M
Mohamed Abdel-Nasser
Researcher at Aswan University
Publications - 99
Citations - 1417
Mohamed Abdel-Nasser is an academic researcher from Aswan University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 13, co-authored 71 publications receiving 759 citations. Previous affiliations of Mohamed Abdel-Nasser include Rovira i Virgili University & South Valley University.
Papers
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Accurate photovoltaic power forecasting models using deep LSTM-RNN
TL;DR: The use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems and offers a further reduction in the forecasting error compared with the other methods.
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Breast tumor classification in ultrasound images using texture analysis and super-resolution methods
TL;DR: It is shown that the super-resolution-based approach improves the performance of the evaluated texture methods and thus outperforms the state of the art in benign/malignant tumor classification.
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Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs
TL;DR: A reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral is proposed and compared with several forecasting methods using six realistic datasets collected from different sites in Finland.
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Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern
TL;DR: A computer-aided diagnosis system to analyze breast tissues in mammograms, which performs two main tasks: breast tissue classification within a region of interest (ROI; mass or normal) and breast density classification and a simple and robust local descriptor called ULDP is proposed.
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FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention
Vivek Kumar Singh,Mohamed Abdel-Nasser,Hatem A. Rashwan,Farhan Akram,Nidhi Pandey,Alain Lalande,Benoit Presles,Santiago Romani,Domenec Puig +8 more
TL;DR: This paper introduces a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution to improve discriminability between the lesion and non-lesion features.