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El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, "Cultural-Based Genetic Algorithm: Design and Real World Applications. ", Eighth International Conference on Intelligent Systems Design and Applications, ISDA 2008, Kaohsiung, Taiwan, pp.488-493 , 26-28 November, 2008. Abstract

Due to their excellent performance in solving combinatorial optimization problems, metaheuristics algorithms such as Genetic Algorithms GA [35], [18], [5], Simulated Annealing SA [34], [13] and Tabu Search TS make up another class of search methods that has been adopted to efficiently solve dynamic optimization problem. Most of these methods are confined to the population space and in addition the solutions of nonlinear problems become quite difficult especially when they are heavily constrained. They do not make full use of the historical information and lack prediction about the search space. Besides the knowledge that individuals inherited "genetic code" from their ancestors, there is another component called Culture. In this paper, a novel culture-based GA algorithm is proposed and is tested against multidimensional and highly nonlinear real world applications.

El-Hosseini, M. A., A. E. Hassanien, A. Abraham, and H. Al-Qaheri, "Cultural-Based Genetic Algorithm: Design and Real World Applications", Intelligent Systems Design and Applications, 2008. ISDA'08. Eighth International Conference on, vol. 3: IEEE, pp. 488–493, 2008. 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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Mostafa, A., M. A. Fattah, A. E. Hassanien, H. Hefny, and G. S. Shao Ying Zhu, "CT Liver Segmentation Using Artificial Bee Colony Optimisation", 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, Procedia Computer Science , Singapore, September, 2015. Abstract

The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.

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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Hamdy, E., A. Adl, A. E. Hassanien, O. Hegazy, and T. - H. Kim, "Criminal Act Detection and Identification Model", Advanced Communication and Networking (ACN), 2015 Seventh International Conference on: IEEE, pp. 79–83, 2015. Abstract
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Torky, M., R. Baberse, R. Ibrahim, A. E. Hassanien, G. Schaefer, I. Korovin, and S. Y. Zhu, "Credibility investigation of newsworthy tweets using a visualising Petri net model", Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on: IEEE, pp. 003894–003898, 2016. Abstract
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Davendra, D., A. El-Atta, H. Ahmed, M. A. Abu ElSoud, M. Adamek, M. Adhikari, A. Adl, H. Aldosari, Abder-Rahman Ali, A. F. Ali, et al., "Cordeschi, Nicola 43 Couceiro, Micael 83, 131 Czopik, Jan 365 Dasgupta, Kousik 271", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014, vol. 303: Springer, pp. 439, 2014. 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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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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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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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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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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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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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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Abraham, A., and A. - E. Hassanien, Computational social networks: Tools, perspectives and applications, : Springer Science & Business Media, 2012. Abstract
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Tourism