scispace - formally typeset
Search or ask a question
Author

Mohamed Abdel-Nasser

Bio: 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 published on a yearly basis

Papers
More filters
Journal ArticleDOI
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.
Abstract: Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids.

443 citations

Journal ArticleDOI
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.

89 citations

Journal ArticleDOI
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.
Abstract: The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.

66 citations

Journal ArticleDOI
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.
Abstract: We propose a simple and robust local descriptor of breast tissues in mammograms called ULDP.ULDP is evaluated in the task of mass/normal breast tissue classification.ULDP is evaluated in the task of breast tissue density classification.The results are comparable to the state-of-the-art methods on two databases. This paper proposes 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. The proposed system consists of three steps: segmentation of the ROI, feature extraction and classification. Although many feature extraction methods have been used to characterize breast tissues, the literature shows no consensus on the optimal feature set for breast tissue characterization. Specifically, mass detection on dense breast tissues is still a challenge. In the feature extraction step, we propose a simple and robust local descriptor for breast tissues in mammograms, called uniform local directional pattern (ULDP). This descriptor can discriminate between different tissues in mammograms, yielding a significant improvement in the analysis of breast cancer. Classifiers based on support vector machines show a performance comparable to the state-of-the-art methods.

56 citations

Journal ArticleDOI
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.
Abstract: Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce 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. The channel attention mechanism increases the discriminability between the lesion and non-lesion features by taking feature channel interdependencies into account. The 1-D factorized kernel block provides extra convolutions layers with a minimum number of parameters to reduce the computations of the higher-order convolutions. Besides, we use a multi-scale input strategy to encourage the development of filters which are scale-variant (i.e., constructing a scale-invariant representation). The proposed model is assessed on three skin challenge datasets: ISBI2016, ISBI2017, and ISIC2018. It yields competitive results when compared to several state-of-the-art methods in terms of Dice coefficient and intersection over union (IoU) score. The codes of the proposed model are publicly available at https://github.com/vivek231/Skin-Project.

47 citations


Cited by
More filters
01 Jan 2011
TL;DR: The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.
Abstract: This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

585 citations

Journal Article

516 citations

BookDOI
TL;DR: Statistical methods in medical research, Statistical methods inmedical research, and statistical methods in scientific research are used in medicine, education and research.
Abstract: Statistical methods in medical research , Statistical methods in medical research , کتابخانه دیجیتال جندی شاپور اهواز

491 citations

Journal ArticleDOI
TL;DR: An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview ofWOA applications that are used to solve optimization problems in various categories.
Abstract: Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

351 citations