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E. Emary, H. M. Zawbaa, A. E. Hassanien, and B. PARV, " Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search, , ", Advances in Data Analysis and Classification, , issue (27 May 2016 on line), , pp. pp 1-17, 2017. AbstractWebsite

This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.

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Eslam Hassan, A. Hafez, A. E. H. and, and A. Fahmy, " Nature inspired algorithms for solving the community detection problem, ", Logic Journal of the IGPL: Oxford Journals, 2017.
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Waleed Yamany, Eid Emary, A. E. Hassanien, G. Schaefer, and S. Y. Zhu, " An Innovative Approach for Attribute Reduction using Rough Sets and Flower Pollination Optimisation ", 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2016,, , United Kingdom., 5-7 September , 2016. Abstract

Optimal search is a major challenge for wrapper-based attribute reduction. Rough sets have been used with much success, but current hill-climbing rough set approaches to attribute reduction are insufficient for finding optimal solutions. In this paper, we propose an innovative use of an intelligent optimisation method, namely the flower search algorithm (FSA), with rough sets for attribute reduction. FSA is a relatively recent computational intelligence algorithm, which is inspired by the pollination process of flowers. For many applications, the attribute space, besides being very large, is also rough with many different local minima which makes it difficult to converge towards an optimal solution. FSA can adaptively search the attribute space for optimal attribute combinations that maximise a given fitness function, with the fitness function used in our work being rough set-based classification. Experimental results on various benchmark datasets from the UCI repository confirm our technique to perform well in comparison with competing methods.

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El-said, S. A., and A. E. Hassanien, " Artificial Eye Vision Using Wireless Sensor Networks", Wireless Sensor Networks: Theory and Applications, USA, , CRC Press, Taylor and Francis Group, 2013. Abstractk15146_c023.pdf

In the past few years, many wireless sensor networks (WSN) had been deployed. It has proved its usage in the future distributed computing environment. Some of its specific applications are habitat monitoring, object tracking, nuclear reactor controlling, fire detection, traffic monitoring, and health care. The main goals of this paper is to describe the major challenges and open research problems of using WSN in healthcare and survey advancements in using WSN to build a chronically implanted artificial retina for visually impaired people. Using WSN in vision repairing addresses two retinal diseases: Age-related Macular Degeneration (severe vision loss at the center of the retina in over 60) and Retinitis Pigmentosa (photoreceptor dysfunction → loss of peripheral vision). The use of WSN in artificial retina provides new features that have the potential to be an economically viable to assist people with visual impairments.

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Elshazly, H. I., A. M. Elkorany, A. E. Hassanien, and M. Waly, " Chronic eye disease diagnosis using ensemble-based classifier", The second International Conference on Engineering and Technology (ICET 2014) , German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
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El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, " Genetic Annealing Optimization: Design and Real World Applications.", Eighth International Conference on Intelligent Systems Design and Applications, ISDA 2008, , Kaohsiung, Taiwan,, 26-28 November , 2008. Abstract

Both simulated annealing (SA) and the genetic algorithms (GA) are stochastic and derivative-free optimization technique. SA operates on one solution at a time, while the GA maintains a large population of solutions, which are optimized simultaneously. Thus, the genetic algorithm takes advantage of the experience gained in the past exploration of the solution space. Since SA operates on one solution at a time, it has very little history to use in learning from past trials. SA has the ability to escape from any local point; even it is a global optimization technique. On the other hand, there is no guarantee that the GA algorithm will succeeded in escaping from any local minima, thus it makes sense to hybridize the genetic algorithm and the simulated annealing technique. In this paper, a novel genetically annealed algorithm is proposed and is tested against multidimensional and highly nonlinear cases; Fed-batch fermentor for Penicillin production, and isothermal continuous stirred tank reactor CSTR. It is evident from the results that the proposed algorithm gives good performance.

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El-Said, S. A., H. M. A. Atta, and A. E. Hassanien, " Interactive soft tissue modelling for virtual reality surgery simulation and planning,", Int. J. Computer Aided Engineering and Technology, Inderscience, , vol. 9, issue 1, pp. pp. 38-61, 2017. AbstractWebsite

While most existing virtual reality-based surgical simulators in the literature use linear deformation models, soft-tissues exhibit geometric and material nonlinearities that should be taken into account for realistic modelling of the deformations. In this paper, an interactive soft tissue model (ISTM) which enables flexible, accurate and robust simulation of surgical interventions on virtual patients is proposed. In ISTM, simulating the tool-tissue interactions using nonlinear dynamic analysis is formulated within a total Lagrangian framework, and the energy function is modified by adding a term in order to achieve material incompressibility. The simulation results show that ISTM increases the stability and eliminates integration errors in the dynamic solution, decreases calculation costs by a factor of 5-7, and leads to very stable and sufficiently accurate results. From the simulation results it can be concluded that the proposed model can successfully create acceptable soft tissue models and generate realistically visual effects of surgical simulation.

