Mohamed Abdel-Nasser

Bio: Mohamed Abdel-Nasser is an academic researcher from Aswan University. The author has contributed to research in topic(s): Segmentation & Photovoltaic system. The author has an hindex of 13, co-authored 71 publication(s) receiving 759 citation(s). Previous affiliations of Mohamed Abdel-Nasser include Rovira i Virgili University & South Valley University.

Accurate photovoltaic power forecasting models using deep LSTM-RNN

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.

Breast tumor classification in ultrasound images using texture analysis and super-resolution methods

Abstract: Ultrasound images can be used to detect tumors that do not appear in the mammograms of dense breasts. Several computer-aided diagnosis (CAD) systems based on this type of images have been proposed to detect tumors and discriminate between benign and malignant ones. To characterize those lesions, many of the aforementioned systems rely on texture analysis methods. However, speckle noise and artifacts that appear in ultrasound images may degrade their performance. To tackle this problem, and contrary to the state-of-the-art methods that utilize a single image of the breast, this paper proposes the use of a super-resolution approach that exploits the complementary information provided by multiple images of the same target. The proposed CAD system consists of four stages: super-resolution computation, extraction of the region of interest, feature extraction and classification. We have evaluated the performance of five texture methods with the proposed CAD system: gray level co-occurrence matrix features, local binary patterns, phase congruency-based local binary pattern, histogram of oriented gradients and pattern lacunarity spectrum. We show that our 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. Graphical abstractDisplay Omitted HighlightsWe propose a new breast tumor classification approach in ultrasound images.We propose the use of a super-resolution approach to improve texture methods.Several texture methods have been evaluated in this paper.

Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern

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.

Fast yet Accurate Energy-Loss-Assessment Approach for Analyzing/Sizing PV in Distribution Systems Using Machine Learning

Abstract: The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These simulations have a heavy computational burden, which makes it difficult to analyze distribution systems and evaluate PV impacts with fine resolutions. To cope with this issue, most related works down-sample, cluster, or quantize the full data to reduce the computational time on the expense of the accuracy. In this paper, we propose a fast yet accurate energy-loss assessment approach in distribution systems using machine learning. The unique feature of the proposed approach is that it uses all data to estimate losses, which yields accurate results close to the exact solutions in a very short time. The simulation results demonstrate that the proposed approach extremely reduces the computational time of energy-loss estimation with high accuracy rates. The speedup of the proposed approach with respect to power flow simulations for a yearlong at a 30-s time resolution is 28 691 (99.9965 $\%$ reduction in computational time). The effectiveness of the proposed approach is also illustrated by applying it to optimize the PV size for minimizing energy losses.

Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods

Abstract: Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these images, the temperature of the regions that contain tumors is warmer than the normal tissue. To detect that difference in temperature between normal and cancerous regions, a dynamic thermography procedure uses thermal infrared cameras to generate infrared images at fixed time steps, obtaining a sequence of infrared images. In this paper, we propose a novel method to model the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods. The proposed method generates a compact representation for the infrared images of each sequence, which is then exploited to differentiate between normal and cancerous cases. Our method produced competitive (AUC = 0.989) results when compared to other studies in the literature.

Online Short-term Solar Power Forecasting

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.

Statistical methods in medical research

Abstract: Statistical methods in medical research , Statistical methods in medical research , کتابخانه دیجیتال جندی شاپور اهواز

Fitzpatrick’s Color Atlas and Synopsis of Clinical Dermatology

Abstract: Covering the full range of conditions, from rashes, to skin lesions and disorders of the hair, nail, and mucosa, Fitzpatrick's is a unique combination of text, clinical reference, and color atlas-one that gives you the best quality and most varied photographs of skin disorders available anywhere. The book features a consistent format featuring key facts pertaining to epidemiology, clinical manifestations, physical exam, diagnosis, and treatment, each paired with several clear photographs to show how the condition appears-plus boxed overviews of each disease category with severity-specific icons.