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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. Source


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. Source


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. Source


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. Source


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. Source

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