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Hafez, A. I., H. M. Zawbaa, E. Emary, and A. E. Hassanien, "Sine cosine optimization algorithm for feature selection", INnovations in Intelligent SysTems and Applications (INISTA), 2016 International Symposium on: IEEE, pp. 1–5, 2016. Abstract
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Dey, A., S. Dey, Siddhartha Bhattacharyya, V. Snasel, and A. E. Hassanien, "Simulated Annealing Based Quantum Inspired Automatic Clustering Technique", AMLTA 2018: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) , Cairo, 23 fEB, 2018. Abstract

Cluster analysis is a popular technique whose aim is to segregate a set of data points into groups, called clusters. Simulated Annealing (SA) is a popular meta-heuristic inspired by the annealing process used in metallurgy, useful in solving complex optimization problems. In this paper, the use of the Quantum Computing (QC) and SA is explored to design Quantum Inspired Simulated Annealing technique, which can be applied to compute optimum number of clusters for image clustering. Experimental results over a number of images endorse the effectiveness of the proposed technique pertaining to fitness value, convergence time, accuracy, robustness, and standard error. The paper also reports the computation results of a statistical superiority test, known as t-test. An experimental judgement to the classical technique has also be presented, which eventually demonstrates that the proposed technique outperforms the other.

Ali, A. F., and A. - E. Hassanien, "A Simplex Nelder Mead Genetic Algorithm for Minimizing Molecular Potential Energy Function", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 1–21, 2016. Abstract
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Ali, M. A. S., M. I. Shaalan, A. E. Hassanien, and T. - H. Kim, "A Simple Approach for Segmentation and Removal of Interphase Cells from Chromosome Images", Computer and Computing Science (COMCOMS), 2015 3rd International Conference on: IEEE, pp. 3–8, 2015. Abstract
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Ghany, K. K. A., A. E. Hassanien, and G. Schaefer, "Similarity measures for fingerprint matching", Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV): The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), pp. 1, 2014. Abstract
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Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "Similarity Measures based Recommender System for Rehabilitation of People with Disabilities", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015. Abstract

This paper proposes a recommender system to predict and suggest a
set of rehabilitation methods for patients with spinal cord injuries (SCI). The proposed
system automates, stores and monitors the heath conditions of SCI patients.
The International Classification of Functioning, Disability and Health classification
(ICF) is used to stores and monitors the progress in health status. A set of
similarity measures are utilized in order to get the similarity between patients and
predict the rehabilitation recommendations. Experimental results showed that the
proposed recommender system has obtained an accuracy of 98% via implementing
the cosine similarity measure.

Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "Similarity Measures Based Recommender System for Rehabilitation of People with Disabilities", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 523–533, 2016. Abstract
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Alaa Tharwat, T. Gaber, M. K. Shahin, B. Refaat, and A. E. H. Ali, "SIFT-based Arabic Sign Language Recognition System", The 1st Afro-European Conference for Industrial Advancement, , Addis Ababa, Ethiopia, November 17-19, , 2014.
Alaa Tharwat, T. Gaber, A. E. Hassanien, M. K. Shahin, and B. Refaat, "Sift-based arabic sign language recognition system", Afro-european conference for industrial advancement: Springer International Publishing, pp. 359–370, 2015. Abstract
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Hassanien, A. E., H. Takahashi, and M. Nakajima, "Semiautomatic feature specification based on snakes for image morphing", Optical Science, Engineering and Instrumentation'97: International Society for Optics and Photonics, pp. 494–502, 1997. Abstract
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Zawbaa, H. M., N. El-Bendary, A. E. Hassanien, and V. Snasel, "Semi-automatic annotation system for home videos", Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on: IEEE, pp. 1275–1280, 2010. Abstract
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Kotyk, T., S. Chakraborty, N. Dey, T. Gaber, A. E. Hassanien, and V. Snasel, "Semi-automated System for Cup to Disc Measurement for Diagnosing Glaucoma Using Classification Paradigm", Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015: Springer International Publishing, pp. 653–663, 2016. Abstract
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Darwish, A., A. E. Hassanien, Q. Tan, and N. R. Pal, "Securing patients medical images and authentication system based on public key infrastructure", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 27–34, 2011. Abstract
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Darwish, A., A. E. Hassanien, Q. Tan, and N. R. Pal, "Securing patients medical images and authentication system based on public key infrastructure", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 27–34, 2011. Abstract
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Elhoseny, M., A. Farouk, A. Shehab, and A. E. Hassanien, "Secure Image Processing and Transmission Schema in Cluster-Based Wireless Sensor Network", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

