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Badr, Y., R. Chbeir, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: Advanced semantic technologies, : Springer Science & Business Media, 2010. Abstract
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Hassanien, A. - E., Emergent Web Intelligence: Advanced Semantic Technologies, : Advanced Information and Knowledge Processing-Springer Verlag, 2010. Abstract
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Badr, Y., R. Chbeir, A. Abraham, and A. - E. Hassanien, Emergent web intelligence: Advanced semantic technologies, : Springer Science & Business Media, 2010. Abstract
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Hassanien, A. E., Emerging Markets and E-Commerce in Developing Economies, , USA, IGI Global USA, 2008. AbstractWebsite

High Internet penetration in regions such as North America, Australia, and Europe, has proven the World Wide Web as an important medium for e-commerce transaction. Despite the soaring adoption statistics for those already developed societies, diffusion rates still remain low for the less developed countries, with e-commerce in its infancy.Emerging Markets and E-Commerce in Developing Economies enhances understanding of e-commerce models and practices in less developed countries, and extends the growing literature on e-commerce. An essential addition to worldwide library collections in technology, commerce, social sciences, and related fields, this essential contribution expands the body of knowledge in the field with relevant theoretical foundations, methodologies, and frameworks, to the benefit of the international academic, research, governmental, and industrial communities.

Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing Springer , 2013. AbstractWebsite

The selection of the optimal ensembles of classifiers in multiple-classifier selection technique is un-decidable in many cases and it is potentially subjected to a trial-and-error search. This paper introduces a quantitative meta-learning approach based on neural network and rough set theory in the selection of the best predictive model. This approach depends directly on the characteristic, meta-features of the input data sets. The employed meta-features are the degree of discreteness and the distribution of the features in the input data set, the fuzziness of these features related to the target class labels and finally the correlation and covariance between the different features. The experimental work that consider these criteria are applied on twenty nine data sets using different classification techniques including support vector machine, decision tables and Bayesian believe model. The measures of these criteria and the best result classification technique are used to build a meta data set. The role of the neural network is to perform a black-box prediction of the optimal, best fitting, classification technique. The role of the rough set theory is the generation of the decision rules that controls this prediction approach. Finally, formal concept analysis is applied for the visualization of the generated rules.

Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 165–177, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 165–177, 2013. Abstract
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Salama, M. A., A. E. Hassanien, and K. Revett, "Employment of neural network and rough set in meta-learning.", Memetic Computing- Springer, vol. 5, issue 3, pp. 165-177, 2013. Website
Fouad, M. M., V. Snasel, and A. E. Hassanien, "Energy-Aware Sink Node Localization Algorithm for Wireless Sensor Networks", International Journal of Distributed Sensor Networks, , vol. 2015, 2015. Website
Fouad, M. M., V. Snasel, and A. E. Hassanien, "Energy-aware sink node localization algorithm for wireless sensor networks", International Journal of Distributed Sensor Networks, vol. 11, no. 7: SAGE Publications, pp. 810356, 2015. Abstract
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Zhou, X., K. Xiao, Alei Liang, Haibing Guan, and A. E. Hassanien, Energy-based Particle Swarm Optimization: Towards Energy Homeostasis in Social Autonomous Robots, , 2011. Abstract
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Zhou, X., K. Xiao, Alei Liang, Haibing Guan, and A. E. Hassanien, Energy-based Particle Swarm Optimization: Towards Energy Homeostasis in Social Autonomous Robots, , 2011. Abstract
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Asmaa Osamaa, S. A. El-Said, and A. E. Hassanien, "Energy-Efficient Routing Techniques for Wireless Sensors Networks", Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization: IGI Global, pp. 37–62, 2016. Abstract
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Osman, M. A., A. Darwish, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Breast cancer or malignant breast neoplasm is the most common type of cancer in women. Researchers are not sure of the exact cause of breast cancer. If the cancer can be detected early, the options of treatment and the chances of total recovery will increase. Computer Aided Diagnostic (CAD) systems can help the researchers and specialists in detecting the abnormalities early. The main goal of computerized breast cancer detection in digital mammography is to identify the presence of abnormalities such as mass lesions and Micro calcification Clusters (MCCs). Early detection and diagnosis of breast cancer represent the key for breast cancer control and can increase the success of treatment. This chapter investigates a new CAD system for the diagnosis process of benign and malignant breast tumors from digital mammography. X-ray mammograms are considered the most effective and reliable method in early detection of breast cancer. In this chapter, the breast tumor is segmented from medical image using Fuzzy Clustering Means (FCM) and the features for mammogram images are extracted. The results of this work showed that these features are used to train the classifier to classify tumors. The effectiveness and performance of this work is examined using classification accuracy, sensitivity and specificity and the practical part of the proposed system distinguishes tumors with high accuracy.

