Publications

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Thesis
Bakrawy, L. M. E., A. - E. " "Hassanien, and N. I. Ghali, Machine Learning in Image Authentication, , Cairo, Al-Azhar University, 2012. Abstract

In recent years image authentication has gained substantial attraction by the research community. It promises the solution to many problems such as content piracy, illicit manipulation of medical/legal documents, content security and so on. As watermark-based image authentication approaches are efficient and attractive, some types of watermarks such as logos, labels, trademark, or random sequence representing the author’s ownership, are mbedded into the desired digital image. Generally, a registration to the authentication center is necessary, which helps to solve ownership disputes by identifying the owner of the disputed media. If necessary, the embedded watermark in the digital image can be used to verify ownership Due to the open environment of Internet downloading, copyright protection introduces a new set of challenging problems regarding security and illegal distribution of privately owned images. One solution to these problems is digital watermarking, i.e., the insertion of information into the image data in such a way that the added information is not visible and yet resistant to image alterations. A watermarking technique is to prevent digital images that belong to rightful owners from being illegally commercialized or used, and it can verify the intellectual property right. The watermark should be robust and transparent, but the ways of pursuing transparency and robustness are conflict. For instance, if we would like to concentrate on the transparency issue, it is natural to embed the smallest modulation into images whenever possible. However, due to such small values in the embedded watermark, attacks can easily destroy the problems The first proposed solution is based on the associative watermarking and vector quantization. It achieves more effective against several images processing such as blurring, sharpening adding in Gaussian noise, cropping, and JPEG lossy compression especially in case of Gaussian noise and blurring. Also this technique is implemented to hide biometric data, fingerprint image, over three different types of medical images: CT, MRI and interventional images. It also achieves an effective resistance against several images processing such as JPEG lossy compression, sharpening, blurring and adding in Gaussian noise The second contribution in this thesis is strict authentication of multimodal biometric images using an improved secure hash function (ISHA-1) and near sets. It indicates that the proposed hash function is collision resistant and assures a good compression and preimage resistance. Also it reduces the time of implementation comparing to standard secure hash function. Moreover, the difference in time between SHA-1 and ISHA-1 increases by increasing the number of letters in message since the running time of implementation of ISHA-1 is limited compared to the running time of implementation of SHA-1. Also the proposed approach guarantees the security assurance and reduces the time of implementation. The third proposed contribution is fragile watermarking approach for image authentication based on rough k-means only and hybridization of rough k-means and particle swarm optimization. It 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, it shows 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. Moreover, the running time of implemented hybrid system is limited compared to the running time of the implemented rough k-means only. Especially, when we used exponential particle swarm optimization to optimize the parameters of rough k-means.

Miscellaneous
Dey, N., V. Bhateja, and A. E. Hassanien, Medical Imaging in Clinical Applications: Algorithmic and Computer-Based Approaches, : Springer, 2016. 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
Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "Machine learning-based soccer video summarization system", Multimedia, Computer Graphics and Broadcasting: Springer Berlin Heidelberg, pp. 19–28, 2011. Abstract
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Kompatsiaris, Y., S. Nikolopoulos, T. Lidy, and A. Rauber, "Media Search Cluster White Paper on" Search Computing".", ERCIM News, vol. 2012, no. 88, 2012. Abstract
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abd elaziz, M., and A. E. Hassanien, "Modified cuckoo search algorithm with rough sets for feature selection", Neural Computing and Applications: Springer London, pp. 1–10, 2016. Abstract
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abd elaziz, M., and A. E. Hassanien, "Modified cuckoo search algorithm with rough sets for feature selection", Download PDF Neural Computing and Applications, vol. 29, issue 4, pp. 925–934, 2018. AbstractWebsite

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

abd elaziz, M., and A. E. Hassanien, "Modified cuckoo search algorithm with rough sets for feature selection,", Neural Computing and Applications,, pp. pp.1-10, 2017, 2017. AbstractWebsite

In this paper, a modified cuckoo search algorithm with rough sets is presented to deal with high dimensionality data through feature selection. The modified cuckoo search algorithm imitates the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds. The modified cuckoo search uses the rough sets theory to build the fitness function that takes the number of features in reduct set and the classification quality into account. The proposed algorithm is tested and validated benchmark on several benchmark datasets drawn from the UCI repository and using different evaluation criteria as well as a further analysis is carried out by means of the Analysis of Variance test. In addition, the proposed algorithm is experimentally compared with the existing algorithms on discrete datasets. Finally, two learning algorithms, namely K-nearest neighbors and support vector machines are used to evaluate the performance of the proposed approach. The results show that the proposed algorithm can significantly improve the classification performance.

Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Staelens, S., and I. Lemahieu, "Monte Carlo Based Image Reconstruction in Emission Tom ography", Computational Intelligence in Medical Imaging: Techniques and Applications: CRC Press, pp. 407, 2009. Abstract
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Sayed, G. I., and A. E. Hassanien, "Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images", Applied Intelligence: Springer US, pp. 1–12, 2017. Abstract
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M.Moftah, A. E. Hassanien, A. Taher, and M. Shoman, "MRI Breast cancer diagnosis approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier", Applied Soft Computing, Elsiever, vol. 14, issue Part A, pp. 62-71, 2014. Website
Hassanien, A. E., and T. - H. Kim, "MRI Breast cancer diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic - Elsevier, 2012. Abstract

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 MRI breast cancer imaging has been chosen and hybridization approach have 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 were 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, "MRI Breast cancer diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic-Elsevier, 2012. Abstract
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Hassanien, A. E., and T. - H. Kim, "MRI Breast cancer diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic-Elsevier, 2012. Abstract
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Hassanien, A. E., and T. - H. Kim, "MRI Breast cancer diagnosis approach using support vector machine and pulse coupled neural networks", Journal of Applied Logic-Elsevier, 2012. Abstract
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Hassanien, A. E., H. M. Moftah, A. T. Azar, and M. Shoman, "MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier", Applied Soft Computing, vol. 14: Elsevier, pp. 62–71, 2014. Abstract
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Abraham, A., H. Liu, and A. E. Hassanien, "Multi swarms for neighbor selection in peer-to-peer overlay networks", Telecommunication Systems, vol. 46, no. 3: Springer Netherlands, pp. 195–208, 2011. Abstract
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Abraham, A., H. Liu, and A. E. Hassanien, "Multi swarms for neighbor selection in peer-to-peer overlay networks", Telecommunication Systems, vol. 46, no. 3: Springer Netherlands, pp. 195–208, 2011. Abstract
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Waleed Yamany, N. El-Bendary, A. E. Hassanien, and Eid Emary, "Multi-Objective Cuckoo Search Optimization for Dimensionality Reduction", Procedia Computer Science, vol. 96: Elsevier, pp. 207–215, 2016. Abstract
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Emary, E., Waleed Yamany, A. E. Hassanien, and V. Snasel, "Multi-objective gray-wolf optimization for attribute reduction", Procedia Computer Science, vol. 65: Elsevier, pp. 623–632, 2015. Abstract
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Moftah, H. M., A. E. Hassanien, N. Ghali, and M. Shoman, Multi-objective optimization K-mean segmentation approach for MRI Breast Images, , 2012. Abstract
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M.Rizk-Allaha, R., A. E. Hassanien, and M. Elhoseny, "A multi-objective transportation model under neutrosophic environment", Computers & Electrical Engineering, 2018. AbstractWebsite

In this paper, a new compromise algorithm for multi-objective transportation problem (MO-TP) is developed, which is inspired by Zimmermann's fuzzy programming and the neutrosophic set terminology. The proposed NCPA is characterized by assigning three membership functions for each objective namely, truth membership, indeterminacy membership and falsity membership. With the membership functions for all objectives, a neutrosophic compromise programming model is constructed with the aim to find best compromise solution (BCS). This model can cover a wide spectrum of BCSs by controlling the membership functions interactively. The performance of the NCPA is validated by measuring the ranking degree using TOPSIS approach. Illustrative examples are reported and compared with exists models in the literature. Based on the provided comparisons, NCPA is superior to fuzzy and different approaches.

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