Publications

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Book
Hassanien, A. - E., C. Grosan, and M. F. Tolba, Applications of Intelligent Optimization in Biology and Medicine Current Trends and Open Problems, , Germany , Springer , 2016. Website
Book Chapter
Amin, R., T. Gaber, G. ElTaweel, and A. E. Hassanien, "Biometric and traditional mobile authentication techniques: Overviews and open issues", Bio-inspiring cyber security and cloud services: trends and innovations: Springer Berlin Heidelberg, pp. 423–446, 2014. Abstract
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El-Bendary, N., V. Snasel, G. Adam, F. Mansour, N. I. Ghali, O. S. Soliman, and A. E. Hassanien, "E-Contract Securing System Using Digital Signature Approach", Advanced Communication and Networking: Springer Berlin Heidelberg, pp. 183–189, 2011. Abstract
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El-Bendary, N., V. Snasel, G. Adam, F. Mansour, N. I. Ghali, O. S. Soliman, and A. E. Hassanien, "E-Contract Securing System Using Digital Signature Approach", Advanced Communication and Networking: Springer Berlin Heidelberg, pp. 183–189, 2011. 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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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Wahid, R., N. I. Ghali, H. S. Own, T. - H. Kim, and A. E. Hassanien, "A Gaussian mixture models approach to human heart signal verification using different feature extraction algorithms", Computer Applications for Bio-technology, Multimedia, and Ubiquitous City: Springer Berlin Heidelberg, pp. 16–24, 2012. Abstract
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Ghali, N. I., W. G. Abd-Elmonim, and A. E. Hassanien, Object-Based Image Retrieval System Using Rough Set Approach, , London, Advances in Reasoning-Based Image Processing Intelligent Systems Intelligent Systems Reference Library, 2012, Volume 29, Part 2, 315-329, 2012. Abstract

In this chapter, we present an object-based image retrieval system using the rough set theory. The system incorporates two major modules: Pre-processing and Object-based image retrieval. In pre processing, an image based object segmentation algorithm in the context of the rough set theory is used to segment the images into meaningful semantic regions. A new object similarity measure is proposed for the image retrieval. Performance is evaluated on an image database and the effectiveness of proposed image retrieval system is demonstrated. Experimental results show that the proposed system performs well in terms of speed and accuracy.

Ghali, N. I., W. G. Abd-Elmonim, and A. E. Hassanien, "Object-based image retrieval system using rough set approach", Advances in Reasoning-Based Image Processing Intelligent Systems: Springer Berlin Heidelberg, pp. 315–329, 2012. Abstract
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Ghali, N. I., W. G. Abd-Elmonim, and A. E. Hassanien, "Object-based image retrieval system using rough set approach", Advances in Reasoning-Based Image Processing Intelligent Systems: Springer Berlin Heidelberg, pp. 315–329, 2012. Abstract
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Abdelaziz, A., Moustafa Zein, M. Atef, A. Adl, K. K. A. Ghany, and A. E. Hassanien, "An Orphan Drug Legislation System", Intelligent Systems' 2014: Springer International Publishing, pp. 389–399, 2015. Abstract
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Gaber, T., and A. E. Hassanien, "An Overview of Self-Protection and Self-Healing in Wireless Sensor Networks", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 185–202, 2014. Abstract
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Alaa Tharwat, T. Gaber, A. E. Hassanien, and B. E. Elnaghi, "Particle Swarm Optimization: A Tutorial", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 614–635, 2017. Abstract
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Mohamed Tahoun, Abd El Rahman Shabayek, H. Nassar, M. M. Giovenco, R. Reulke, Eid Emary, and A. E. Hassanien, "Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features", Image Feature Detectors and Descriptors: Springer International Publishing, pp. 135–171, 2016. Abstract
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Ghali, N., M. Panda, A. E. Hassanien, A. Abraham, and V. Snasel, "Social networks analysis: Tools, measures and visualization", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Ghali, N., M. Panda, A. E. Hassanien, A. Abraham, and V. Snasel, "Social networks analysis: Tools, measures and visualization", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Ghali, N., M. Panda, A. E. Hassanien, A. Abraham, and V. Snasel, "Social Networks: Computational Aspects and Mining", Computational Social Networks: Tools, Perspectives and Applications, London, Computer and Communication Networks Springer Series, 2012. Abstract

Computational social science is a new emerging field that has overlapping regions from Mathematics, Psychology, Computer Sciences, Sociology,and Management. Social computing is concerned with the intersection of social behavior and computational systems. It supports any sort of social behavior in or through computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. Thus, blogs, email, instant messaging, social network services, wikis, social bookmarking, and other instances of what is often called social software illustrate ideas from social computing. Social network analysis is the study of relationships among social entities. It is becoming an important tool for investigators. However all the necessary information is often distributed over a number of Web sites. Interest in this field is blossoming as traditional practitioners in the social and behavioral sciences are being joined by researchers from statistics, graph theory, machine learning and data mining. In this chapter, we illustrate the concept of social networks from a computational point of view, with a focus on practical services, tools, and applications and open avenues for further research. Challenges to be addressed and future directions of research are presented and an extensive bibliography is also included.

El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and J. F. Peters, "Strict authentication of multimodal biometric images using near sets", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 249–258, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and J. F. Peters, "Strict authentication of multimodal biometric images using near sets", Soft Computing in Industrial Applications: Springer Berlin Heidelberg, pp. 249–258, 2011. Abstract
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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.

Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: reviews and recent advances", Advances in Robotics and Virtual Reality: Springer Berlin Heidelberg, pp. 363–385, 2012. Abstract
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Ghali, N. I., O. Soluiman, N. El-Bendary, T. M. Nassef, S. A. Ahmed, Y. M. Elbarawy, and A. E. Hassanien, "Virtual reality technology for blind and visual impaired people: reviews and recent advances", Advances in Robotics and Virtual Reality: Springer Berlin Heidelberg, pp. 363–385, 2012. Abstract
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Tourism