Publications

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Book Chapter
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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Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 897–914, 2017. Abstract
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Sayed, G. I., M. Soliman, and A. E. Hassanien, "Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 487–506, 2016. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Bio-inspiring Techniques in Watermarking Medical Images: A Review", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 93–114, 2014. Abstract
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Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, and A. E. Hassanien, "Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 547–557, 2015. Abstract
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Amin, R., T. Gaber, G. ElTaweel, and A. E. Hassanien, "Biometric and traditional mobile authentication techniques: Overviews and open issues", Bio-inspiring cyber security and cloud services: trends and innovations: Springer Berlin Heidelberg, pp. 423–446, 2014. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind Robust 3D-Watermarking Scheme Based on Progressive Mesh and Self Organization Maps", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 131–142, 2013. Abstract
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Mouhamed, M. R., H. M. Zawbaa, E. T. Al-Shammari, A. E. Hassanien, and V. Snasel, "Blind watermark approach for map authentication using support vector machine", Advances in security of information and communication networks: Springer Berlin Heidelberg, pp. 84–97, 2013. Abstract
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Fouad, M. M., K. M. Amin, N. El-Bendary, and A. E. Hassanien, "Brain Computer Interface: A Review", Brain-Computer Interfaces: Springer International Publishing, pp. 3–30, 2015. Abstract
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Conference Paper
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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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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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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Sahlol, A. T., C. Y. Suen, H. M. Zawbaa, A. E. Hassanien, and M. A. Fattah, "Bio-inspired BAT optimization algorithm for handwritten Arabic characters recognition", Evolutionary Computation (CEC), 2016 IEEE Congress on: IEEE, pp. 1749–1756, 2016. Abstract
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Elharir, E., N. El-Bendary, and A. E. Hassanien, "Bio-inspired optimization for feature set dimensionality reduction", 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA),, Beirut, Lebanon, 13-15 July , 2016. Abstract

In this paper, two novel bio-inspired optimization algorithms; namely Dragonfly Algorithm (DA) and Grey Wolf Optimizer (GWO), have been applied for fulfilling the goal of feature set dimensional reduction. The proposed classification system has been tested via solving the problem of Electromyography (EMG) signal classification with optimal features subset selection. The obtained experimental results showed that the GWO based Support Vector Machines (SVM) classification algorithm has achieved an accuracy of 93.22% using 31% of the total extracted features. It also outperformed both the typical SVM algorithm, with no feature set optimization, and the DA based optimized feature set SVM classification, for the tested EMG dataset.

Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "Bio-inspired optimization for feature set dimensionality reduction", Advances in Computational Tools for Engineering Applications (ACTEA), 2016 3rd International Conference on: IEEE, pp. 184–189, 2016. Abstract
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Asmaa Hashem Sweidan, Nashwa El-Bendary, O. M. H. A. E. H.:, "Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, pp. 547-557,, Ostrava, Czech Republic, June 29 - July , 2015. Abstract

This paper presents Case-Based Reasoning (CBR) system to asses water pollution based on fish liver histopathology as biomarker. The proposed approach utilizes fish liver microscopic images in order to asses water pollution based on knowledge stored in the case-based database and stores likelihood description of the previous solutions in order to make the knowledge stored more flexible. The proposed case-based reasoning system consists of 5 phases; namely case representation (pre-processing and feature extraction), retrieve, reuse/adapt, revise, and retain phases. After applying pre-processing and feature extraction algorithms on the input images, similarity between the input and case base database is being calculated in order to retrieve similarity. Experimental results show that the performance of CBR systems increases according to the number of retrieved cases in each scenario against each strategy. The proposed system achieved 95.9

Raffat, M. M., R. Ahmed, and A. E. Hassanien, "Blind 2D vector data watermarking approach using random table and polar coordinates", 2nd IEEE International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE): 14-15 Aug., 2012. Abstract
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Mouhamed, M. R., A. M. Rashad, and A. E. Hassanien, "Blind 2D vector data watermarking approach using random table and polar coordinates", Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on: IEEE, pp. 67–70, 2012. Abstract
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Raffat, M. M., R. Ahmed, and A. E. Hassanien, "Blind 2D vector data watermarking approach using random table and polar coordinates", 2nd IEEE International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE): 14-15 Aug., 2012. Abstract
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Alshabrawy, O. S., A. E. Hassanien, W. A. Awad, and A. Salama, "Blind Separation of Underdetermined Mixtures with Additive White and Pink Noises", 13th IEEE International Conference on Hybrid Intelligent Systems (HIS13) Tunisia, pp. 306-312, 2013, Tunisia, 4-6 Dec, 2013.
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