Theo dõi
Ammar M. Okran
Ammar M. Okran
Email được xác minh tại urv.cat
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
Năm
Effective deep learning-based ensemble model for road crack detection
AM Okran, M Abdel-Nasser, HA Rashwan, D Puig
2022 IEEE international conference on big data (Big Data), 6407-6415, 2022
82022
A curated dataset for crack image analysis: Experimental verification and future perspectives
AM Okran, M Abdel-Nasser, HA Rashwan, D Puig
Artificial Intelligence Research and Development, 225-228, 2022
12022
A glance of the performance potential of serverless computing
AM Okran
6th URV Doctoral Workshop in Computer Science and Mathematics, 21, 2020
12020
Advanced Diabetic Retinopathy Classification: Integrating Pathological Indicators Segmentation and Morphological Feature Analysis
SK Musluh, AM Okran, S Abdulwahab, D Puig, HA Rashwan
International Workshop on Ophthalmic Medical Image Analysis, 104-114, 2024
2024
Weakly Supervised Localization of Mammograms for Dense Breast Tissues
AM Okran, S Algabli, HA Rashwan, D Puig
CCIA, 2024
2024
Weakly Supervised Localization of Mammograms for Dense Breast Tissues
T Alsuhbani, AM Okran, S Algabli, HA Rashwan, D Puig
Artificial Intelligence Research and Development, 78-79, 2024
2024
Enhanced Crack Segmentation Network: Leveraging Multi-Dimensional Attention
AM Okran, HA Rashwan, D Puig
Artificial Intelligence Research and Development, 94-96, 2024
2024
7th URV Doctoral Workshop in Computer Science and Mathematics
M Abdel-Nasser, O Farràs, D Puig, HA Rashwan
PUBLICACIONS UNIVERSITAT ROVIRA I VIRGILI, 2023
2023
Stacking Up for Success: A Cascade Network Model for Efficient Road Crack Segmentation
AM Okran, A Saleh, D Puig, HA Rashwan
Artificial Intelligence Research and Development, 38-47, 2023
2023
N-Channel Convolutional Neural Networks for Irony Detection in Twitter.
M Jabreel, N Maaroof, A Okran, A Moreno
CCIA, 237-246, 2018
2018
Efficient Crack Segmentation with Multi-Decoder Networks and Enhanced Feature Fusion
AM Okran, HA Rashwan, A Saleh, DS Puig Valls
Available at SSRN 4935980, 0
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