Publications

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Adl, A., I. B. Shaheed, M. I. Shaalan, A. K. Al-Mokaddem, and A. E. Hassanien, "Digital Pathological Services Capability Framework", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 109–118, 2014. Abstract
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Ahmed, M. M., M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "Discrete Group Search Optimizer for Community Detection in Social Networks", International Joint Conference on Rough Sets: Springer International Publishing, pp. 439–448, 2016. Abstract
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Ahmed, M. M., M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "Discrete Group Search Optimizer for community detection in multidimensional social network", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec. , 2016. Abstract

Multidimensionality is a distinctive aspect of real world social networks. Multidimensional social networks appeared as a result of that most social media sites such as Facebook, Twitter, and YouTube enable people to interact with each other through different social activities, reflecting different kinds of relationships between them. Recently, studying community structures hidden in multidimensional social networks has attracted a lot of attention. When dealing with these networks, the concept of community detection problem changes to be the discovery of the shared group structure across all network dimensions, such that members in the same group are tightly connected with each other, but are loosely connected with others outside the group. Studies in community detection topic have traditionally focused on networks that represent one type of interactions or one type of relationships between network entities. In this paper, we propose Discrete Group Search Optimizer (DGSO-MDNet) to solve the community detection problem in Multidimensional social networks, without any prior knowledge about the number of communities. The method aims to find community structure that maximizes multi-slice modularity, as an objective function. The proposed DGSO-MDNet algorithm adopts the locus-based adjacency representation and several discrete operators. Experiments on synthetic and real life networks show the capability of the proposed algorithm to successfully detect the structure hidden within these networks compared with other high performance algorithms in the literature.

Ahmed, M. M., M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "Discrete Group Search Optimizer for community detection in multidimensional social network", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 47–52, 2016. Abstract
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Ahmed, S. A., T. M. Nassef, N. I. Ghali, G. Schaefer, and A. E. Hassanien, "Determining protrusion cephalometric readings from panoramic radiographic images", Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on: IEEE, pp. 321–324, 2012. Abstract
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Ahmed, K., A. I. Hafez, and A. E. Hassanien, "A discrete krill herd optimization algorithm for community detection", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 297–302, 2015. Abstract
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Ahmed, S. A., T. M. Nassef, N. I. Ghali, G. Schaefer, and A. E. Hassanien, "Determining protrusion cephalometric readings from panoramic radiographic images", Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on: IEEE, pp. 321–324, 2012. Abstract
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Al-Qaheri, H., A. E. Hassanien, and A. Abraham, "Discovering stock price prediction rules using rough sets", Neural Network World, vol. 18, no. 3: Institute of Computer Science, pp. 181, 2008. Abstract
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Al-Qaheri, H., A. E. Hassanien, and A. Abraham, "Discovering stock price prediction rules using rough sets", Neural Network World, vol. 18, no. 3: Institute of Computer Science, pp. 181, 2008. Abstract
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Ali, M. A. S., G. I. Sayed, T. Gaber, A. E. Hassanien, V. Snasel, and L. F. Silva, "Detection of breast abnormalities of thermograms based on a new segmentation method", Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on: IEEE, pp. 255–261, 2015. Abstract
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Attia, K. A., A. I. Hafez, and A. ella hasanien, "A Discrete Krill Herd Optimization Algorithm For Community Detection", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Distance similarity as a CBR technique for early detection of breast cancer: An Egyptian case study", Information Systems Design and Intelligent Applications: Springer India, pp. 449–456, 2015. Abstract
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Azar, A. T., and A. E. Hassanien, "Dimensionality reduction of medical big data using neural-fuzzy classifier", Soft computing, vol. 19, no. 4: Springer Berlin Heidelberg, pp. 1115–1127, 2015. Abstract
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Aziz, A. S. A., M. Salama, A. E. Hassanien, and E. L. Sanaa, "Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 597–602, 2012. Abstract
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Aziz, A. S. A., M. Salama, A. E. Hassanien, and E. L. Sanaa, "Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 597–602, 2012. Abstract
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Aziz, A. S. A., M. Salama, A. E. Hassanien, and E. L. Sanaa, "Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 597–602, 2012. Abstract
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Banerjee, S., N. Elbendary, A. E. Hassanien, and M. Tolba, "Decision Support System for Customer Churn Reduction Approach", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp.229-234, 2013, Tunisia, , 4-6 Dec, 2013.
Banerjee, S., N. El-Bendary, A. E. Hassanien, and M. F. Tolba, "Decision support system for customer churn reduction approach", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 228–233, 2013. Abstract
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Elbedwehy, M. N., H. M. Zawbaa, N. Ghali, and A. E. Hassanien, "Detection of heart disease using binary particle swarm optimization", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 177–182, 2012. Abstract
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Elbedwehy, M. N., H. M. Zawbaa, N. Ghali, and A. E. Hassanien, "Detection of Heart Disease using Binary Particle Swarm Optimization", IEEE Federated Conference on Computer Science and Information Systems, Wroclaw - Poland, pp. 199–204, 2012. Abstractdetection_of_heart_disease_using_binary_particle.pdf

