, Dhruvi Lalakiya, Shekha Desai, Shreya Shreya, Vibha Patel
2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184). 202025 Citations
Related on ScholarIQ
University of Maryland, Baltimore
Institution
ResTS: Residual Deep interpretable architecture for plant disease detection
Paper
Descriptive Analysis of State and Federal Spine Surgery Malpractice Litigation in the United States
Paper
Maximum power point tracking in wind energy conversion system using radial basis function based neural network control strategy
Paper
Self-folding immunoprotective cell encapsulation devices
Paper
Early Detection of Alzheimer's Disease Using Various Machine Learning Techniques: A Comparative Study
Paper