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A
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.

Anter, A. M., 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. IGI Global, pp. 22–37, 2014. Abstract
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Fouad, M. M. M., H. zawbaa, N. Elbendary, and H. Aboul Ella, "Automatic Nile Tilapia Fish Classification Approach using Machine Learning Techniques", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 173-179, , Tunisia, , 4-6 Dec, 2013.
Fouad, M. M. M., H. M. Zawbaa, N. El-Bendary, and A. E. Hassanien, "Automatic nile tilapia fish classification approach using machine learning techniques", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 173–178, 2013. Abstract
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Anter, A. M., A. E. Hassanien, and G. Schaefer, "Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension", 2nd IAPR Asian Conference on Pattern Recognition (ACPR), 2013 , Okinawa, Japan. , 5 Nov. , 2013.
Anter, A. M., A. E. Hassanien, and G. Schaefer, "Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension", Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on: IEEE, pp. 937–941, 2013. Abstract
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Zawbaa, H. M., and A. E. Hassanien, Automatic Soccer Video Summarization, , Cairo, Cairo Unversity, 2012. Abstract

This thesis presents an automatic soccer video summarization system using machine learning (ML) techniques. The proposed system is composed of ve phases. Namely; in the pre-processing phase, the system segments the whole video stream into small video shots. Then, in the shot processing
phase, it applies two types of classi cation (shot type classi cation and play / break classification) to the video shots resulted from the pre-processing phase. Afterwards, in the replay detection phase, the proposed system applies two machine learning algorithms, namely; support vector machine (SVM) and arti cial neural network (ANN), for emphasizing important segments with championship logo appearance. Also, in the excitement event detection phase, the proposed system uses both machine learning algorithms for detecting the scoreboard which contain an information about the score of the game. The proposed system also uses k-means algorithm and Hough line transform for detecting vertical goal posts and Gabor lter for detecting goal net. Finally, in the event detection and summarization phase, the proposed system highlights the most important events during the match. Experiments on real soccer videos demonstrate encouraging results. The event detection and summarization has attained recall 94% and precision 97.3% for soccer match videos from ve international soccer championships.

Zawbaa, H. M., and A. E. Hassanien, Automatic Soccer Video Summarization, : Cairo Unversity, 2012. Abstract
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Xiao, K., S. H. Ho, and others, "Automatic unsupervised segmentation methods for mri based on modified fuzzy c-means", Fundamenta Informaticae, vol. 87, no. 3-4: IOS Press, pp. 465–481, 2008. Abstract
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Xiao, K., S. H. Ho, and others, "Automatic unsupervised segmentation methods for mri based on modified fuzzy c-means", Fundamenta Informaticae, vol. 87, no. 3-4: IOS Press, pp. 465–481, 2008. 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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B
Tharwat;, A., A. E. Hassanien;, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of support vector machine", Pattern recognition letter, 2017. AbstractWebsite

Support Vector Machine (SVM) parameters such as kernel parameter and penalty parameter (C) have a great impact on the complexity and accuracy of predicting model. In this paper, Bat algorithm (BA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. To evaluate the proposed model (BA-SVM), the experiment adopted nine standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the BA-SVM algorithm are compared with grid search, which is a conventional method of searching parameter values, and two well-known optimization algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results proved that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem. The results also demonstrated lower classification error rates compared with PSO and GA algorithms.

Alaa Tharwat, A. E. Hassanien, and B. E. Elnaghi, "A BA-based algorithm for parameter optimization of Support Vector Machine", Pattern Recognition Letters: North-Holland, 2016. Abstract
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Kilany, M., A. E. Hassanien, A. Badr, P. - W. Tsai, and J. - S. Pan, "A Behavioral Action Sequences Process Design", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 502–512, 2016. Abstract
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Esraa Elhariri, N. El-Bendary, A. M. M. Hussein, A. E. Hassanien, and A. Badr, "Bell Pepper Ripeness Classification based on Support Vector Machine ", The second International Conference on Engineering and Technology , German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
Esraa Elhariri, N. El-Bendary, A. M. M. Hussein, A. E. Hassanien, and A. Badr, "Bell pepper ripeness classification based on support vector machine", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
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el-hoseny, M., Alaa Tharwat, and A. E. Hassanien, "Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm", Journal of Computational Science, 2017. Website
Heba, E., M. Salama, A. E. Hassanien, and T. - H. Kim, "Bi-Layer Behavioral-Based Feature Selection Approach for Network Intrusion Classification", Security Technology - International Conference, SecTech 2011, pp.195-203, Jeju Island, Korea, December 8-10,, 2011. Abstract

