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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 H. Asad, A. T. Azar, and A. E. Hassanien, "Integrated Features Based on Gray-Level and Hu Moment Invariants with Ant Colony System for Retinal Blood Vessels Segmentation", International Journal of Systems Biology and Biomedical Technologies, , vol. 1, issue 4, pp. 61-74, 2012. AbstractWebsite

Abnormality detection plays an important role in many real-life applications. Retinal vessel segmentation
algorithms are the critical components of circulatory blood vessel Analysis systems for detecting the various
abnormalities in retinal images. Traditionally, the vascular network is mapped by hand in a time-consuming
process that requires both training and skill. Automating the process allows consistency, and most importantly, frees up the time that a skilled technician or doctor would normally use for manual screening. Several studies were carried out on the segmentation of blood vessels in general; however, only a small number of them were associated to retinal blood vessels. In this paper, an approach for segmenting retinal blood vessels is
proposed using only ant colony system. Eight features are selected for the developed system; four are based on gray-level and the other features on Hu moment-invariants. The features are directly computed from values of image pixels, so they take about 90 seconds in computation. The performance of the proposed structure is evaluated in terms of accuracy, sensitivity and specificity. The results showed that the overall accuracy and sensitivity of the presented approach achieved 90.28% and 74%, respectively

Ahmed I. Hafez, AE Hassanien, F. A. A., "BNEM - A Fast Community Detection Algorithm using generative models", Social Network Analysis and Mining, , vol. 4(, issue 1, pp. 1-20,, 2014. AbstractWebsite

Actors in social networks tend to form community groups based on common location, interests, occupation, etc. Communities play special roles in the structure–function relationship; therefore, detecting such communities can be a way to describe and analyze such networks. However, the size of those networks has grown tremendously with the increase of computational power and data storage. While various methods have been developed to extract community structures, their computational cost or the difficulty to parallelize existing algorithms make partitioning real networks into communities a challenging problem. In this paper, we introduce a generative process to model the interactions between social network’s actors. Through unsupervised learning using expectation maximization, we derive an efficient and fast community detection algorithm based on Bayesian network and expectation maximization (BNEM). We show that BNEM algorithm can infer communities within directed or undirected networks, and within weighted or un-weighted networks. We also show that the algorithm is easy to parallelize. We then explore and analyze the result of the BNEM method. Finally, we conduct a comparative analysis with other well-known methods in the fields of community detection.

Ahmed Ibrahim Hafez, N. Ghali, A. E. Hassanien, and A. Fahmy, "Genetic Algorithms for Multi-Objective Community Detection in Complex Networks ", IEEE International Conference on Intelligent Systems Design and Applications (ISDA) , Kochi, India, pp. 460 - 465, Nov. 27-29 2012. Abstract

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.

Ahmed M. Anter, A. E. Hassenian, M. A. Elsoud, and M. F.Tolba, "Neutrosophic sets and fuzzy C-means clustering for improving CT liver image segmentation", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.
Ahmed M. Anter, and A. E. Hassenian, "Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation", Journal of Computational Science, 2018. Abstract

In this paper, an improved segmentation approach for abdominal CT liver tumor based on neutrosophic sets (NS), particle swarm optimization (PSO), and fast fuzzy C-mean algorithm (FFCM) is proposed. To increase the contrast of the CT liver image, the intensity values and high frequencies of the original images were removed and adjusted firstly using median filter approach. It is followed by transforming the abdominal CT image to NS domain, which is described using three subsets namely; percentage of truth T, percentage of falsity F, and percentage of indeterminacy I. The entropy is used to evaluate indeterminacy in NS domain. Then, the NS image is passed to optimized FFCM using PSO to enhance, optimize clusters results and segment liver from abdominal CT. Then, these segmented livers passed to PSOFCM technique to cluster and segment tumors. The experimental results obtained based on the analysis of variance (ANOVA) technique, Jaccard Index and Dice Coefficient measures show that, the overall accuracy offered by neutrosophic sets is accurate, less time consuming and less sensitive to noise and performs well on non-uniform CT images.

Ahmed M. Anter, M. A. Elsoud, and A. E. Hassanien, "Automatic Liver Parenchyma Segmentation from Abdominal CT Images", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) pp. 32 – 36, Cairo, EGYPT -, December 29-30, , 2013.
Ahmed M. Anter, M. A. Elsoud, and A. E. Hassanien, "Automatic Mammographic Parenchyma Classification According to BIRADS Dictionary", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, pp. 22-37,, 2014. Abstract

Internal.density.of.the.breast.is.a.parameter.that.clearly.affects.the.performance.of.segmentation.and.
classification.algorithms.to.define.abnormality.regions..Recent.studies.have.shown.that.their.sensitivity.
is.significantly.decreased.as.the.density.of.the.breast.is.increased..In.this.chapter,.enhancement.and. segmentation.processis applied to increase the computation and focus onmammographic parenchyma.
This.parenchyma is analyzed to discriminate tissue density according to BIRADS using Local Binary
Pattern.(LBP),.Gray.Level.Co-Occurrence.Matrix.(GLCM),.Fractal.Dimension.(FD),.and.feature.fusion.
technique.is.applied.to.maximize.and.enhance.the.performance.of.the.classifier.rate..The.different.methods.
for.computing.tissue.density.parameter.are.reviewed,.and.the.authors.also.present.and.exhaustively.
evaluate.algorithms.using.computer.vision.techniques..The.experimental.results.based.on.confusion.
matrix.and.kappa.coefficient.show.a.higher.accuracy.is.obtained.by.automatic.agreement.classification.

