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Arepalli Gopi
ResearcherPublications, citations & collaboration network
Arepalli Gopi is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 17 works, 38 citations, an h-index of 3 and an i10-index of 2.
17
Works
38
Citations
3
h-index
2
i10-index
IDs:OpenAlex
How has Arepalli Gopi's publication output changed over time?
ScholarIQpublication output · 2018–2024
Output grew100% over the shown period — from 1 works in 2018 to 2 in 2024.
1
2
5
2
2018202220232024
What are the most-cited papers on Arepalli Gopi?
ScholarIQmost cited works
Survey of privacy and security issues in IoT
Arepalli Gopi, M. Kameswara Rao
S4210200385. 201813 CitationsOPEN ACCESS
Automated Deep Learning with Wavelet Neural Network based Rice Plant Classification
V. Rekha, L. Venkateswara Reddy, Sachin Vasant Chaudhari, Arepalli Gopi, C. Nithiya, Shaik Khaleel Ahamed
202310 Citations
Intelligent System for Early Diagnosis of Skin Cancer using Bilinear CNN and Soft Attention Approach
Aditya Kumar Singh Pundir, Md. Abul Ala Walid, Pravin Prakash Adivarekar, Arepalli Gopi, V. Malathy, Kamlesh Singh
20237 Citations
Implementation of Blur Image to Sharp Image Conversion using Laplacian Approach
S. S. Aravinth, Arepalli Gopi, Gummadi Likhith Chowdary, Kvr Bhagavath, Desaboina Rahul Srinivas
2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). 20223 Citations
Disclosing the Potential of Deep Learning in Cassava Leaf Disease Analysis by using CNN and Neural Networks Approach
Arepalli Gopi, Sudha L. R, Iwin Thanakumar Joseph S
20243 Citations
Related on ScholarIQ
Koneru Lakshmaiah Education Foundation
Institution
Survey of privacy and security issues in IoT
Paper
Automated Deep Learning with Wavelet Neural Network based Rice Plant Classification
Paper
Intelligent System for Early Diagnosis of Skin Cancer using Bilinear CNN and Soft Attention Approach
Paper
Implementation of Blur Image to Sharp Image Conversion using Laplacian Approach
Paper
Disclosing the Potential of Deep Learning in Cassava Leaf Disease Analysis by using CNN and Neural Networks Approach
Paper