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.

SLSNet: Skin lesion segmentation using a lightweight generative adversarial network

Abstract: The determination of precise skin lesion boundaries in dermoscopic images using automated methods faces many challenges, most importantly, the presence of hair, inconspicuous lesion edges and low contrast in dermoscopic images, and variability in the color, texture and shapes of skin lesions. Existing deep learning-based skin lesion segmentation algorithms are expensive in terms of computational time and memory. Consequently, running such segmentation algorithms requires a powerful GPU and high bandwidth memory, which are not available in dermoscopy devices. Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model. The 1-D kernel factorized network reduces the computational cost of 2D filtering. The position and channel attention modules enhance the discriminative ability between the lesion and non-lesion feature representations in spatial and channel dimensions, respectively. A multiscale block is also used to aggregate the coarse-to-fine features of input skin images and reduce the effect of the artifacts. SLSNet is evaluated on two publicly available datasets: ISBI 2017 and the ISIC 2018. Although SLSNet has only 2.35 million parameters, the experimental results demonstrate that it achieves segmentation results on a par with the state-of-the-art skin lesion segmentation methods with an accuracy of 97.61%, and Dice and Jaccard similarity coefficients of 90.63% and 81.98%, respectively. SLSNet can run at more than 110 frames per second (FPS) in a single GTX1080Ti GPU, which is faster than well-known deep learning-based image segmentation models, such as FCN. Therefore, SLSNet can be used for practical dermoscopic applications.

HIFA: Promising Heterogeneous Solar Irradiance Forecasting Approach Based on Kernel Mapping

Abstract: The rapid employment of photovoltaic (PV) has highlighted the importance of accurate solar irradiance forecasting in grid operation. However, the intermittent nature of solar irradiance represents a big challenge and degrades the accuracy of forecasting techniques, posing towards developing ensemble-based approaches. Most ensemble approaches generate weights based on the performance of individual forecasting models (IFMs) where linear operations are often used to aggregate them. The generalization of such weights could not be practically guaranteed due to the high variability among predictions obtained by IFMs. To tackle these issues, a novel heterogeneous solar irradiance forecasting approach, so-called HIFA, is proposed in this article. Specifically, we propose an effective aggregation strategy based on kernel mapping for aggregating the predictions of accurate deep learning based IFMs. The proposed aggregation strategy can properly map the predictions of IFMs onto a consensus prediction. HIFA utilizes efficient deep recurrent neural networks, which can exploit long-term information from previous computations to model the fluctuated solar irradiance, for building the IFMs. The results reveal that HIFA substantially improves the accuracy of solar irradiance forecasting when compared to ensemble-based approaches, thanks to the generalization capability of the proposed aggregation strategy and the high accuracy of deep IFMs.

A High Performance Point Based Image Alignment Approach Using an Artificial Immune System

Abstract: In this paper, a high performance image alignment approach is presented. This approach is classified as a point based alignment approach. An artificial immune system (AIS) with a modified mutation formula is used to find the correspondence points between the reference and the input images. After the correspondence is found, the least mean squares technique (LMS) is used to determine the transformation which is used to align the two images. This approach doesn't require any additional refinement or features detector as some others approaches required. To demonstrate the effectiveness of proposed algorithm, it compared with two state-of-the-art algorithms for different data sets. formula based on an uniform distribution was used. F. Ye et. al (7) proposed two step image registration by artificial immune system and chamfer matching, in this paper the artificial immune system has been used to find an initial transformation where the edge distance used as a fitness function then an area based method has been used to refine the transformation estimation. The artificial immune systems are used for function optimization, the clonal selection and affinity mutation principles are used to explain how the immune systems perform the optimization process. There are many artificial immune systems were published in the context. An immune algorithm, named CLONALG, was developed to perform pattern recognition and optimization. De Castro and J. Timmis proposed opti-aiNet for Multimodal Function Optimization (8). This algorithm is used in our registration approach. The mutation formula proposed at (8) was:

Application of mutation operators to grey wolf optimizer for solving emission-economic dispatch problem

Abstract: This paper presents applying the grey wolf optimization (GWO) to find an optimal solution for the combined economic and emission dispatch problem which aims to minimize the generation costs and keeping emission reduction. Six mutation operators are applied to the GWO to enhance its performance. The effect of a weight factor between generation cost and emission is also studied in this paper. A test system that consists of 10 units is simulated, the results show the effect of applying the mutation operators to the GWO.

Breast Tissue Characterization in X-Ray and Ultrasound Images using Fuzzy Local Directional Patterns and Support Vector Machines

Abstract: Accurate breast mass detection in mammographies is a difficult task, especially with dense tissues. Although ultrasound images can detect breast masses even in dense breasts, they are always corrupted by noise. In this paper, we propose fuzzy local directional patterns for breast mass detection in X-ray as well as ultrasound images. Fuzzy logic is applied on the edge responses of the given pixels to produce a meaningful descriptor. The proposed descriptor can properly discriminate between mass and normal tissues under different conditions such as noise and compression variation. In order to assess the effectiveness of the proposed descriptor, a support vector machine classifier is used to perform mass/normal classification in a set of regions of interest. The proposed method has been validated using the well-known mini-MIAS breast cancer database (X-ray images) as well as an ultrasound breast cancer database. Moreover, quantitative results are shown in terms of area under the curve of the receiver operating curve analysis.

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.