Publications

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Conference Paper
Hossam Moftah, Walaa Elmasry, A. E. Hassanien, Adel Alimi, H. Karray, and M. Tolba, "Ant-based clustering algorithm for magnetic resonance breast image segmentation", 13th IEEE International Conference on Hybrid Intelligent Systems | (HIS13) . pp. 162-167, Tunisia, , 4-6 Dec, 2013.
Moftah, H. M., A. E. Hassanien, A. M. Alimi, H. Karray, and M. F. Tolba, "Ant-based clustering algorithm for magnetic resonance breast image segmentation", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 161–166, 2013. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. A. Hefny, "Applying formal concept analysis for visualizing DNA methylation status in breast cancer tumor subtypes", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 37–42, 2013. Abstract
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Amin, I. I., S. K. Kassim, A. E. Hassanien, and H. A. Hefny, "Applying formal concept analysis for visualizing DNA methylation statusamong breast cancer tumors subtypes", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) pp. 37 - 42, Cairo, EGYPT -, December 29-30, , 2013.
Fattah, M. A., M. A. A. ELsoud, A. E. Hassanien, and T. - H. Kim, "Automated classification of galaxies using invariant moments", International Conference on Future Generation Information Technology: Springer Berlin Heidelberg, pp. 103–111, 2012. Abstract
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Fattah, M. A., M. A. A. ELsoud, A. E. Hassanien, and T. - H. Kim, "Automated classification of galaxies using invariant moments", International Conference on Future Generation Information Technology: Springer Berlin Heidelberg, pp. 103–111, 2012. 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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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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Ismail, F. H., E. A. Hassan, A. E. Hassanien, and T. - H. Kim, "Blog Clustering with Committee Approach", 2015 Fourth International Conference on Information Science and Industrial Applications (ISI): IEEE, pp. 61–64, 2015. Abstract
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Hassanien, A. E., N. El-Bendary, M. Kudělka, and Václav Snášel, "Breast cancer detection and classification using support vector machines and pulse coupled neural network", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 269–279, 2013. Abstract
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Hassanien, A. E., N. El-Bendary, M. Kudělka, and Václav Snášel, "Breast cancer detection and classification using support vector machines and pulse coupled neural network", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 269–279, 2013. Abstract
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Hassanien, A. E., N. El-Bendary, M. Kudělka, and Václav Snášel, "Breast cancer detection and classification using support vector machines and pulse coupled neural network", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 269–279, 2013. Abstract
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Hamdy, A., N. El-Bendary, A. Khodeir, M. M. M. Fouad, A. E. Hassanien, and H. Hefny, "Cardiac disorders detection approach based on local transfer function classifier", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 55–61, 2013. Abstract
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Ayeldeen, H., M. A. Fattah, O. Shaker, A. E. Hassanien, and T. - H. Kim, "Case-Based Retrieval Approach of Clinical Breast Cancer Patients", Computer, Information and Application (CIA), 2015 3rd International Conference on: IEEE, pp. 38–41, 2015. Abstract
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Torky, M., R. Baberse, R. Ibrahim, A. E. Hassanien, G. Schaefer, I. Korovin, and S. Y. Zhu, "Credibility investigation of newsworthy tweets using a visualising Petri net model", Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on: IEEE, pp. 003894–003898, 2016. Abstract
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Hamdy, E., A. Adl, A. E. Hassanien, O. Hegazy, and T. - H. Kim, "Criminal Act Detection and Identification Model", Advanced Communication and Networking (ACN), 2015 Seventh International Conference on: IEEE, pp. 79–83, 2015. Abstract
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Jagatheesan, K., B. Anand, N. Dey, T. Gaber, A. E. Hassanien, and T. - H. Kim, "A Design of PI Controller using Stochastic Particle Swarm Optimization in Load Frequency Control of Thermal Power Systems", Information Science and Industrial Applications (ISI), 2015 Fourth International Conference on: IEEE, pp. 25–32, 2015. 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 %).

Kotyk, T., N. Dey, A. S. Ashour, C. V. A. Drugarin, T. Gaber, A. E. Hassanien, and V. Snasel, "Detection of Dead stained microscopic cells based on Color Intensity and Contrast", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 57–68, 2016. Abstract
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Ahmad. Taher Azar, A. E. Hassanien, and T. - H. Kim, "Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis.", International Conference on Bio-Science and Bio-Technology (BSBT2012), , Kangwondo, Korea. pp. 94--105, December 16-19, 2012. Abstract3530094.pdf

The thyroid, an endocrine gland that secretes hormones in the blood, circulates its products to all tissues of the body, where they control vital functions in every cell. Normal levels of thyroid hormone help the brain, heart, intestines, muscles and reproductive system function normally. Thyroid hormones control the metabolism of the body. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Therefore, the correct diagnosis of these diseases is very important topic. In this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features (LHNFCSF) is presented for diagnosis of thyroid diseases. The performance evaluation of this system is estimated by using classification accuracy and k-fold cross-validation. The results indicated that the classification accuracy without feature selection was 98.6047% and 97.6744% during training and testing phases, respectively with RMSE of 0.02335. After applying feature selection algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase. However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for each class, 90.6977% using two clusters, 91.8605% using three clusters and 97.6744% using four clusters for each class and 12 fuzzy rules. The obtained classification accuracy was very promising with regard to the other classification applications in literature for this problem.

Nadi, M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Falling Detection System Based on Machine Learning", Advanced Information Technology and Sensor Application (AITS), 2015 4th International Conference on: IEEE, pp. 71–75, 2015. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A fast and secure one-way hash function", International Conference on Security Technology: Springer Berlin Heidelberg, pp. 85–93, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A fast and secure one-way hash function", International Conference on Security Technology: Springer Berlin Heidelberg, pp. 85–93, 2011. Abstract
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