Khalfalla Awedat

According to our database1, Khalfalla Awedat authored at least 12 papers between 2016 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2022
COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach.
Int. J. Comput. Vis. Image Process., 2022

Adaptive DBSCAN LiDAR Point Cloud Clustering For Autonomous Driving Applications.
Proceedings of the 2022 IEEE International Conference on Electro Information Technology, 2022

2021
Novel Robust Augmentation Approach Based on Sensing Features for Data Classification.
IEEE Access, 2021

2020
COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches.
CoRR, 2020

Compressive Sensing Evaluation Strategy For Prostate Cancer Classification.
Proceedings of the 2020 IEEE International Conference on Electro Information Technology, 2020

2019
Efficient Face Recognition Using Regularized Adaptive Non-Local Sparse Coding.
IEEE Access, 2019

IR Thermal Image Analysis: An Efficient Algorithm for Accurate Hot-Spot Fault Detection and Localization in Solar Photovoltaic Systems.
Proceedings of the 2019 IEEE International Conference on Electro Information Technology, 2019

2018
Modular Sparse Representation Based Classification Approach for Robust Face Recognition.
Proceedings of the 2018 IEEE International Conference on Electro/Information Technology, 2018

2017
Prostate cancer recognition based on mass spectrometry sensing data and data fingerprint recovery.
Biomed. Signal Process. Control., 2017

Sparse representation classification based linear integration of ℓ<sup>1</sup>-norm and ℓ<sup>2</sup>-norm for robust face recognition.
Proceedings of the IEEE International Conference on Electro Information Technology, 2017

Compressive sensing of jointly sparse signals as a method for dimensionality reduction of mass spectrometry data.
Proceedings of the IEEE International Conference on Electro Information Technology, 2017

2016
Mass spectrometry sensing data for robust cancer classification.
Proceedings of the 2016 IEEE International Conference on Electro Information Technology, 2016


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