Scientific Research Group in Egypt

in Egypt, Egypt

Scientific Research Group in Egypt

in Egypt, Egypt

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Emary E.,Cairo University | Emary E.,Scientific Research Group in Egypt | Zawbaa H.M.,Babes - Bolyai University | Zawbaa H.M.,Beni Suef University | And 5 more authors.
Proceedings of the International Joint Conference on Neural Networks | Year: 2014

Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. In this paper, we propose an automated retinal blood vessel segmentation approach based on artificial bee colony optimisation in conjunction with fuzzy c-means clustering. Artificial bee colony optimisation is applied as a global search method to find cluster centers of the fuzzy c-means objective function. Vessels with small diameters appear distorted and hence cannot be correctly segmented at the first segmentation level due to confusion with nearby pixels. We employ a pattern search approach to optimisation in order to localise small vessels with a different fitness function. The proposed algorithm is tested on the publicly available DRIVE and STARE retinal image databases and confirmed to deliver performance that is comparable with state-of-the-art techniques in terms of accuracy, sensitivity and specificity. © 2014 IEEE.


El-Bendary N.,Arab Academy for Science and Technology | El-Bendary N.,Scientific Research Group in Egypt | El Hariri E.,Fayoum University | El Hariri E.,Scientific Research Group in Egypt | And 3 more authors.
Expert Systems with Applications | Year: 2015

Tomato quality is one of the most important factors that helps ensuring a consistent marketing of tomato fruit. As ripeness is the main indicator for tomato quality from customers perspective, the determination of tomato ripeness stages is a basic industrial concern regarding tomato production in order to get high quality product. Automatic ripeness evaluation of tomato is an essential research topic as it may prove benefits in ensuring optimum yield of high quality product, this will increase the income because tomato is one of the most important crops in the world. This article presents an automated multi-class classification approach for tomato ripeness measurement and evaluation via investigating and classifying the different maturity/ripeness stages. The proposed approach uses color features for classifying tomato ripeness stages. The approach proposed in this article uses Principal Components Analysis (PCA) in addition to Support Vector Machines (SVMs) and Linear Discriminant Analysis (LDA) algorithms for feature extraction and classification, respectively. Experiments have been conducted on a dataset of total 250 images that has been used for both training and testing datasets with 10-fold cross validation. Experimental results showed that the proposed classification approach has obtained ripeness classification accuracy of 90.80%, using one-against-one (OAO) multi-class SVMs algorithm with linear kernel function, ripeness classification accuracy of 84.80% using one-against-all (OAA) multi-class SVMs algorithm with linear kernel function, and ripeness classification accuracy of 84% using LDA algorithm. © 2014 Elsevier Ltd. All rights reserved.


Hassanien A.E.,Cairo University | Hassanien A.E.,Scientific Research Group in Egypt | Moftah H.M.,Beni Suef University | Moftah H.M.,Scientific Research Group in Egypt | And 3 more authors.
Applied Soft Computing Journal | Year: 2014

This article introduces a hybrid approach that combines the advantages of fuzzy sets, ant-based clustering and multilayer perceptron neural networks (MLPNN) classifier, in conjunction with statistical-based feature extraction technique. An application of breast cancer MRI imaging has been chosen and hybridization system has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: Benign or Malignant. The introduced hybrid system starts with an algorithm based on type-II fuzzy sets to enhance the contrast of the input images. This is followed by an improved version of the classical ant-based clustering algorithm, called adaptive ant-based clustering to identify target objects through an optimization methodology that maintains the optimum result during iterations. Then, more than twenty statistical-based features are extracted and normalized. Finally, a MLPNN classifier was employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether the cancer is Benign or Malignant. To evaluate the performance of presented approach, we present tests on different breast MRI images. The experimental results obtained, show that the adaptive ant-based segmentation is superior to the classical ant-based clustering technique and the overall accuracy offered by the employed hybrid technique confirm that the effectiveness and performance of the proposed hybrid system is high. © 2013 Elsevier B.V.


Hafez A.I.,Minia University | Hafez A.I.,Scientific Research Group in Egypt | Ghali N.I.,Al - Azhar University of Egypt | Ghali N.I.,Scientific Research Group in Egypt | And 3 more authors.
International Conference on Intelligent Systems Design and Applications, ISDA | Year: 2012

Community detection in complex networks has attracted a lot of attention in recent years. Community detection can be viewed as an optimization problem, in which an objective function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. Many single-objective optimization techniques have been used to solve the problem however those approaches have its drawbacks since they try optimizing one objective function and this results to a solution with a particular community structure property. More recently researchers viewed the problem as a multi-objective optimization problem and many approaches have been proposed to solve it. However which objective functions could be used with each other is still under debated since many objective functions have been proposed over the past years and in somehow most of them are similar in definition. In this paper we use Genetic Algorithm (GA) as an effective optimization technique to solve the community detection problem as a single-objective and multi-objective problem, we use the most popular objectives proposed over the past years, and we show how those objective correlate with each other, and their performances when they are used in the single-objective Genetic Algorithm and the Multi-Objective Genetic Algorithm and the community structure properties they tend to produce. © 2012 IEEE.


