Publications

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2018
Alaa Tharwat, M. Elhoseny, A. E. Hassanien, and T. G. A. and Kumar, "Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm", Cluster Computing, 2018. Abstract

Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bézier curve-based model for path planning. The control points of the Bézier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bézier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.

2017
Hassanien, A. E., T. Gaber, U. Mokhtar, and H. Hefny, "An Improved Moth Flame Optimization Algorithm based on Rough Sets for Tomato Diseases Detection", Journal of Computers and Electronics in Agriculture, vol. 136, issue 15, pp. 86-96 , 2017. AbstractWebsite

Plant diseases is one of the major bottlenecks in agricultural production that have bad effects on the economic of any country. Automatic detection of such disease could minimize these effects. Features selection is a usual pre-processing step used for automatic disease detection systems. It is an important process for detecting and eliminating noisy, irrelevant, and redundant data. Thus, it could lead to improve the detection performance. In this paper, an improved moth-flame approach to automatically detect tomato diseases was proposed. The moth-flame fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. The proposed algorithm used both of the power of exploration of the moth flame and the high performance of rough sets for the feature selection task to find the set of features maximizing the classification accuracy which was evaluated using the support vector machine (SVM). The performance of the MFORSFS algorithm was evaluated using many benchmark datasets taken from UCI machine learning data repository and then compared with feature selection approaches based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with rough sets. The proposed algorithm was then used in a real-life problem, detecting tomato diseases (Powdery mildew and early blight) where a real dataset of tomato disease were manually built and a tomato disease detection approach was proposed and evaluated using this dataset. The experimental results showed that the proposed algorithm was efficient in terms of Recall, Precision, Accuracy and F-Score, as long as feature size reduction and execution time.

abd elaziz, M., and A. E. Hassanien, "An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem,", Neural Computing and Applications, 2017 , 2017. AbstractWebsite

The minimum number attribute reduction problem is an important issue when dealing with huge amounts of data. The problem of minimum attribute reduction is formally known to be as an NP complete nonlinearly constrained optimization problem. Social spider optimization algorithm is a new meta-heuristic algorithm of the swarm intelligence field to global solution. The social spider optimization algorithm is emulates the behavior of cooperation between spiders based on the biological laws of the cooperative colony. Inspired by the social spiders, in this paper, an improved social spider algorithm for the minimal reduction problem was proposed. In the proposed algorithm, the fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. For each spider, the fitness function is computed and compared with the global best fitness value. If the current value is better, then the global best fitness is replaced with it and its position became the reduct set. Then, the position of each spider is updated according to its type. This process is repeated until the stopping criterion is satisfied. To validate the proposed algorithm, several real clinical medical datasets which are available from the UCI Machine Learning Repository were used to compute the performance of the proposed algorithm. The experimental results illustrate that the proposed algorithm is superior to state-of-the-art swarm-based in terms of classification accuracy while limiting number of features.

Hassanien, A. E., T. Gaber, U. Mokhtar, and H. Hefny, "An improved moth flame optimization algorithm based on rough sets for tomato diseases detection", Computers and Electronics in Agriculture, vol. 136: Elsevier, pp. 86–96, 2017. Abstract
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El-Said, S. A., H. M. A. Atta, and A. E. Hassanien, "Interactive soft tissue modelling for virtual reality surgery simulation and planning", International Journal of Computer Aided Engineering and Technology, vol. 9, no. 1: Inderscience Publishers (IEL), pp. 38–61, 2017. Abstract
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2016
El-Baz, A. H., A. E. Hassanien, and G. Schaefer, "Identification of Diabetes Disease Using Committees of Neural Network-Based Classifiers", Machine Intelligence and Big Data in Industry: Springer International Publishing, pp. 65–74, 2016. Abstract
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Houssein, E. H., M. A. S. Ali, and A. E. Hassanien, "An image steganography algorithm using Haar Discrete Wavelet Transform with Advanced Encryption System", Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on: IEEE, pp. 641–644, 2016. Abstract
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Waleed Yamany, Eid Emary, A. E. Hassanien, G. Schaefer, and S. Y. Zhu, "An Innovative Approach for Attribute Reduction Using Rough Sets and Flower Pollination Optimisation", Procedia Computer Science, vol. 96: Elsevier, pp. 403–409, 2016. Abstract
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2015
Sara Yassen, T. Gaber, and A. E. Hassanien, "Integer Wavelet Transform for Thermal Image Authentication", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , November 13 - 15, 2015. Abstract

Thermal imaging is a technology with property of
seeing objects in the darkness. Such property makes this technology
very important tool for security and surveillance applications.
In this paper, a thermal image authentication technique using
hash function is proposed. In this technique, the thermal images
are used as cover images and bits from secret data (i.e. messages
or images) are then hidden in the cover images. This is achieved
by using the hash function and IntegerWavelet Transform (IWT).
1, 2 and 3 bits per bytes have been hidden in both horizontal
and vertical components of wavelet transform. The proposed
technique has been evaluated based on mean square error (MSE),
peak signal to noise ratio (PSNR), image fidelity (IF) and standard
deviation (SD). The results have shown better performance of the
proposed technique comparing with the most related work.

