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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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Elbedwehy, M. N., M. E. Ghoneim, and A. E. Hassanien, "Computational model for artificial learning using fonnal concept analysis", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 9–14, 2013. Abstract
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Elbedwehy, M. N., M. E. Ghoneim, and A. E. Hassanien, "Computational model for artificial learning using fonnal concept analysis", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 9–14, 2013. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
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Abraham, A., A. - E. Hassanien, V. Sná, and others, Computational social network analysis: Trends, tools and research advances, : Springer Science & Business Media, 2009. Abstract
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Hassanien, A. - E., Computational Social Networks Analysis, , London, Computer Communications and Networks Series - Springer, 2010. AbstractWebsite

Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Web 2.0 has enabled a new generation of web-based communities, social networks, and folksonomies to facilitate collaboration among different communities. This unique text/reference compares and contrasts the ethological approach to social behavior in animals with web-based evidence of social interaction, perceptual learning, information granulation, the behavior of humans and affinities between web-based social networks. An international team of leading experts present the latest advances of various topics in intelligent-social-networks and illustrates how organizations can gain competitive advantages by applying the different emergent techniques in real-world scenarios. The work incorporates experience reports, survey articles, and intelligence techniques and theories with specific network technology problems.

Hassanien, A. - E., Computational Social Networks Analysis, : Computer Communications and Networks Series-Springer, 2010. Abstract
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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
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Abraham, A., and A. - E. Hassanien, Computational social networks: Tools, perspectives and applications, : Springer Science & Business Media, 2012. Abstract
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Abraham, A., and A. - E. Hassanien, Computational social networks: Tools, perspectives and applications, : Springer Science & Business Media, 2012. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. 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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Soliman, O. S., and A. E. Hassanien, "A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 106–115, 2012. Abstract
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Soliman, O. S., and A. E. Hassanien, "A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 106–115, 2012. Abstract
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Soliman, O. S., and A. E. Hassanien, "A Computer Aided Diagnosis System for Breast Cancer Using Support Vector Machine", International Conference on Rough Sets and Current Trends in Computing: Springer Berlin Heidelberg, pp. 106–115, 2012. Abstract
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Aziz, A. S. A., A. T. Azar, A. E. Hassanien, and S. E. - O. Hanafy, "Continuous features discretization for anomaly intrusion detectors generation", Soft computing in industrial applications: Springer International Publishing, pp. 209–221, 2014. Abstract
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Aziz, A. S. A., A. T. Azar, A. E. Hassanien, and S. E. - O. Hanafy, "Continuous features discretization for anomaly intrusion detectors generation", Soft computing in industrial applications: Springer International Publishing, pp. 209–221, 2014. Abstract
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Aziz, A. S. A., A. T. Azar, A. E. Hassanien, and S. E. - O. Hanafy, "Continuous features discretization for anomaly intrusion detectors generation", Soft computing in industrial applications: Springer International Publishing, pp. 209–221, 2014. Abstract
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Hassanien, A. E., O. S. Soliman, and N. El-Bendary, "Contrast enhancement of breast MRI images based on fuzzy type-II", Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011: Springer Berlin Heidelberg, pp. 77–83, 2011. Abstract
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Ibrahim, R. A., H. A. Hefny, and A. E. Hassanien, "Controlling Rumor Cascade over Social Networks", International Conference on Advanced Intelligent Systems and Informatics: Springer International Publishing, pp. 456–466, 2016. Abstract
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