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.

Ajith Abraham, Aboul-Ella Hassanien, A. C. V. S., Foundations of Computational Intelligence Volume 6: Data Mining, , Germany, ISBN: 978-3-642-01090-3, Studies in Computational Intelligence, Springer Verlag, 2009. AbstractWebsite

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

Ajith Abraham, Aboul-Ella Hassanien, P. S. A. E., Foundations of Computational Intelligence Volume 3: Global Optimization, , Germany, Studies in Computational Intelligence, Springer Verlag, Vol. 203 , 2009. AbstractWebsite

Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.

Ajith Abraham, Aboul-Ella Hassanien, A. C., Foundations of Computational Intelligence Volume 4: Bio-Inspired Data Mining, , Germany, Studies in Computational Intelligence, Springer Verlag, 2009. AbstractWebsite

Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. Nature has been very successful in providing clever and efficient solutions to different sorts of challenges and problems posed to organisms by ever-changing and unpredictable environments. It is easy to observe that strong scientific advances have been made when issues from different research areas are integrated. A particularly fertile integration combines biology and computing. Computational tools inspired on biological process can be found in a large number of applications. One of these applications is Data Mining, where computing techniques inspired on nervous systems; swarms, genetics, natural selection, immune systems and molecular biology have provided new efficient alternatives to obtain new, valid, meaningful and useful patterns in large datasets.

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., S. Zamoon, A. E. Hassanien, and A. Abraham, " Rough Set Generating Prediction Rules for Stock Price Movement", The Second IEEE UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, England, UK, pp.111-116 , 8-10 September , 2008. Abstract

This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree and neural networks algorithms have been made. Rough sets show a higher overall accuracy rates reaching over 97% and generate more compact rules.

Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Al-Qaheri, H., A. E. Hassanien, and A. Abraham, "A Generic Scheme for Generating Prediction Rules Using Rough Sets", Rough Set Theory: A True Landmark in Data Analysis: Springer Berlin Heidelberg, pp. 163–186, 2009. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, " Rough Set Generating Prediction Rules for Stock Price Movement", The Second IEEE UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, England, UK, pp.111-116 , 8-10 September , 2008. Abstract

This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree and neural networks algorithms have been made. Rough sets show a higher overall accuracy rates reaching over 97% and generate more compact rules.

Al-Qaheri, H., A. E. Hassanien, and A. Abraham, "A Generic Scheme for Generating Prediction Rules Using Rough Sets", Rough Set Theory: A True Landmark in Data Analysis: Springer Berlin Heidelberg, pp. 163–186, 2009. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 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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Al-Shammari, E. T., A. E. Hassanien, and O. Saleh, "Protecting Patient Privacy against Unauthorized Release of Medical Images Using Weighted Quantum Particle Swarm Optimization Algorithm", 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp 667- 671, Okinawa, Japan, 2013.
Al-Shammari, E. T., A. E. Hassanien, and O. S. Al-Razgan, "Protecting Patient Privacy against Unauthorized Release of Medical Images Using Weighted Quantum Particle Swarm Optimization Algorithm", Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on: IEEE, pp. 667–671, 2013. Abstract
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Alaa Tharwat, Hani Mahdi, Adel El Hennawy, and A. E. Hassanien, "Face Sketch Synthesis and Recognition Based on Linear Regression Transformation and Multi-Classifier Technique", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015. Abstract

Fish detection and identi cation are important steps towards
monitoring sh behavior. The importance of such monitoring step comes
from the need for better understanding of the sh ecology and issuing
conservative actions for keeping the safety of this vital food resource.
The recent advances in machine learning approaches allow many appli-
cations to easily analyze and detect a number of sh species. The main
competence between these approaches is based on two main detection
parameters: the time and the accuracy measurements. Therefore, this
paper proposes a sh detection approach based on BAT optimization
algorithm (BA). This approach aims to reduce the classi cation time
within the sh detection process. The performance of this system was
evaluated by a number of well-known machine learning classi ers, KNN,
ANN, and SVM. The approach was tested with 151 images to detect the
Nile Tilapia sh species and the results showed that k-NN can achieve
high accuracy 90%, with feature reduction ratio close to 61% along with
a noticeable decrease in the classi cation time.

Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Two biometric approaches for cattle identification based on features and classifiers fusion", International Journal of Image Mining, vol. 1, no. 4: Inderscience Publishers (IEL), pp. 342–365, 2015. Abstract
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Alaa Tharwat, B. E. Elnaghi, A. M. Ghanem, and A. E. Hassanien, "Automatically Human Age Estimation Approach via Two-Dimensional Facial Image Analysis", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 491–501, 2016. Abstract
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