Sayed, G. I., and A. E. Hassanien, "Interphase cells removal from metaphase chromosome images based on meta-heuristic grey wolf optimizer", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
Alaa Tharwat, Mahir M. Sharif, A. E. Hassanien, and H. A. Hefny, "Improving Enzyme Function Classification Performance Based on Score Fusion Method.", 10th International Conference Hybrid Artificial Intelligent System, Bilbao, Spain, 23 June, 2015.
Hafez, A. I., Hossam M. Zawbaa, E. Emary, and A. E. H. Hamdi A. Mahmoud, "An innovative approach for feature selection based on chicken swarm optimization", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan,, November 13 - 1, 2015. Abstract

In this paper, a system for feature selection based
on chicken swarm optimization (CSO) algorithm is proposed.
Datasets ordinarily includes a huge number of attributes, with
irrelevant and redundant attribute. Commonly wrapper-based
approaches are used for feature selection but it always requires
an intelligent search technique as part of the evaluation function.
Chicken swarm optimization (CSO)is a new bio-inspired
algorithm mimicking the hierarchal order of the chicken
swarm and the behaviors of chicken swarm, including roosters,
hens and chicks, CSO can efficiently extract the chickens’
swarm intelligence to optimize problems. Therefore, CSO was
employed to feature selection in wrapper mode to search
the feature space for optimal feature combination maximizing
classification performance, while minimizing the number of
selected features. The proposed system was benchmarked
on 18 datasets drawn from the UCI repository and using
different evaluation criteria and proves advance over particle
swarm optimization (PSO) and genetic algorithms (GA) that
commonly used in optimization problems

Moustafa Zeina, A. A. Fatma Yakouba, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", International Conference on Communications, management, and Information technology (ICCMIT'2015) Volume 65, 2015, Pages 582–591, Ostrava, Czech Republic, 2015. Abstract

Geotagged photos carry hidden data about the surrounding area, and the owner of the photo. Moreover; Geotagged photos have background information about the user, where the alternative resources of Geo-spatial data lack background information. In this study, we propose identification for the circles of relations of the smartphone user from Geotagged photos. The proposed solution mainly depends on a framework, which is based on smartphone photo gallery. The framework extracts a degree of relation between smartphone user and circles of relations entities. Circles of relations incorporate closest people, places, where the participant visits, and interests. The circles of relations are represented in a social graph, which shows the clusters of social relations and interests of smartphone user. The social graph clarifies the nature and the degree of the relations for the participants. The results of framework introduced the relation between the level of variety of participant social relations, and the degree of relations.

Lamiaa M. El Bakrawy, N. I.Ghali, and A. E. Hassanien, "Intelligent Machine Learning in Image Authentication.", signal processing system, vol. 78, issue 2, pp. 223-237 , 2015. Website
Moustafa Zein, F. Yakoub, A. Adl, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", Procedia Computer Science, vol. 65: Elsevier, pp. 582–591, 2015. Abstract
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Mokhtar, U., M. A. S. Ali, A. E. Hassanien, and H. Hefny, "Identifying two of tomatoes leaf viruses using support vector machine", Information Systems Design and Intelligent Applications: Springer India, pp. 771–782, 2015. Abstract
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Alaa Tharwat, M. M. Sharif, A. E. Hassanien, and H. A. Hefeny, "Improving Enzyme Function Classification Performance Based on Score Fusion Method", International Conference on Hybrid Artificial Intelligence Systems: Springer International Publishing, pp. 530–542, 2015. Abstract
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Hafez, A. I., H. M. Zawbaa, E. Emary, H. A. Mahmoud, and A. E. Hassanien, "An innovative approach for feature selection based on chicken swarm optimization", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 19–24, 2015. Abstract
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Yassen, S., T. Gaber, and A. E. Hassanien, "Integer wavelet transform for thermal image authentication", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 13–18, 2015. Abstract
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El Bakrawy, L. M., N. I. Ghali, and A. E. Hassanien, "Intelligent Machine Learning in Image Authentication", Journal of Signal Processing Systems, vol. 78, no. 2: Springer US, pp. 223–237, 2015. Abstract
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Sayed, G. I., and A. E. Hassanien, "Interphase cells removal from metaphase chromosome images based on meta-heuristic Grey Wolf Optimizer", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 261–266, 2015. Abstract
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2014
and ella S. Udhaya Kumar, H. Hannah Inbarani, A. T. A. A. H., "Identification of Heart Valve Disease using Bijective, Soft sets Theory ", International Journal of Rough Sets and Data Analysis, vol. 1, issue 2, pp. , 1(2), 1-13, 2014. Abstract

Major complication of heart valve diseases is congestive heart valve failure. The heart is of essential significance to human beings. Auscultation with a stethoscope is considered as one of the techniques used in the analysis of heart diseases. Heart auscultation is a difficult task to determine the heart condition and requires some superior training of medical doctors. Therefore, the use of computerized techniques in the diagnosis of heart sounds may help the doctors in a clinical environment. Hence, in this study computer-aided heart sound diagnosis is performed to give support to doctors in decision making. In this study, a novel hybrid Rough-Bijective soft set is developed for the classification of heart valve diseases. A rough set (Quick Reduct) based feature selection technique is applied before classification for increasing the classification accuracy. The experimental results demonstrate that the overall classification accuracy offered by the employed Improved Bijective soft set approach (IBISOCLASS) provides higher accuracy compared with other classification techniques including hybrid Rough-Bijective soft set (RBISOCLASS), Bijective soft set (BISOCLASS), Decision table (DT), Naïve Bayes (NB) and J48.

Kareem Kamal A.Ghany, and A. E. Hassanien, An Intelligent Hybrid Biometrics System, , Cairo, EGYPT , Cairo University , 2014. thesis_presentation.pdf
Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "ICF based automation system for spinal cord injuries rehabilitation", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 192–197, 2014. Abstract
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Kumar, U. S., H. H. Inbarani, A. T. Azar, and A. E. Hassanien, "Identification of heart valve disease using bijective soft sets theory", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 1, no. 2: IGI Global, pp. 1–14, 2014. Abstract
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Tourism