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Walaa Elmasry, Hossam Moftah, Walaa Elmasry, N. Elbendary, and A. E. Hassanien, " Performance Evaluation of Computed Tomography Liver Image Segmentation Approachers", The IEEE International Conference on Hybrid Intelligent Systems (HIS2012). , Pune. India., 4-7 Dec. 2012,, pp. 109 - 114, 2012. Abstract

This paper presents and evaluates the performance of two well-known segmentation approaches that were applied on liver computed tomography (CT) images. The two approaches are K-means and normalized cuts. An experiment was applied on ten liver CT scan images, with reference segmentations, in order to test the performance of the two approaches. Experimental results were compared using an evaluation measure that highlights segmentation accuracy. Based on the obtained results in this study, it has been observed that K-means clustering algorithm outperformed normalized cuts segmentation algorithm for cases where region of interest depicts a closed shape, while, normalized cuts algorithm obtained better results with non-circular clusters. Moreover, for K-means clustering, different initial partitions can result in different final clusters.

El-Atta, A. A. H., M. I. Moussa, and A. E. Hassenian, " Predicting biological activity of 2,4,6-trisubstituted 1,3,5-triazines", 5ththe 5th International Conference on Innovations in Bio-Inspired Computing and Applications - IBICA2014 (Springer), Ostrava, Czech Republic., 22-24 June, 2013.
R
Eid Emary, H. zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, " Retinal Blood Vessel Segmentation using Bee Colony Optimisation and Pattern Search ", The annual IEEE International Joint Conference on Neural Networks (IJCNN) – July 6-, Beijing, China, 6 July, 2014.
Eid Emary, H. zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, " Retinal Vessel Segmentation based on Possibilistic Fuzzy c-means Clustering Optimised with Cuckoo Search", The annual IEEE International Joint Conference on Neural Networks (IJCNN) – July 6-, Beijing, China, 6 July, 2014.
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El-Bendary, N., A. E. Hassanien, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 34–39, 2011. Abstract
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Hassanien, A. E., N. El-Bendary, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", The First ACM International Workshop on Security and Privacy Preserving in e-Societies, Lebanon , 7 June, 2011. Abstract

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El-Bendary, N., A. E. Hassanien, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 34–39, 2011. Abstract
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Hassanien, A. E., N. El-Bendary, J. Sedano, O. S. Soliman, and N. I. Ghali, "$μ$TESLA-based secure routing protocol for wireless sensor networks", The First ACM International Workshop on Security and Privacy Preserving in e-Societies, 2011. Abstract

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El-dahshan, E., A. Redi, A. E. Hassanien, and K. Xiao, "Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network", Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007. International Symposium on: IEEE, pp. 308–311, 2007. Abstract
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El-dahshan, E., A. Redi, A. E. Hassanien, and K. Xiao, "Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network", Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007. International Symposium on: IEEE, pp. 308–311, 2007. Abstract
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El-dahshan, E., A. Redi, A. E. Hassanien, and K. Xiao, "Accurate detection of prostate boundary in ultrasound images using biologically-inspired spiking neural network", Intelligent Signal Processing and Communication Systems, 2007. ISPACS 2007. International Symposium on: IEEE, pp. 308–311, 2007. Abstract
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Ahmed, K., A. E. Hassanien, E. Ezzat, and P. - W. Tsai, "An Adaptive Approach for Community Detection Based on Chicken Swarm Optimization Algorithm", International Conference on Genetic and Evolutionary Computing: Springer International Publishing, pp. 281–288, 2016. Abstract
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et.al., A. E. H., "AMLTA (2018): International Conference on Advanced Machine Learning Technologies and Applications", AMLTA (2018): International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Springer, 2018.
Ahmed.H.Asad, A. T. Azar, N. El-Bendary, and A. E. Hassaanien, "Ant Colony based Feature Selection Heuristics for Retinal Vessel Segmentation", In Proceedings of the Second IEEE International Symposium on Intelligent Informatics (ISI'13), , Mysore, India., 23-24 August, 2013. Abstractsi2013-tation_using_ant_colony_system.pdf

Features selection is an essential step for successful data classification,
since it reduces the data dimensionality by removing redundant features. Consequently,
that minimizes the classification complexity and time in addition to maximizing
its accuracy. In this article, a comparative study considering six features
selection heuristics is conducted in order to select the best relevant features subset.
The tested features vector consists of fourteen features that are computed for each
pixel in the field of view of retinal images in the DRIVE database. The comparison
is assessed in terms of sensitivity, specificity, and accuracy measurements of
the recommended features subset resulted by each heuristic when applied with the
ant colony system. Experimental results indicated that the features subset recommended
by the relief heuristic outperformed the subsets recommended by the other
experienced heuristics.

Emary, E., R. E. Elesawy, S. A. M. El Ella, and A. E. Hassanien, "Aquatic weeds prediction: A comparative study", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 259–265, 2014. Abstract
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El-Bendary, N., A. E. Hassanien, E. Corchado, and R. C. Berwick, "ARIAS: Automated retinal image analysis system", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 67–76, 2011. Abstract
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El-Bendary, N., A. E. Hassanien, E. Corchado, and R. C. Berwick, "ARIAS: Automated retinal image analysis system", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 67–76, 2011. Abstract
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