M
Muhammad Ramzan
Researcher at Islamia University
Publications - 240
Citations - 3285
Muhammad Ramzan is an academic researcher from Islamia University. The author has contributed to research in topics: Quantum decoherence & Quantum entanglement. The author has an hindex of 21, co-authored 218 publications receiving 1976 citations. Previous affiliations of Muhammad Ramzan include Umm al-Qura University & Information Technology University.
Papers
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Determinants of carbon emissions in Argentina: The roles of renewable energy consumption and globalization
Li Yuping,Muhammad Ramzan,Li Xincheng,Muntasir Murshed,Abraham Ayobamiji Awosusi,Sununu Ibrahim Bah,Tomiwa Sunday Adebayo +6 more
TL;DR: In this article, the authors evaluate the dynamic effects of globalization, renewable energy consumption, non-renewable energy consumption and economic growth on carbon-dioxide emission levels in Argentina over the 1970-2018 period.
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A Survey on State-of-the-Art Drowsiness Detection Techniques
Muhammad Ramzan,Hikmat Ullah Khan,Shahid Mahmood Awan,Amina Ismail,Mahwish Ilyas,Ahsan Mahmood +5 more
TL;DR: Overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.
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Emerging Applications of Porphyrins and Metalloporphyrins in Biomedicine and Diagnostic Magnetic Resonance Imaging
TL;DR: The most recent research and developments on metalloporphyrin-based materials in bio-imaging, drug delivery, and the determination of ferrochelatase in bone marrow indicating their prospective clinical applications are explored.
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Effect of Aloe vera gel coating enriched with Fagonia indica plant extract on physicochemical and antioxidant activity of sapodilla fruit during postharvest storage.
TL;DR: The addition of FI plant extract to AV gel coating could be a promising approach to prolong the shelf life and preserve the quality of sapodilla fruit during storage.
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Skin Cancer Detection: A Review Using Deep Learning Techniques
Mehwish Dildar,Shumaila Akram,Muhammad Irfan,Hikmat Ullah Khan,Muhammad Ramzan,Muhammad Ramzan,Abdur Rehman Mahmood,Soliman Ayed Alsaiari,Abdul Hakeem M Saeed,Mohammed Olaythah Alraddadi,Mater H. Mahnashi +10 more
TL;DR: A detailed systematic review of deep learning techniques for the early detection of skin cancer is presented in this article, where Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer.