Publications

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Thesis
Kareem Kamal A.Ghany, and A. E. Hassanien, An Intelligent Hybrid Biometrics System, , Cairo, EGYPT , Cairo University , 2014. thesis_presentation.pdf
Miscellaneous
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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Hassanien, A. E., T. - H. Kim, J. Kacprzyk, and A. I. Awad, Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations, : Springer, 2014. Abstract
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Kacprzyk, J., A. Abraham, A. - E. Hassanien, and F. Herrera, Foundations of Computational Intelligence Volume 2: Approximate Reasoning, : Springer Berlin Heidelberg, 2009. Abstract
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Kacprzyk, J., A. Abraham, A. P. L. F. de Carvalho, and A. - E. Hassanien, Foundations of Computational Intelligence Volume 4: Bio-Inspired Data Mining, : Springer Berlin Heidelberg, 2009. Abstract
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Hassanien, A. E., M. M. Fouad, A. A. Manaf, M. Zamani, R. Ahmad, and J. Kacprzyk, Multimedia Forensics and Security: Foundations, Innovations, and Applications, : Springer, 2016. Abstract
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Journal Article
Mukherjee, A., N. Dey, N. Kausar, A. S. Ashour, R. Taiar, and A. E. Hassanien, " A Disaster Management Specific Mobility Model for Flying Ad-hoc Network", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 3, issue 3, 2016. AbstractWebsite

The extended Mobile Ad-hoc Network architecture is a paramount research domain due to a wide enhancement of smart phone and open source Unmanned Aerial Vehicle (UAV) technology. The novelty of the current work is to design a disaster aware mobility modeling for a Flying Ad-hoc network infrastructure, where the UAV group is considered as nodes of such ecosystem. This can perform a collaborative task of a message relay, where the mobility modeling under a “Post Disaster” is the main subject of interest, which is proposed with a multi-UAV prototype test bed. The impact of various parameters like UAV node attitude, geometric dilution precision of satellite, Global Positioning System visibility, and real life atmospheric upon the mobility model is analyzed. The results are mapped with the realistic disaster situation. A cluster based mobility model using the map oriented navigation of nodes is emulated with the prototype test bed.

Karam, H., A. E. Hassanien, and M. Nakajima, "15-1 Polar Decomposition Interpolations for Linear Fractal Metamorphosis", 映像情報メディア学会年次大会講演予稿集, no. 1998: 一般社団法人映像情報メディア学会, pp. 200–201, 1998. Abstract
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Hassanien, A. E., H. Karam, and M. Nakajima, "15-2 Image Metamorphosis for Inter Slice Interpolation of Medical Images", 映像情報メディア学会年次大会講演予稿集, no. 1998: 一般社団法人映像情報メディア学会, pp. 202–203, 1998. Abstract
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Hassanien, A. E., T. - H. Kim, P. S. Rajan, and K. K. K. Hari, "Analysis of Energy Utilization through Mobile Ad Hoc Network with AODV", Proc. of the Intl. Conf. on Computer Applications, vol. 1, 2012. Abstract

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Karam, H., A. - E. Hassanien, and M. Nakajima, "Animation of linear fractal shapes using polar decomposistion interpolation", Journal of ITE, vol. 53, no. 3, 1999. Abstract
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El-Bendary, N., T. - H. Kim, A. E. Hassanien, and M. Sami, "Automatic image annotation approach based on optimization of classes scores", Computing -Spriner , vol. 96, issue 5, pp. 381-402 , 2014. Website
El-Bendary, N., T. - H. Kim, A. E. Hassanien, and M. Sami, "Automatic image annotation approach based on optimization of classes scores", Computing, vol. 96, no. 5: Springer Vienna, pp. 381–402, 2014. Abstract
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Lamiaa M. El Bakrawy, N. I.Ghali, T. - H. Kim, and A. E. Hassanien, "A Block-wise-based Fragile Watermarking Hybrid Approach using Rough Sets and Exponential Particle Swarm Optimization ", Journal of Future Generation Communication and Networking, , vol. 4, issue 4, 2011. Abstractblock-wise-based_fragile_watermarking.pdf

