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M.Moftah, H., A. E. Hassanien, N. Ghali, and M. Shoman, Multi-objective optimization K-mean segmentation approach for MRI Breast Images, , 2012. Abstract

The objective of this paper is to evaluate a new approach intended for reliable MRI breast image segmentation. It is based on the concepts of multi-objective and adaptation to identify target objects through an optimization methodology which keeps the optimum result during its iterations. The proposed approach were used to improve and enhance the traditional k-means clustering algorithm to be more effective and efficient. The clustering and breast cancer segmentation are implemented in the proposed approach at the same time by using the concept of multiobjective, and adaptation continually, in each iteration and then maintaining the best results. To evaluate performance of the presented approach, we run tests over different MRI breast images. The experimental results show that the overall accuracy offered by the multiobjective proposed k-means is high compared with standard K-mean clustering technique.

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.

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.

abd elaziz, M., A. A. Ewees, and A. E. Hassanien, "Multi-objective whale optimization algorithm for content-based image retrieval", Download PDF Multimedia Tools and Applications, 2018. AbstractWebsite

In the recent years, there are massive digital images collections in many fields of our life, which led the technology to find methods to search and retrieve these images efficiently. The content-based is one of the popular methods used to retrieve images, which depends on the color, texture and shape descriptors to extract features from images. However, the performance of the content-based image retrieval methods depends on the size of features that are extracted from images and the classification accuracy. Therefore, this problem is considered as a multi-objective and there are several methods that used to manipulate it such as NSGA-II and NSMOPSO. However, these methods have drawbacks such as their time and space complexity are large since they used traditional non-dominated sorting methods. In this paper, a new non-dominated sorting based on multi-objective whale optimization algorithm is proposed for content-based image retrieval (NSMOWOA). The proposed method avoids the drawbacks in other non-dominated sorting multi-objective methods that have been used for content-based image retrieval through reducing the space and time complexity. The results of the NSMOWOA showed a good performance in content-based image retrieval problem in terms of recall and precision.

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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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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Hassanien, A. E., M. M. Fouad, A. A. Manaf, M. Zamani, R. Ahmad, and J. Kacprzyk, Multimedia Forensics and Security: Foundations, Innovations, and Applications, , Germany , Springer, 2017. AbstractWebsite

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El-Sehiemy, R. A., Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, "Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem", Studies in Informatics and Control, vol. 22, no. 2, pp. 113–122, 2013. Abstract
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El-Sehiemy, R. A., Mostafa Abdelkhalik El-hosseini, and A. E. Hassanien, "Multiobjective real-coded genetic algorithm for economic/environmental dispatch problem", Studies in Informatics and Control, vol. 22, no. 2, pp. 113–122, 2013. Abstract
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Issa, M., and A. E. Hassanien, "Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 409–423, 2017. Abstract
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Hassanien, A. E., I. El Henawy, and H. S. Own, "Multiresolution image denoising based on wavelet transform", International Symposium on Optical Science and Technology: International Society for Optics and Photonics, pp. 383–394, 2001. Abstract
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Hassanien, A. E., I. El Henawy, and H. S. Own, "Multiresolution image denoising based on wavelet transform", International Symposium on Optical Science and Technology: International Society for Optics and Photonics, pp. 383–394, 2001. Abstract
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Hassanien, A. E., I. El Henawy, and H. Own, "Multiresolution image denosing based on wavelet transform", Machine Graphics and Vision, vol. 10, no. 2, pp. 221–230, 2001. Abstract
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Own, H. S., and A. E. Hassanien, "Multiresolution image registration algorithm in wavelet transform domain", Digital Signal Processing, 2002. DSP 2002. 2002 14th International Conference on, vol. 2: IEEE, pp. 889–892, 2002. Abstract
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Mostafa A. Salama, M. M. M. Fouad, N. El-Bendary, and A. E. Hassanien, "Mutagenicity analysis based on Rough Set Theory and Formal Concept Analysis", In Proceedings of the Second International Symposium on Intelligent Informatics (ISI'13), , Mysore, India, 23-24 August, 2, 2013. mutagenicity_analysis_based_on_roug_set_FCA.pdf
Salama, M. A., M. M. M. Fouad, N. El-Bendary, and A. E. O. Hassanien, "Mutagenicity Analysis Based on Rough Set Theory and Formal Concept Analysis", Recent Advances in Intelligent Informatics: Springer International Publishing, pp. 265–273, 2014. 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, 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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Tourism