FlexibleNet: A New Lightweight Convolutional Neural Network Model
  • View Times: 30
  • |
  • Release Date: 2024-08-23
  • Light CNN
  • Remote Sensing
  • Carbon sequestration
  • Environment
Video Introduction

The video is a presentation that explains the research work that was published in MDPI Remote Sensing Journal in 2023 [1] . It explains in detail all the steps necessary to understand the problem and the objectives. Moreover, it helps in capture the important points behind the research and to get advantage of all resources available for continuing this research.

 

References
  1. Mohamad M. Awad; FlexibleNet: A New Lightweight Convolutional Neural Network Model for Estimating Carbon Sequestration Qualitatively Using Remote Sensing. MDPI Remote Sensing 2023, 15, 272, .
Full Transcript
1000/1000
Hot Most Recent
©Text is available under the terms and conditions of the Creative Commons-Attribution ShareAlike (CC BY-SA) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Awad, M.M. FlexibleNet: A New Lightweight Convolutional Neural Network Model. Encyclopedia. Available online: https://encyclopedia.pub/video/video_detail/1342 (accessed on 15 November 2024).
Awad MM. FlexibleNet: A New Lightweight Convolutional Neural Network Model. Encyclopedia. Available at: https://encyclopedia.pub/video/video_detail/1342. Accessed November 15, 2024.
Awad, Mohamad Mustafa. "FlexibleNet: A New Lightweight Convolutional Neural Network Model" Encyclopedia, https://encyclopedia.pub/video/video_detail/1342 (accessed November 15, 2024).
Awad, M.M. (2024, August 23). FlexibleNet: A New Lightweight Convolutional Neural Network Model. In Encyclopedia. https://encyclopedia.pub/video/video_detail/1342
Awad, Mohamad Mustafa. "FlexibleNet: A New Lightweight Convolutional Neural Network Model." Encyclopedia. Web. 23 August, 2024.
ScholarVision Creations
Feedback