Publications

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2004
Hassanien, A., J. Ali, and H. Nobuhara, "Detection of spiculated masses in Mammograms based on fuzzy image processing", Artificial Intelligence and Soft Computing-ICAISC 2004: Springer Berlin/Heidelberg, pp. 1002–1007, 2004. Abstract
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Hassanien, A., J. Ali, and H. Nobuhara, "Detection of spiculated masses in Mammograms based on fuzzy image processing", Artificial Intelligence and Soft Computing-ICAISC 2004: Springer Berlin/Heidelberg, pp. 1002–1007, 2004. Abstract
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Hassanien, A. E., and J. M. Ali, "Digital mammogram segmentation algorithm using pulse coupled neural networks", Image and Graphics (ICIG'04), Third International Conference on: IEEE, pp. 92–95, 2004. Abstract
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Hassanien, A. E., and J. M. Ali, "Digital mammogram segmentation algorithm using pulse coupled neural networks", Image and Graphics (ICIG'04), Third International Conference on: IEEE, pp. 92–95, 2004. 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).

2008
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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2009
Terzopoulos, D., C. McIntosh, T. McInerney, and G. Hamarneh, "Deformable Organisms", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 433–474, 2009. Abstract
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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.

2010
Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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Hassanien, A. E., Developing Advanced Web Services Through P2P Computing And Autonomous Agents: Trends And Innovations, , USA, IGI-Global USA, 2010. AbstractWebsite

In recent years, the development of distributed systems, in particular the Internet, has been influenced heavily by three paradigms: peer-to-peer, autonomous agents, and service orientation. Developing Advanced Web Services through P2P Computing and Autonomous Agents: Trends and Innovations establishes an understanding of autonomous peer-to-peer Web Service models and developments as well as extends growing literature on emerging technologies. This scholarly publication is an important reference for researchers and academics working in the fields of peer-to-peer computing, Web and grid services, and agent technologies.

2012
Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density based fuzzy thresholding for image segmentation", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 118–127, 2012. Abstract
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Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density based fuzzy thresholding for image segmentation", International Conference on Advanced Machine Learning Technologies and Applications: Springer Berlin Heidelberg, pp. 118–127, 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", 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", Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on: IEEE, pp. 177–182, 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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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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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, 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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Salama, M., Data Mining for Medical Informatics, , Cairo, Cairo Unv, 2012. AbstractThesis.pdfPresentation.pdf

The work presented in this thesis investigates the nature of real-life data, mainly in the medical field, and the problems in handling such nature by the conventional data mining techniques. Accordingly, a set of alternative techniques are proposed in this thesis to handle the medical data in the three stages of data mining process. In the first stage which is preprocessing, a proposed technique named as interval-based feature evaluation technique that depends on a hypothesis that the decrease of the overlapped interval of values for every class label leads to increase the importance of such attribute. Such technique handles the difficulty of dealing with continuous data attributes without the need of applying discretization of the input and it is proved by comparing the results of the proposed technique to other attribute evaluation and selection techniques. Also in the preprocessing stage, the negative effect of normalization algorithm before applying the conventional PCA has been investigated and how the avoidance of such algorithm enhances the resulted classification accuracy. Finally in the preprocessing stage, an experimental analysis introduces the ability of rough set methodology to successfully classify data without the need of applying feature reduction technique. It shows that the overall classification accuracy offered by the employed rough set approach is high compared with other machine learning techniques including Support Vector Machine, Hidden Naive Bayesian network, Bayesian network and other techniques.
In the machine learning stage, frequent pattern-based classification technique is proposed; it depends on the detection of variation of attributes among objects of the same class. The preprocessing of the data like standardization, normalization, discretization or feature reduction is not required in this technique which enhances the performance in time and keeps the original data without being distorted. Another contribution has been proposed in the machine learning stage including the support vector machine and fuzzy c-mean clustering techniques; this contribution is about the enhancement of the Euclidean space calculations through applying the fuzzy logic in such calculations. This enhancement has used chimerge feature evaluation techniques in applying fuzzification on the level of features. A comparison is applied on these enhanced techniques to the other classical data mining techniques and the results shows that classical models suffers from low classification accuracy due to the dependence of un-existed presumption.
Finally, in the visualization stage, a proposed technique is presented to visualize the continuous data using Formal Concept Analysis that is better than the complications resulted from the scaling algorithms.

Li, J., B. Dai, K. Xiao, and A. E. Hassanien, "Density Based Fuzzy Thresholding for Image Segmentation", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, pp. 118--127, 2012. Abstract3220118.pdf

In this paper, we introduce an image segmentation framework which
applies automatic threshoding selection using fuzzy set theory and fuzzy
density model. With the use of different types of fuzzy membership function,
the proposed segmentation method in the framework is applicable for images of
unimodal, bimodal and multimodal histograms. The advantages of the method
are as follows: (1) the threshoding value is automatically retrieved thus requires
no prior knowledge of the image; (2) it is not based on the minimization of a
criterion function therefore is suitable for image intensity values distributed
gradually, for example, medical images; (3) it overcomes the problem of local
minima in the conventional methods. The experimental results have
demonstrated desired performance and effectiveness of the proposed approach.

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.