Publications

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Abdel-Aziz, A. S., A. E. Hassanien, A. T. Azar, and S. E. - O. Hanafi, "Machine learning techniques for anomalies detection and classification", Advances in security of information and communication networks: Springer Berlin Heidelberg, pp. 219–229, 2013. 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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Ahmed, S., T. Gaber, Alaa Tharwat, and A. E. Hassanien, "Muzzle-based Cattle Identification using Speed up Robust Feature Approach", IEEE International Conference on Intelligent Networking and Collaborative Systems, ,015, pp. 99-104, Taipei, Taiwan, 2-4 September , 2015. Abstractabo1.pdf

Starting from the last century, animals identification
became important for several purposes, e.g. tracking,
controlling livestock transaction, and illness control. Invasive and
traditional ways used to achieve such animal identification in
farms or laboratories. To avoid such invasiveness and to get more
accurate identification results, biometric identification methods
have appeared. This paper presents an invariant biometric-based
identification system to identify cattle based on their muzzle
print images. This system makes use of Speeded Up Robust
Feature (SURF) features extraction technique along with with
minimum distance and Support Vector Machine (SVM) classifiers.
The proposed system targets to get best accuracy using minimum
number of SURF interest points, which minimizes the time
needed for the system to complete an accurate identification.
It also compares between the accuracy gained from SURF
features through different classifiers. The experiments run 217
muzzle print images and the experimental results showed that
our proposed approach achieved an excellent identification rate
compared with other previous works.

Ahmed, M. M., A. I. Hafez, M. M. Elwakil, A. E. Hassanien, and E. Hassanien, "A multi-objective genetic algorithm for community detection in multidimensional social network", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 129–139, 2016. Abstract
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Ahmed, S., T. Gaber, Alaa Tharwat, A. E. Hassanien, and V. Snáel, "Muzzle-based cattle identification using speed up robust feature approach", Intelligent Networking and Collaborative Systems (INCOS), 2015 International Conference on: IEEE, pp. 99–104, 2015. Abstract
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Alaa Tharwat, B. E. Elnaghi, and A. E. Hassanien, "Meta-Heuristic Algorithm Inspired by Grey Wolves for Solving Function Optimization Problems", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 480–490, 2016. Abstract
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Ali, A. F., A. E. Hassanien, and V. Snasel, "Memetic Artificial Bee Colony for integer programming ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Ali, A. F., and A. E. Hassanien, "Minimizing molecular potential energy function using genetic Nelder-Mead algorithm", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 177–183, 2013. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Ali, A. F., A. E. Hassanien, and V. Snasel, "Memetic Artificial Bee Colony for Integer Programming", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 268–277, 2014. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Ali, J. M., and A. E. Hassanien, "Mathematical Morphology Approach for Enhancement Digital Mammography Images", IASTED, International Conference on Biomedical Engineering (BioMED2004) February, pp. 16–18, 2004. Abstract
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Amira El sayed, A. E. Hassanien, S. E. - O. Hanafy, and M. Tolba, "Multi-layer hybrid machine learning techniques for anomalies detection and classification approach. ", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp. 216-221, 2013, Tunisia, , 4-6 Dec, 2013.
Asmaa Hashem Sweidan, N. El-Bendary, A. E. Hassanien, O. M. Hegazy, and A. E. -karim Mohamed, "Machine learning based approach for water pollution detection via fish liver microscopic images analysis", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 253–258, 2014. Abstract
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Aziz, A. S. A., A. E. Hassanien, S. E. - O. Hanaf, and M. F. Tolba, "Multi-layer hybrid machine learning techniques for anomalies detection and classification approach", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 215–220, 2013. Abstract
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Aziz, A. S. A., S. E. - O. Hanafi, and A. E. Hassanien, "Multi-agent artificial immune system for network intrusion detection and classification", International Joint Conference SOCO’14-CISIS’14-ICEUTE’14: Springer International Publishing, pp. 145–154, 2014. Abstract
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Aziz, A. S. A., S. E. - O. Hanafy, and A. E. Hassanien, "Multi-agent artificial immune system for network intrusion detection and classification", 9th International Conference on Soft Computing Models in Industrial and Environmental Applications, Bilbao, Spain, 25th - 27th Jun, 2014.
Aziz, A. S. A., and A. E. Hassanien, "Multilayer Machine Learning-Based Intrusion Detection System", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 225–247, 2014. Abstract
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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.

Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", IEEE 14th International on Multitopic Conference (INMIC), pp. 35-40, Packistan, , 22-24 Dec., 2011. Abstract

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.

Banerjee, S., H. Al-Qaheri, and A. E. Hassanien, "Mining social networks for viral marketing using fuzzy logic", Mathematical/Analytical Modelling and Computer Simulation (AMS), 2010 Fourth Asia International Conference on: IEEE, pp. 24–28, 2010. Abstract
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Banerjee, S., H. Al-Qaheri, and A. E. Hassanien, "Mining social networks for viral marketing using fuzzy logic", Mathematical/Analytical Modelling and Computer Simulation (AMS), 2010 Fourth Asia International Conference on: IEEE, pp. 24–28, 2010. Abstract
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Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", 2011 IEEE 14th International Multitopic Conference (INMIC), PP. 35-40 , Karachi, Pakistan , 22-24 Dec. 2011. Abstract

This paper proposes an elementary pattern detection approach for viruses propagated through e-mail and address books using the non-uniform pheromone deposition mechanism of ant colony. The local temporary tabu memory has been used to learn the pattern and it can combine known information from past viruses with a type of prediction for future viruses. This is achieved through certain generated test signature of viruses associated with e-mail over landscape. A non-uniform and non-decreasing time function for pheromone deposition and evaporation ensures that subsequent ants who are close enough to a previously selected trial solution will follow the trajectory or test landscape. They are capable to examine gradually thicker deposition of pheromone over the trajectory. It is empirically shown that the proposed modified pheromone learning mechanism can be an alternative approach to detect virus pattern for e-mail messages.

Banerjee, S., N. El-Bendary, A. E. Hassanien, and T. - H. Kim, "A modified pheromone dominant ant colony algorithm for computer virus detection", Multitopic Conference (INMIC), 2011 IEEE 14th International: IEEE, pp. 35–40, 2011. Abstract
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