WSN as a new category of computer-based computing platforms and network structures is showing new applications in different areas such as environmental monitoring, health care and military applications. Although there are a lot of secure image processing schemas designed for image transmission over a network, the limited resources and the dynamic environment make it invisible to be used with Wireless Sensor Networks (WSNs). In addition, the current secure data transmission schemas in WSN are concentrated on the text data and are not applicable for image transmission's applications. Furthermore, secure image transmission is a big challenging issue in WSNs especially for the application that uses image as its main data such as military applications. The reason why is because the limited resources of the sensor nodes which are usually deployed in unattended environments. This chapter introduces a secure image processing and transmission schema in WSN using Elliptic Curve Cryptography (ECC) and Homomorphic Encryption (HE).

El-Bendary, N., O. S. Soliman, N. I. Ghali, A. E. Hassanien, V. Palade, and H. Liu, "A secure directed diffusion routing protocol for wireless sensor networks", Next Generation Information Technology (ICNIT), 2011 The 2nd International Conference on: IEEE, pp. 149–152, 2011. Abstract
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El-Bendary, N., O. S. Soliman, N. I. Ghali, A. E. Hassanien, V. Palade, and H. Liu, "A secure directed diffusion routing protocol for wireless sensor networks", Next Generation Information Technology (ICNIT), 2011 The 2nd International Conference on: IEEE, pp. 149–152, 2011. Abstract
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Abraham, A., K. Wegrzyn-Wolska, A. E. Hassanien, V. Snasel, and A. M. Alimi, Second International Afro-European Conference for Industrial Advancement AECIA 2015, , 2015. Abstract

This volume contains accepted papers presented at AECIA2014, the First International Afro-European Conference for Industrial Advancement. The aim of AECIA was to bring together the foremost experts as well as excellent young researchers from Africa, Europe, and the rest of the world to disseminate latest results from various fields of engineering, information, and communication technologies. The first edition of AECIA was organized jointly by Addis Ababa Institute of Technology, Addis Ababa University, and VSB - Technical University of Ostrava, Czech Republic and took place in Ethiopia's capital, Addis Ababa.

Liu, H., A. Abraham, and A. E. Hassanien, "Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm", Future Generation Computer Systems, vol. 26, no. 8: Elsevier, pp. 1336–1343, 2010. Abstract
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Mohamed Tahoun, Abd El Rahman Shabayek, H. Nassar, M. M. Giovenco, R. Reulke, Eid Emary, and A. E. Hassanien, "Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features", Image Feature Detectors and Descriptors: Springer International Publishing, pp. 135–171, 2016. Abstract
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Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Rule Generation Approach for Granular Computing Using Rough Mereology", International Conference on Computer Research and Development, 5th (ICCRD 2013): ASME Press, 2013. Abstract
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Own, H. S., and A. E. Hassanien, "Rough Wavelet Hybrid Image Classification Scheme", Journal of Convergence Information Technology, vol. 3, issue 4, pp. 65-75, 2008. AbstractWebsite

This paper introduces a new computer-aided classification system for detection of prostate cancer in
Transrectal Ultrasound images (TRUS). To increase the efficiency of the computer aided classification
process, an intensity adjustment process is applied first, based on the Pulse Coupled Neural Network
(PCNN) with a median filter. This is followed by applying a PCNN-based segmentation algorithm to
detect the boundary of the prostate image. Combining the adjustment and segmentation enable to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. Then, wavelet based features have been extracted and
normalized, followed by application of a rough set analysis to discover the dependency between the
attributes and to generate a set of reduct that contains a minimal number of attributes. Finally, a rough
confusion matrix is designed that contain information about actual and predicted classifications done by a
classification system. Experimental results show that the introduced system is very successful and has high detection accuracy

Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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Own, H. S., and A. E. Hassanien, "Rough wavelet hybrid image classification scheme", Journal of Convergence Information Technology, vol. 3, no. 4, pp. 65–75, 2008. Abstract
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