Elsayed W.,Suez Canal University | Gaber T.,Suez Canal University | Gaber T.,Scientific Research Group in Egypt | Zhang N.,University of Manchester | Moussa M.I.,Benha University
Advances in Intelligent Systems and Computing | Year: 2016

Pervasive computing is a concept in computer science where computing appear everywhere and anywhere, the devices are heterogeneous and may belong to different domains, and they interact with each other to provide smart services. Traditional access control models such as DAC, MAC and RBAC are not suitable to this environment. Therefore, we need more flexible, dynamic and generic access control models for controlling access to such environments. There have been many proposals proposed to address the new security requirements in the pervasive computing environments. The goal of this paper is to review and analyze the existing proposals seen in literature and to compare the approaches taken in these proposals based on the security requirements. This comparison will lead to the identification of some research gaps that require further investigation. © Springer International Publishing Switzerland 2016.


Salama M.A.,The British University in Egypt | Salama M.A.,Scientific Research Group in Egypt | Hassanien A.E.,Cairo University | Hassanien A.E.,Scientific Research Group in Egypt | Mostafa A.,The British University in Egypt
Eurasip Journal on Bioinformatics and Systems Biology | Year: 2016

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence. © 2016, Salama et al.


Ali A.F.,Suez Canal University | Ali A.F.,Scientific Research Group in Egypt | Ahmed N.N.,Suez Canal University | Sherif N.A.M.,Suez Canal University | Mersal S.,Suez Canal University
Advances in Intelligent Systems and Computing | Year: 2016

In this paper, we present a new hybrid differential evolution algorithm with simulated annealing algorithm to minimize a molecular potential energy function. The proposed algorithm is called Hybrid Differential Evolution and Simulated Annealing Algorithm (HDESA). The problem of minimizing the molecular potential energy function is very difficult, since the number of local minima grows exponentially with the molecular size. The proposed HDESA is tested on a simplified model of a molecular potential energy function with up to 100° of freedom and it is compared against 9 algorithms. The experimental results show that the proposed algorithm is a promising algorithm and can obtain the global or near global minimum of the molecular potential energy function in reasonable time. © Springer International Publishing Switzerland 2016.


Elshazly H.I.,Cairo University | Elshazly H.I.,Scientific Research Group in Egypt | Elkorany A.M.,Cairo University | Hassanien A.E.,Cairo University | And 3 more authors.
Proceedings - 2013 8th International Conference on Computer Engineering and Systems, ICCES 2013 | Year: 2013

Machine Learning concept offers the biomedical research field a great support. It provides many opportunities for disease discovering and related drugs revealing. The machine learning medical applications had been evolved from the physician needs and motivated by the promising results extracted from empirical studies. Medical support systems can be provided by screening, medical images, pattern classification and microarrays gene expression analysis. Typically medical data is characterized by its huge dimensionality and relatively limited examples. Feature selection is a crucial step to improve classification performance. Recent studies in machine learning field about classification process emerged a novel strong classifier scheme called the ensemble classifier. In this paper, a study for the performance of two novel ensemble classifiers namely Random Forest (RF) and Rotation Forest (ROT) for biomedical data sets is tested with five medical datasets. Three different feature selection methods were used to extract the most relevant features in each data set. Prediction performance is evaluated using accuracy measure. It was observed that ROT achieved the highest classification accuracy in most tested cases. © 2013 IEEE.


El-Masry W.H.,Cairo University | Emary E.,Cairo University | Hassanien A.E.,Scientific Research Group in Egypt
ICET 2014 - 2nd International Conference on Engineering and Technology | Year: 2015

In this paper, an automated liver CT image clustering approach based on evolutionary metaheuristic algorithm called invasive weed optimization is presented without any prior information about the number of naturally occurring groups in the images. The fitness function used in the genetic algorithm is k-means objective function for searching of the smoothed compact cluster. The experimental results suggest that invasive weed optimization holds immense promise to appear as an efficient metaheuristic for multi-objective optimization in computer aided diagnosis applications. © 2014 IEEE.


PubMed | Suez Canal University and Scientific Research Group in Egypt
Type: | Journal: Scientific reports | Year: 2016

Measuring toxicity is one of the main steps in drug development. Hence, there is a high demand for computational models to predict the toxicity effects of the potential drugs. In this study, we used a dataset, which consists of four toxicity effects:mutagenic, tumorigenic, irritant and reproductive effects. The proposed model consists of three phases. In the first phase, rough set-based methods are used to select the most discriminative features for reducing the classification time and improving the classification performance. Due to the imbalanced class distribution, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. ITerative Sampling (ITS) method is proposed to avoid the limitations of those methods. ITS method has two steps. The first step (sampling step) iteratively modifies the prior distribution of the minority and majority classes. In the second step, a data cleaning method is used to remove the overlapping that is produced from the first step. In the third phase, Bagging classifier is used to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed model performed well in classifying the unknown samples according to all toxic effects in the imbalanced datasets.

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