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.

Efficient SPF approach based on regression and correction models for active distribution systems

Abstract: This study proposes efficient methods for sequential power flow (SPF) analysis of distribution systems with intermittent photovoltaic (PV) units and fluctuated loads. The proposed methods are based on machine learning techniques; more specifically, they use a regression trees (RTs) algorithm to construct a model for voltage estimation. This model is trained using synthetic data generated by a number of PV generation and load demand scenarios. The SPF methods that utilise iterative techniques have a high computational burden. In turn, the proposed method, which is called SPF-RT, is fast and accurate. Furthermore, the authors combine SPF-RT with a correction method to develop a new method, called SPF-RTC, which significantly reduces the estimation error of the RT model. The proposed methods are tested using a 33-bus distribution test system interconnected with two PV units. To assess the performance of the proposed methods, they conducted several experiments at different resolutions of day/year data. The proposed methods are compared with the iterative SPF methods and validated using the OpenDSS software. The simulation results demonstrate that the proposed methods outperform the other methods in terms of the computational speed. The SPF-RT and SPF-RTC methods are useful for real-time assessment of distribution systems with PV units.

Link quality prediction in wireless community networks using deep recurrent neural networks

Abstract: Wireless community networks (WCNs) are large, heterogeneous, dynamic, and decentralized networks Such complex characteristics raise different challenges, such as the effect of wireless communications on the performance of networks and routing protocols The prediction approaches of link quality (LQ) can improve the performance of routing algorithms of WCNs while avoiding weak links The prediction of LQ in WCNs can be a complex task because of the fluctuated nature of LQ measurements due to the dynamic wireless environment In this paper, a deep learning based approach is proposed to accurately predict LQ in WCNs Specifically, we propose the use of two variants of deep recurrent neural network (RNN): long short-term memory recurrent neural networks (LSTM-RNN) and gated recurrent unit (GRU) The positive feature of the proposed variants is that they can handle the fluctuating nature of LQ due to their ability to learn and exploit the context in LQ time-series The experimental results on data collected from a real-world WCN show that the proposed LSTM-RNN and GRU models accurately predict LQ in WCNs compared to related methods The proposed approach could be a helpful tool for accurately predicting LQ, thereby improving the performance of routing protocols of WCNs

An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning

Abstract: This paper proposes an efficient solution for tumor segmentation and classification in breast ultrasound (BUS) images. We propose to add an atrous convolution layer to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images. To automatically re-balance the relative impact of each of the highest level encoded features, we also propose to add a channel-wise weighting block in the network. In addition, the SSIM and L1-norm loss with the typical adversarial loss are used as a loss function to train the model. Our model outperforms the state-of-the-art segmentation models in terms of the Dice and IoU metrics, achieving top scores of 93.76% and 88.82%, respectively. In the classification stage, we show that few statistics features extracted from the shape of the boundaries of the predicted masks can properly discriminate between benign and malignant tumors with an accuracy of 85%$

Optimal Voltage Control in Distribution Systems With Intermittent PV Using Multiobjective Grey-Wolf-Lévy Optimizer

Abstract: The intermittent photovoltaic (PV) units significantly affect the performance of distribution systems, and they often cause several operational problems, most importantly, voltage rise/drop. At high PV penetration, excessive tap movements of transformers and high curtailed PV power are expected to completely solve the voltage violation problem. In this paper, we propose an optimal voltage control method for distribution systems considering the number of tap movements of transformers and the active power curtailment of PV units. The objective function of the proposed method comprises: 1) voltage drop violation, 2) voltage rise violation, 3) tap movement rate (TMR) of transformers, and 4) curtailed power of PV (CPPV). A multiobjective grey wolf optimizer integrated with a Levy mutation operator (GWO-Levy) is formulated to accurately solve the voltage control problem. A 24-h simulation is performed on the 119-bus distribution system with PV and different types of loads. The performance of GWO-Levy is compared with three other optimizers, finding that it achieves the best performance. The simulation results demonstrate the efficacy of the proposed method for solving the voltage violation problem with PV while simultaneously optimizing TMR and CPPV.

Compressive sensing MRI reconstruction using empirical wavelet transform and grey wolf optimizer

Abstract: Magnetic resonance imaging (MRI) has exhibited an outstanding performance in the track of medical imaging compared to several imaging modalities, such as X-ray, positron emission tomography and computed tomography. MRI modality suffers from protracted scanning time, which affects the psychological status of patients. This scanning time also increases the blurring levels in MR image due to local motion actions, such as breathing as in the case of cardiac imaging. An acquisition technique called compressed sensing has contributed to solve the drawbacks of MRI and decreased the acquisition time by reducing the quantity of the measured data that is needed to reconstruct an image without significant degradation in image quality. All recent works have used different types of conventional wavelets for sparsifying the image, which employ constant filter banks that are independent of the characteristics of the input image. This paper proposes to use the empirical wavelet transform (EWT) which tunes its filter banks to the characteristics of the analyzed images. In other words, we use EWT to produce a sparse representation of the MRI images which yields a more accurate sparsification transform. In addition, the grey wolf optimizer is used to optimize the parameters of the proposed method. To validate the proposed method, we use three MRI datasets of different organs: brain, cardiac and shoulder. The experimental results show that the proposed method outperforms the state-of-the-art methods in terms of signal-to-noise ratio and structure similarity metrics.

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

A comprehensive survey: Whale Optimization Algorithm and its applications

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.