Publications

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Journal Article
Elbedwehy, M. N., M. E. Ghoneim, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Application (Springer), vol. In press, 2014.
Elbedwehy, M. N., M. E. Ghoneim, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Application , vol. June 2014, 2014. AbstractWebsite

The accumulating data are easy to store but the ability of understanding and using it does not keep track with its growth. So researches focus on the nature of knowledge processing in the mind. This paper proposes a semantic model (CKRMCC) based on cognitive aspects that enables cognitive computer to process the knowledge as the human mind and find a suitable representation of that knowledge. In cognitive computer, knowledge processing passes through three major stages: knowledge acquisition and encoding, knowledge representation, and knowledge inference and validation. The core of CKRMCC is knowledge representation, which in turn proceeds through four phases: prototype formation phase, discrimination phase, generalization phase, and algorithm development phase. Each of those phases is mathematically formulated using the notions of real-time process algebra. The performance efficiency of CKRMCC is evaluated using some datasets from the well-known UCI repository of machine learning datasets. The acquired datasets are divided into training and testing data that are encoded using concept matrix. Consequently, in the knowledge representation stage, a set of symbolic rule is derived to establish a suitable representation for the training datasets. This representation will be available in a usable form when it is needed in the future. The inference stage uses the rule set to obtain the classes of the encoded testing datasets. Finally, knowledge validation phase is validating and verifying the results of applying the rule set on testing datasets. The performances are compared with classification and regression tree and support vector machine and prove that CKRMCC has an efficient performance in representing the knowledge using symbolic rules.

Elbedwehy, M. N., M. E. Ghoneim, A. E. Hassanien, and A. T. Azar, "A computational knowledge representation model for cognitive computers", Neural Computing and Applications, vol. 25, no. 7-8: Springer London, pp. 1517–1534, 2014. Abstract
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Hassanien, A. - E., Computational Social Networks Analysis, : Computer Communications and Networks Series-Springer, 2010. Abstract
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Ashour, A. S., S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, and A. E. Hassanien, "Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach", Journal of Signal and Information Processing, vol. 6, pp. 244-257, 2015. Abstractjsip_2015083113193757_1.pdfWebsite

Medical image enhancement is an essential process for superior disease diagnosis as well as for
detection of pathological lesion accurately. Computed Tomography (CT) is considered a vital medical
imaging modality to evaluate numerous diseases such as tumors and vascular lesions. However,
speckle noise corrupts the CT images and makes the clinical data analysis ambiguous.
Therefore, for accurate diagnosis, medical image enhancement is a must for noise removal and
sharp/clear images. In this work, a medical image enhancement algorithm has been proposed using
log transform in an optimization framework. In order to achieve optimization, a well-known
meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to determine the optimal
parameter settings for log transform. The performance of the proposed technique is studied on a
low contrast CT image dataset. Besides this, the results clearly show that the CS based approach
has superior convergence and fitness values compared to PSO as the CS converge faster that
proves the efficacy of the CS based technique. Finally, Image Quality Analysis (IQA) justifies the robustness >
of the proposed enhancement technique.

Ashour, A. S., S. Samanta, N. Dey, N. Kausar, W. B. Abdessalemkaraa, A. E. Hassanien, and others, "Computed tomography image enhancement using cuckoo search: a log transform based approach", Journal of Signal and Information Processing, vol. 6, no. 03: Scientific Research Publishing, pp. 244, 2015. Abstract
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Hassanien, A. E., "A Copyright Protection using Watermarking Algorithm", Informatica, vol. 17 , issue 2, pp. 187-198, 2006. AbstractWebsite

In this paper, a digital watermarking algorithm for copyright protection based on the concept of embed digital watermark and modifying frequency coefficients in discrete wavelet transform (DWT) domain is presented. We embed the watermark into the detail wavelet coefficients of the original image with the use of a key. This key is randomly generated and is used to select the exact locations in the wavelet domain in which to embed the watermark. The corresponding watermark detection algorithm is presented. A new metric that measure the objective quality of the image based on the detected watermark bit is introduced, which the original unmarked image is not required for watermark extraction. The performance of the proposed watermarking algorithm is robust to variety of signal distortions, such a JPEG, image cropping, geometric transformations and noises.

Hassanien, A. E., "A copyright protection using watermarking algorithm", Informatica, vol. 17, no. 2: Institute of Mathematics and Informatics, pp. 187–198, 2006. Abstract
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Mostafa, A., A. Fouad, M. A. Fattah, A. E. Hassanien, H. Hefny, S. Y. Zhu, and G. Schaefer, "CT liver segmentation using artificial bee colony optimisation", Procedia Computer Science, vol. 60: Elsevier, pp. 1622–1630, 2015. Abstract
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El-Bendary, N., Esraa Elhariri, M. Hazman, S. M. Saleh, and A. E. Hassanien, "Cultivation-time Recommender System Based on Climatic Conditions for Newly Reclaimed Lands in Egypt", Procedia Computer Science, vol. Volume 96, , pp. Pages 110-119, 2016. AbstractWebsite

