Skip to main content
Muhammad Awais Ashraf on Muck Rack

Muhammad Awais Ashraf

Pakistan
As seen in: Nature, MDPI, Taylor & Francis Online
Covers:  creative writing Content writing. Essay writing. Ghost writing. Copywritting. Press release. Media Alert. News Alert. Proofreading Editing Articles Bloggs Business writing Resume

Get in touch with Muhammad

Contact Muhammad, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Muhammad Awais Ashraf’s Biography

Read Full Bio →

Well. I am Muhammad Awais Ashraf. I am from Arifwala, Pakistan. I am studying in Government college University Faislabad. I am a student of Microbiology. I am 21 years old. I am a freelancer. I have certification in Freelancing and creative writing and various other seminar and webinar of health related events.

Articles

UAV-based tree species classification using DenseNet121 with transfer learning on visible light images

|
By Wei Zeng, Jianlong Huang, Kavimbi Chipusu, Muhammad Awais Ashraf, Xianxian Luo, Jianbing Xiahou, Songya Xu
| Nature Verified
Abstract This study introduces a cutting-edge approach to tree species classification in environmental monitoring by leveraging UAV-based visible light imagery. Focusing exclusively on visible light images captured by unmanned aerial vehicles (UAVs), we address the limitations of traditional classification methods with the powerful DenseNet121 deep learning architecture.
Open in Who Shared Issue with article?

Industrial logistics optimisation in transportation with fuzzing techniques and reverse engineering to secure proprietary traffic control systems

|
By Muhammad Awais Ashraf, Kelvin Wong, Hai-Chen Wang
| Taylor & Francis Online
About Your Privacy We process your data to deliver content or advertisements and measure the delivery of such content or advertisements to extract insights about our website. We share this information with our partners on the basis of consent and legitimate interest. You may exercise your right to consent or object to a legitimate interest, based on a specific purpose below or at a partner level in the link under each purpose.
Open in Who Shared View Duplicates (482) Issue with article?

Symmetry-Enhanced LSTM-Based Recurrent Neural Network for Oscillation Minimization of Overhead Crane Systems during Material Transportation

|
By Xu Cui, Kavimbi Chipusu, Muhammad Awais Ashraf, Mudassar Riaz
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Open in Who Shared Issue with article?
See all 4 articles