This article introduces a computer-aided diagnosis
system of the heart valve disease using binary particle swarm
optimization and support vector machine, in conjunction with
K-nearest neighbor and with leave-one-out cross-validation. The
system was applied in a representative heart dataset of 198
heart sound signals, which come both from healthy medical cases
and from cases suffering from the four most usual heart valve
diseases: aortic stenosis (AS), aortic regurgitation (AR), mitral
stenosis (MS) and mitral regurgitation (MR). The introduced
approach starts with an algorithm based on binary particle
swarm optimization to select the most weighted features. This
is followed by performing support vector machine to classify
the heart signals into two outcome: healthy or having a heart
valve disease, then its classified the having a heart valve disease
into four outcomes: aortic stenosis (AS), aortic regurgitation
(AR), mitral stenosis (MS) and mitral regurgitation (MR). The
experimental results obtained, show that the overall accuracy
offered by the employed approach is high compared with other
techniques.

Elbedwehy, M. N., H. M. Zawbaa, N. Ghali, and A. E. Hassanien, "Detection of heart disease using binary particle swarm optimization", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 177–182, 2012. Abstract
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Gaber, T., T. Kotyk, N. Dey, A. D. C. V. Amira Ashour, A. E. Hassanienan, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) , Springer. , Beni Suef University, Beni Suef, Egypt, Nov. 28-30, 2015. Abstract

Apoptosis is an imperative constituent of various processes including proper
progression and functioning of the immune system, embryonic development as well
as chemical-induced cell death. Improper apoptosis is a reason in numerous human/
animal’s conditions involving ischemic damage, neurodegenerative diseases,
autoimmune disorders and various types of cancer. An outstanding feature of
neurodegenerative diseases is the loss of specific neuronal populations. Thus, the
detection of the dead cells is a necessity. This paper proposes a novel algorithm to
achieve the dead cells detection based on color intensity and contrast changes and
aims for fully automatic apoptosis detection based on image analysis method. A
stained cultures images using Caspase stain of albino rats hippocampus specimens
using light microscope (total 21 images) were used to evaluate the system
performance. The results proved that the proposed system is efficient as it achieved
high accuracy (98.89 ± 0.76 %) and specificity (99.36 ± 0.63 %) and good mean
sensitivity level of (72.34 ± 19.85 %).

Grosan, C., A. Abraham, and A. - E. Hassanien, "Designing resilient networks using multicriteria metaheuristics", Telecommunication Systems , vol. 40, issue 1-2, pp. 75-88, 2009. AbstractWebsite

The paper deals with the design of resilient networks that are fault tolerant against link failures. Usually,
fault tolerance is achieved by providing backup paths, which are used in case of an edge failure on a primary path. We consider this task as a multiobjective optimization problem: to provide resilience in networks while minimizing the cost subject to capacity constraint. We propose a stochastic approach,
which can generate multiple Pareto solutions in a single run. The feasibility of the proposed method is illustrated by considering several network design problems using a single weighted average of objectives and a direct multiobjective optimization approach using the Pareto dominance concept.

H
Hassan, E. A., A. I. Hafez, A. E. Hassanien, and A. A. Fahmy, "A discrete bat algorithm for the community detection problem", International Conference on Hybrid Artificial Intelligence Systems: Springer International Publishing, pp. 188–199, 2015. Abstract
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Hassanien, A. E., J. M. H. Ali, and H. Nobuhara, "Detection of Spiculated Masses in Mammograms Based on Fuzzy Image Processing.", Artificial Intelligence and Soft Computing - ICAISC 2004, 7th International Conference, , Zakopane, Poland, Volume 3070/2004, 1002-1007, June 7-11, 2004. Abstract

This paper presents an efficient technique for the detection of spiculated massesin the digitized mammogram to assist the attending radiologist in making his decisions. The presented technique consists of two stages, enhancement of spiculation masses followed by the segmentation process. Fuzzy Histogram Hyperbolization (FHH) algorithm is first used to improve the quality of the digitized mammogram images. The Fuzzy C-Mean (FCM) algorithm is then applied to the preprocessed image to initialize the segmentation. Four measures of quantifying enhancement have been developed in this work. Each measure is based on the statistical information obtained from the labelled region of interest and a border area surrounding it. The methodology is based on the assumption that target and background areas are accurately specified. We have tested the algorithms on digitized mammograms obtained from the Digital Databases for Mammographic Image Analysis Society (MIAS).