To satisfy the ever growing need for effective screening and diagnostic tests, medical practitioners have turned their attention to high resolution, high throughput methods. One approach is to use mass spectrometry based methods for disease diagnosis. Effective diagnosis is achieved by classifying the mass spectra as belonging to healthy or diseased individuals. Unfortunately, the high resolution mass spectrometry data contains a large degree of noisy, redundant and irrelevant information, making accurate classification difficult. To overcome these obstacles, feature extraction methods are used to select or create small sets of relevant features. This paper compares existing feature selection methods to a novel wrapper-based feature selection and centroid-based classification method. A key contribution is the exposition of different feature extraction techniques, which encompass dimensionality reduction and feature selection methods. The experiments, on two cancer data sets, indicate that feature selection algorithms tend to both reduce data dimensionality and increase classification accuracy, while the dimensionality reduction techniques sacrifice performance as a result of lowering the number of features. In order to evaluate the dimensionality reduction and feature selection techniques, we use a simple classifier, thereby making the approach tractable. In relation to previous research, the proposed algorithm is very competitive in terms of (i) classification accuracy, (ii) size of feature sets, (iii) usage of computational resources during both training and classification phases.

Eid, H. F., M. A. Salama, A. E. Hassanien, and T. - H. Kim, "Bi-layer behavioral-based feature selection approach for network intrusion classification", International Conference on Security Technology: Springer Berlin Heidelberg, pp. 195–203, 2011. Abstract
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Eid, H. F., M. A. Salama, A. E. Hassanien, and T. - H. Kim, "Bi-layer behavioral-based feature selection approach for network intrusion classification", International Conference on Security Technology: Springer Berlin Heidelberg, pp. 195–203, 2011. Abstract
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Hassanien, A. - E., A. T. Azar, V. Snasel, J. Kacprzyk, and J. H. Abawajy, Big data in complex systems: challenges and opportunities, : Springer, 2015. Abstract
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Amin, I. I., A. E. Hassanien, S. K. Kassim, and H. A. Hefny, "Big DNA Methylation data analysis and visualizing in a common form of breast cancer", Big Data in Complex Systems: Springer International Publishing, pp. 375–392, 2015. Abstract
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Amin, K. M., M. A. Fattah, A. E. Hassanien, and G. Schaefer, "A binarization algorithm for historical arabic manuscript images using a neutrosophic approach", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 266–270, 2014. Abstract
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Salama, M. A., and A. E. Hassanien, "Binarization and Validation in Formal Concept Analysis", International Journal of Systems Biology and Biomedical Technologies, vol. 1, issue 4, pp. 17-28, 2012. AbstractWebsite

Representation and visualization of continuous data using the Formal Concept Analysis (FCA) became an
important requirement in real-life fields. Application of formal concept analysis (FCA) model on numerical
data, a scaling or Discretization / binarization procedures should be applied as preprocessing stage. The
Scaling procedure increases the complexity of computation of the FCA, while the binarization process leads to a distortion in the internal structure of the input data set. The proposed approach uses a binarization procedure prior to applying FCA model, and then applies a validation process to the generated lattice to measure or ensure its degree of accuracy. The introduced approach is based on the evaluation of each attribute according to the objects of its extent set. To prove the validity of the introduced approach, the technique is applied on two data sets in the medical field which are the Indian Diabetes and the Breast Cancer data sets. Both data sets show the generation of a valid lattice.

Salama, M. A., and A. E. Hassanien, "Binarization and validation in formal concept analysis", International Journal of Systems Biology and Biomedical Technologies (IJSBBT), vol. 1, no. 4: IGI Global, pp. 16–27, 2012. Abstract
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