Osman, M. A., A. Darwish, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Breast cancer or malignant breast neoplasm is the most common type of cancer in women. Researchers are not sure of the exact cause of breast cancer. If the cancer can be detected early, the options of treatment and the chances of total recovery will increase. Computer Aided Diagnostic (CAD) systems can help the researchers and specialists in detecting the abnormalities early. The main goal of computerized breast cancer detection in digital mammography is to identify the presence of abnormalities such as mass lesions and Micro calcification Clusters (MCCs). Early detection and diagnosis of breast cancer represent the key for breast cancer control and can increase the success of treatment. This chapter investigates a new CAD system for the diagnosis process of benign and malignant breast tumors from digital mammography. X-ray mammograms are considered the most effective and reliable method in early detection of breast cancer. In this chapter, the breast tumor is segmented from medical image using Fuzzy Clustering Means (FCM) and the features for mammogram images are extracted. The results of this work showed that these features are used to train the classifier to classify tumors. The effectiveness and performance of this work is examined using classification accuracy, sensitivity and specificity and the practical part of the proposed system distinguishes tumors with high accuracy.

Osman, M. A., A. Darwish, A. E. Khedr, A. Z. Ghalwash, and A. E. Hassanien, "Enhanced Breast Cancer Diagnosis System Using Fuzzy Clustering Means Approach in Digital Mammography", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 925–941, 2017. Abstract
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Mostafa, A., M. A. Fattah, A. Ali, and A. E. Hassanin, "Enhanced Region Growing Segmentation For CT Liver Images", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, . Beni Suef University, Beni Suef, Egypt , Nov. 28-30 , 2015. Abstract

This paper intends to enhance the image for the next usage
of region growing technique for segmenting the region of liver away from
other organs. The approach depends on a preprocessing phase to enhance
the appearance of the boundaries of the liver. This is performed using
contrast stretching and some morphological operations to prepare the
image for next segmentation phase. The approach starts with combining
Otsu's global thresholding with dilation and erosion to remove image
annotation and machine's bed. The second step of image preparation
is to connect ribs, and apply lters to enhance image and deepen liver
boundaries. The combined lters are contrast stretching and texture l-
ters. The last step is to use a simple region growing technique, which has
low computational cost, but ignored for its low accuracy. The proposed
approach is appropriate for many images, where liver could not be sep-
arated before, because of the similarity of the intensity with other close
organs. A set of 44 images taken in pre-contrast phase, were used to test
the approach. Validating the approach has been done using similarity
index. The experimental results, show that the overall accuracy o ered
by the proposed approach results in 91.3% accuracy.

Mostafa, A., M. A. Fattah, A. Fouad, A. E. Hassanien, and H. Hefny, "Enhanced region growing segmentation for CT liver images", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 115–127, 2016. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Enhanced rough sets rule reduction algorithm for classification digital mammography", Journal of Intelligent Systems, vol. 13: FREUND PUBLISHING HOUSE LTD., pp. 151–171, 2003. Abstract
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Elshazly, H. I., A. M. Elkorany, A. E. Hassanien, and A. T. Azar, "Ensemble classifiers for biomedical data: performance evaluation", Computer Engineering & Systems (ICCES), 2013 8th International Conference on: IEEE, pp. 184–189, 2013. Abstract
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Elshazly, H. I., A. M. Elkorany, and A. E. Hassanien, "Ensemble-based classifiers for prostate cancer Diagnosis", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) pp. 49 - 54, Cairo, EGYPT -, December 29-30, 2013.
Elshazly, H. I., A. M. Elkorany, and A. E. Hassanien, "Ensemble-based classifiers for prostate cancer diagnosis", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 49–54, 2013. Abstract
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Sharif, M. M., Alaa Tharwat, A. E. Hassanien, H. A. Hefny, and G. Schaefer, "Enzyme function classification based on borda count ranking aggregation method", Machine Intelligence and Big Data in Industry: Springer International Publishing, pp. 75–85, 2016. Abstract
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