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About the database ScholarIQanswers from OpenAlex & ORCID
How has Muhammad Kashif Shahzad's publication output changed over time?
ScholarIQpublication output · 2015–2023
Output grew50% over the shown period — from 2 works in 2015 to 3 in 2023.
2
1
1
3
3
20152017202020222023
What are the most-cited papers on Muhammad Kashif Shahzad?
ScholarIQmost cited works
A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification
Natasha Nigar, Muhammad Umar, Muhammad Kashif Shahzad, Shahid Islam, Douhadji Abalo
IEEE Access. 2022161 CitationsOPEN ACCESS
Staff retention: a factor of sustainable competitive advantage in the higher education sector of Pakistan
Akasha Butt, Rab Nawaz Lodhi, Muhammad Kashif Shahzad
S162196882. 202044 Citations
Failure Prediction Methodology for Improved Proactive Maintenance using Bayesian Approach ★ ★The authors gratefully acknowledge STMicroelectronics for their support and provision of data for TT case study. The authors also acknowledge European project INTEGRATE and region RhoneAlpes for ongoing Research.
Asma Abu-Samah, Muhammad Kashif Shahzad, Éric Zamaï, Anis Ben Said
IFAC-PapersOnLine. 201541 CitationsOPEN ACCESS
IoMT Meets Machine Learning: From Edge to Cloud Chronic Diseases Diagnosis System
Natasha Nigar, Abdul Jaleel, Shahid Islam, Muhammad Kashif Shahzad, Emmanuel Ampoma Affum
Journal of Healthcare Engineering. 202327 CitationsOPEN ACCESS
Experts’ knowledge renewal and maintenance actions effectiveness in high-mix low-volume industries, using Bayesian approach
Anis Ben Said, Muhammad Kashif Shahzad, Éric Zamaï, Stéphane Hubac, Michel Tollenaere
Cognition Technology & Work. 201517 Citations
Related on ScholarIQ
Shandong University
Institution
A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification
Paper
Staff retention: a factor of sustainable competitive advantage in the higher education sector of Pakistan
Paper
Failure Prediction Methodology for Improved Proactive Maintenance using Bayesian Approach ★ ★The authors gratefully acknowledge STMicroelectronics for their support and provision of data for TT case study. The authors also acknowledge European project INTEGRATE and region RhoneAlpes for ongoing Research.
Paper
IoMT Meets Machine Learning: From Edge to Cloud Chronic Diseases Diagnosis System
Paper
Experts’ knowledge renewal and maintenance actions effectiveness in high-mix low-volume industries, using Bayesian approach
Paper