This research proposes cultivation-time recommender system for predicting the best sowing dates for winter cereal crops in the newly reclaimed lands in Farafra Oasis, The Egyptian Western Desert. The main goal of the proposed system is to support the best utilization of farm resources. In this research, predicting the best sowing dates for the aimed crops is based on weather conditions prediction along with calculating the seasonal accumulative growing degree days (GDD) fulfillment duration for each crop. Various Machine Learning (ML) regression algorithms have been used for predicting the daily minimum and maximum air temperature based on historical weather conditions data for twenty-five growing seasons (1990/91 to 2014/15). Experimental results showed that using the M5P and IBk ML regression algorithms have outperformed the other implemented regression algorithms for predicting the daily minimum and maximum air temperature based on historical weather conditions data. That has been measured based on the calculated mean absolute error (MAE). Also, obtained experimental results obviously indicated that the best cultivation-time prediction by the proposed recommender system has been achieved by the M5P algorithm, based on the seasonal accumulative GDD fulfillment duration, for the coming five growing seasons (2016/17 to 2019/20).

El-Bendary, N., Esraa Elhariri, M. Hazman, S. M. Saleh, and A. E. Hassanien, "Cultivation-time recommender system based on climatic conditions for newly reclaimed lands in Egypt", Procedia Computer Science, vol. 96: Elsevier, pp. 110–119, 2016. Abstract
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Conference Paper
Hamdy, A., N. El-Bendary, A. Khodeir, M. M. M. Fouad, A. E. Hassanien, and H. Hefny, "Cardiac disorders detection approach based on local transfer function classifier", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 55–61, 2013. Abstract
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Ayeldeen, H., O. Hegazy, and A. E. Hassanien, "Case selection strategy based on K-means clustering,", The Second International Conference on INformation systems Design and Intelligent Applications ((INDIA 15), Kalyani, India, January 8-9 , 2015.
Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Case-based reasoning: A knowledge extraction tool to use", The Second International Conference on INformation systems Design and Intelligent Applications ((INDIA 15), Kalyani, India, January 8-9 , 2015.
Ayeldeen, H., M. A. Fattah, O. Shaker, A. E. Hassanien, and T. - H. Kim, "Case-Based Retrieval Approach of Clinical Breast Cancer Patients", Computer, Information and Application (CIA), 2015 3rd International Conference on: IEEE, pp. 38–41, 2015. Abstract
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Mahmoud, H. A., H. M. El Hadad, F. A. Mousa, and A. E. Hassanien, "Cattle classifications system using Fuzzy K-Nearest Neighbor Classifier", Informatics, Electronics & Vision (ICIEV), 2015 International Conference on: IEEE, pp. 1–5, 2015. Abstract
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Alaa Tharwat, T. Gaber, A. E. Hassanien, H. A. Hassanien, and M. F. Tolba, "Cattle Identi cation using Muzzle Print Images based on Texture Features Approach", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. Abstractibica2014_p26.pdf

The increasing growth of the world trade and growing con-
cerns of food safety by consumers need a cutting-edge animal identi-
cation and traceability systems as the simple recording and reading
of tags-based systems are only eective in eradication programs of na-
tional disease. Animal biometric-based solutions, e.g. muzzle imaging
system, oer an eective and secure, and rapid method of addressing
the requirements of animal identication and traceability systems. In
this paper, we propose a robust and fast cattle identication approach.
This approach makes use of Local Binary Pattern (LBP) to extract local
invariant features from muzzle print images. We also applied dierent
classiers including Nearest Neighbor, Naive Bayes, SVM and KNN for
cattle identication. The experimental results showed that our approach
is superior than existed works as ours achieves 99,5% identication accu-
racy. In addition, the results proved that our proposed method achieved
this high accuracy even if the testing images are rotated in various angels
or occluded with dierent parts of their sizes.

Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Cattle Identification based on Muzzle Images using Gabor Features and SVM Classifier ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Alaa Tharwat, T. Gaber, and A. E. Hassanien, "Cattle identification based on muzzle images using gabor features and SVM classifier", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 236–247, 2014. Abstract
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Awad, A. I., H. zawbaa, and A. E. Hassanien, "A Cattle Identification of Approach Using Live Captured Muzzle Print Images", International conference on Advances in Security of Information and Communication Networks, (SecNet 2013) , Springer , Egypt, 3-5 Sept, , 2013. a_cattle_identification.pdf
Alaa Tharwat, T. Gaber, A. E. Hassanien, H. A. Hassanien, and M. F. Tolba, "Cattle identification using muzzle print images based on texture features approach", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 217–227, 2014. Abstract
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Elshazly, H. I., M. Waly, A. M. Elkorany, and A. E. Hassanien, "Chronic eye disease diagnosis using ensemble-based classifier", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
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Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Classification Approach based on Rough Mereology", In Proceedings of the Second International Symposium on Intelligent Informatics (ISI'13), , Mysore, India, 23-24 August, 20, 2013. isi2013-india-classification_approach_based_on_rough_mereology.pdf
Schaefer, G., Niraj P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Schaefer, G., N. P. Doshi, Qinghua Hu, and A. E. Hassanien, "Classification of HEp-2 Cell Images Using Compact Multi-Scale Texture Information and Margin Distribution Based Bagging", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 299–308, 2014. Abstract
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