In this paper, we propose a fragile watermarking hybrid approach using rough set kmeans and exponential particle swarm optimization (EPSO) systems. It is based on a block-wise dependency mechanism which can detect any alterations made to the protected image. Initially, the input image is divided into blocks with equal size in order to improve image tamper localization precision. Then feature sequence is generated by applying rough k-means and EPSO clustering to create the relationship between all image blocks and cluster
all of them since EPSO is used to optimize the parameters of rough k-means. Both feature sequence and generated secret key are used to construct the authentication data. Each resultant 8-bit authentication data is embedded into the eight least significant bits (LSBs) of the corresponding image block. We gives experimental results which show the feasibility of using these optimization algorithms for the fragile watermarking and demonstrate the
accuracy of the proposed approach. The performance comparison of the approach was also realized. The performance of a fragile watermarking approach has been improved in this paper by using exponential particle swarm optimization (EPSO) to optimize the rough kmean parameters. The proposed approach can embed watermark without causing noticeable visual artifacts, and does not only achieve superior tamper detection in images accurately,
it also recovers tampered regions effectively. In addition, the results show that the proposed approach can effectively thwart different attacks, such as the cut-and paste attack and collage attack, while sustaining superior tamper detection and localization accuracy.

El Bakrawy, L. M., N. I. Ghali, T. - H. Kim, and A. E. Hassanien, "A Block-wise-based Fragile Watermarking Hybrid Approach using Rough Sets and Exponential Particle Swarm Optimization", International Journal of Future Generation Communication and Networking, vol. 4, no. 4, pp. 77–88, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, T. - H. Kim, and A. E. Hassanien, "A Block-wise-based Fragile Watermarking Hybrid Approach using Rough Sets and Exponential Particle Swarm Optimization", International Journal of Future Generation Communication and Networking, vol. 4, no. 4, pp. 77–88, 2011. Abstract
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Hassanien, A. E., and T. - H. Kim, "Breast cancer diagnosis system based on machine learning techniques", Applied Logic journal, vol. 10, issue 4, pp. 277–284, 2012. AbstractWebsite

This article introduces a hybrid approach that combines the advantages of fuzzy sets, pulse coupled neural networks (PCNNs), and support vector machine, in conjunction with wavelet-based feature extraction. An application of breast cancer MRI imaging has been chosen and hybridization approach has been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: normal or non-normal. The introduced approach starts with an algorithm based on type-II fuzzy sets to enhance the contrast of the input images. This is followed by performing PCNN-based segmentation algorithm in order to identify the region of interest and to detect the boundary of the breast pattern. Then, wavelet-based features are extracted and normalized. Finally, a support vector machine classifier was employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented approach, we present tests on different breast MRI images. The experimental results obtained, show that the overall accuracy offered by the employed machine learning techniques is high compared with other machine learning techniques including decision trees, rough sets, neural networks, and fuzzy artmap.

Hassanien, A. E., and T. - H. Kim, "Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic, vol. 10, no. 4: Elsevier, pp. 277–284, 2012. Abstract
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Ashour, A. S., S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, and A. E. Hassanien, "Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach", Journal of Signal and Information Processing, vol. 6, pp. 244-257, 2015. Abstractjsip_2015083113193757_1.pdfWebsite

Medical image enhancement is an essential process for superior disease diagnosis as well as for
detection of pathological lesion accurately. Computed Tomography (CT) is considered a vital medical
imaging modality to evaluate numerous diseases such as tumors and vascular lesions. However,
speckle noise corrupts the CT images and makes the clinical data analysis ambiguous.
Therefore, for accurate diagnosis, medical image enhancement is a must for noise removal and
sharp/clear images. In this work, a medical image enhancement algorithm has been proposed using
log transform in an optimization framework. In order to achieve optimization, a well-known
meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to determine the optimal
parameter settings for log transform. The performance of the proposed technique is studied on a
low contrast CT image dataset. Besides this, the results clearly show that the CS based approach
has superior convergence and fitness values compared to PSO as the CS converge faster that
proves the efficacy of the CS based technique. Finally, Image Quality Analysis (IQA) justifies the robustness >
of the proposed enhancement technique.

Ashour, A. S., S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, A. E. Hassanien, and others, "Computed tomography image enhancement using cuckoo search: a log transform based approach", Journal of Signal and Information Processing, vol. 6, no. 03: Scientific Research Publishing, pp. 244, 2015. Abstract
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Mukherjee, A., N. Dey, N. Kausar, A. S. Ashour, R. Taiar, and A. E. Hassanien, "A disaster management specific mobility model for flying ad-hoc network", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 3, no. 3: IGI Global, pp. 72–103, 2016. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Event detection based approach for soccer video summarization using machine learning", Int J Multimed Ubiquitous Eng, vol. 7, no. 2, pp. 63–80, 2012. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Event detection based approach for soccer video summarization using machine learning", Int J Multimed Ubiquitous Eng, vol. 7, no. 2, pp. 63–80, 2012. Abstract
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