Publications

Export 21 results:
Sort by: [ Author  (Asc)] Title Type Year
A B C D E F G H I J K L M N O P Q R S [T] U V W X Y Z   [Show ALL]
A
Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Two biometric approaches for cattle identification based on features and classifiers fusion", International Journal of Image Mining, vol. 1, no. 4: Inderscience Publishers (IEL), pp. 342–365, 2015. Abstract
n/a
Alaa Tharwat, T. Gaber, M. M. Fouad, V. Snasel, and A. E. Hassanien, "Towards an automated zebrafish-based toxicity test model using machine learning", Procedia Computer Science, vol. 65: Elsevier, pp. 643–651, 2015. Abstract
n/a
Alaa Tharwat, A. M. Ghanem, and A. E. Hassanien, "Three different classifiers for facial age estimation based on k-nearest neighbor", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 55–60, 2013. Abstract
n/a
Alaa Tharwat, Ahmed M. Ghanem, and A. E. Hassanien, "Three different classifiers for facial age estimation based on K-nearest neighbor", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) - pp. 55 - 60 , 2013, Cairo, EGYPT -, December 29-30, , 2013.
Alaa Tharwataf, Tarek Gaberb, V. S. Mohamed Mostaf Fouadc, and Aboul Ella Hassaniene, "Towards an Automated Zebrafish-based Toxicity Test Model Using Machine Learning", International Conference on Communications, management, and Information technology (ICCMIT'2015) Volume 65, 2015, Pages 643–651, Check Republica, 2015. Abstract

Zebrafish animal is considered as one of the most suitable animals to test toxicity of compounds due many features such as transparency and a large number of embryos produced in each mating. The main problem of the zebrafish-based toxicity test is the manual inspection of thousands of animals images in different phases and this is not feasible enough for the analysis, i.e. it is slow and may be inaccurate process. To help addressing this problem, in this paper, an automated classification of alive (healthy) and coagulant (died because of toxic compounds) zebrafish embryos are proposed. The embryos’ images are used to extract some features using the Segmentation-based Fractal Texture Analysis (SFTA) technique. The Rotation Forest classifier is then used to match between testing and training features (i.e. to classify alive and coagulant embryos). The experiments have proved that choosing threshold value of SFTA technique and the size of the rotation forest classifier have a great impact on the classification accuracy. With accuracy around 99.98%, the experimental results have showed that the proposed model is a very promising step toward a fully automated toxicity test during drug discovery.

C
Cun Hang, Fei Hu, Aboul Ella Hassanieny, and K. Xiao, "Texture-based Rotation-Invariant Histograms of Oriented Gradients", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
E
El-Atta, A. A. H., and A. E. Hassanien, "Two-class support vector machine with new kernel function based on paths of features for predicting chemical activity", Information Sciences, vol. 403: Elsevier, pp. 42–54, 2017. Abstract
n/a
G
Gaber, T., G. Ismail, A. Anter, M. Soliman, M. Ali, N. Semary, A. E. Hassanien, and V. Snasel, "Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm", Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE: IEEE, pp. 4254–4257, 2015. Abstract
n/a
H
Hafez, A. I., A. E. Hassanien, and A. A. Fahmy, "Testing community detection algorithms: A closer look at datasets", Social Networking: Springer International Publishing, pp. 85–99, 2014. Abstract
n/a
Hang, C., F. Hu, A. E. Hassanien, and K. Xiao, "Texture-based rotation-invariant Histograms of Oriented Gradients", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 223–228, 2015. Abstract
n/a
HASSAN, A. H. M. E. D., and A. E. Hassanien, "Two-Class Support Vector Machine with New Kernel Function Based on Paths of Features for Predicting Chemical Activity", Information Sciences, 2017. AbstractWebsite

