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How has Abdulhalim Dandoush's publication output changed over time?
ScholarIQpublication output · 2007–2024
Output grew400% over the shown period — from 1 works in 2007 to 5 in 2024.
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1
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5
200720092018202020232024
What are the most-cited papers on Abdulhalim Dandoush?
ScholarIQmost cited works
A Comprehensive Survey on the E2E 5G Network Slicing Model
Mohammed Chahbar, Gladys Diaz, Abdulhalim Dandoush, Christophe Cérin, Kamal Ghoumid
S173527311. 2020174 Citations
Unveiling the Metaverse: Exploring Emerging Trends, Multifaceted Perspectives, and Future Challenges
Mueen Uddin, Selvakumar Manickam, Hidayat Ullah, Muath Obaidat, Abdulhalim Dandoush
IEEE Access. 202381 CitationsOPEN ACCESS
Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification
Irfan Ali Kandhro, Selvakumar Manickam, Kanwal Fatima, Mueen Uddin, Urooj Ali Malik, Anum Naz, Abdulhalim Dandoush
Heliyon. 202475 CitationsOPEN ACCESS
Exploring the convergence of Metaverse, Blockchain, and <scp>AI</scp>: A comprehensive survey of enabling technologies, applications, challenges, and future directions
Mueen Uddin, Muath Obaidat, Selvakumar Manickam, Shams Ul Arfeen Laghari, Abdulhalim Dandoush, Hidayat Ullah, Syed Sajid Ullah
S2505707916. 202455 CitationsOPEN ACCESS
Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification
Mudassir Iftikhar, Irfan Ali Kandhro, Neha Kausar, Asadullah Kehar, Mueen Uddin, Abdulhalim Dandoush
Artificial Intelligence Review. 202447 CitationsOPEN ACCESS
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A Comprehensive Survey on the E2E 5G Network Slicing Model
Paper
Unveiling the Metaverse: Exploring Emerging Trends, Multifaceted Perspectives, and Future Challenges
Paper
Performance evaluation of E-VGG19 model: Enhancing real-time skin cancer detection and classification
Paper
Exploring the convergence of Metaverse, Blockchain, and <scp>AI</scp>: A comprehensive survey of enabling technologies, applications, challenges, and future directions
Paper
Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification
Paper