Information and computer science fields such as machine learning and graph theory are implemented in chemoinformatics to discover the properties of chemical compounds. This paper presents a new algorithm based on the two-class support vector machine (SVM) model, which has new kernel functions for paths of features, enabling the prediction of chemical compound activity. Initially, we extract all paths of features (star subgraphs) with certain lengths, and we encode them depending on their structure in the graphs. Then, we use these codes to construct two relationship matrices between those paths. These matrices contain common and different sub-paths between paths of stars. The number of sub-paths/paths for each compound is passed to the proposed kernel functions in the two-class SVM to predict the activity of chemical compounds. The relationship matrices created by the proposed algorithm help to reduce the number of features, which improves prediction accuracy. We apply the proposed algorithm with and without feature selection using two benchmark datasets, specifically, the monoamine oxidase (MAO) dataset and the AIDS antiviral screen database of active compound dataset, which have 68 and 2000 chemical compounds, respectively. We perform comparative experiments for the proposed kernel functions and many other two-class SVM prediction methods, and the results before feature selection show prediction accuracies of 94% and 99.5% for MAO and AIDS, respectively. After selection, the prediction accuracies are 96% and 99.5% for MAO and AIDS, respectively.

K
Karam, H., A. E. Hassanien, and M. Nakajima, "Three-Dimentional Image Information Media. Animation of Linear Fractal Shapes using Polar Decomposition Interpolation.", 映像情報メディア学会誌, vol. 53, no. 3: The Institute of Image Information and Television Engineers, pp. 411–416, 1999. Abstract
n/a
Kilany, M., A. Adl, A. E. Hassanien, and T. - H. Kim, "Towards a Computational Human Behavioral Model", Computer, Information and Application (CIA), 2015 3rd International Conference on: IEEE, pp. 42–45, 2015. Abstract
n/a
M
Mokhtar, U., A. E. Hassanien, and M. A. H. A. S. Hefny, "Tomato leaves diseases detection approach based on support vector machines", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.
S
Sahlol, A. T., A. A. Ewees, A. M. Hemdan, and A. E. Hassanien, "Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 35–40, 2016. Abstract
n/a
Sahlol, A. T., A. A. Ewees, A. M. H.;, and A. E. Hassanien, "Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite", 12th International Computer Engineering Conference (ICENCO),, Cairo, 28-29 Dec, 2016. Abstract

Analytical prediction of oxidative stress biomarkers in ecosystem provides an expressive result for many stressors. These oxidative stress biomarkers including superoxide dismutase, glutathione peroxidase and catalase activity in fish liver tissue were analyzed within feeding different levels of selenium nanoparticles. Se-nanoparticles represent a salient defense mechanism in oxidative stress within certain limits; however, stress can be engendered from toxic levels of these nanoparticles. For instance, prediction of the level of pollution and/or stressors was elucidated to be improved with different levels of selenium nanoparticles using the bio-inspired Sine-Cosine algorithm (SCA). In this paper, we improved the prediction accuracy of liver enzymes of fish fed by nano-selenite by developing a neural network model based on SCA, that can train and update the weights and the biases of the network until reaching the optimum value. The performance of the proposed model is better and achieved more efficient than other models.

Salama, M. A., N. El-Bendary, and A. E. Hassanien, "Towards secure mobile agent based e-cash system", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 1–6, 2011. Abstract
n/a
Salama, M. A., N. El-Bendary, and A. E. Hassanien, "Towards secure mobile agent based e-cash system", Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies: ACM, pp. 1–6, 2011. Abstract
n/a
Sara Ahmed, T. Gaber, and A. E. Hassanien, "Telemetry Data Mining Techniques, Applications, and Challenges", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

The most recent rise of telemetry is around the use of Radio-telemetry technology for tracking the traces of moving objects. Initially, the radio telemetry was first used in the 1960s for studying the behavior and ecology of wild animals. Nowadays, there's a wide spectrum application of can benefits from radio telemetry technology with tracking methods, such as path discovery, location prediction, movement behavior analysis, and so on. Accordingly, rapid advance of telemetry tracking system boosts the generation of large-scale trajectory data of tracking traces of moving objects. In this study, we survey various applications of trajectory data mining and review an extensive collection of existing trajectory data mining techniques to be used as a guideline for designing future trajectory data mining solutions.

Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
n/a
Skowron, J. P. A. F., V. M. E. W. Orłowska, and R. S. W. Ziarko, Transactions on Rough Sets VII, , 2007. Abstract
n/a