Publications

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Book Chapter
Waleed Yamany, N. El-Bendary, H. M. Zawbaa, A. E. Hassanien, and Václav Snášel, "Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 259–270, 2014. Abstract
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Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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Kacprzyk, J., J. F. Peters, A. Abraham, and A. E. Hassanien, "Rough Sets in Medical Imaging", Computational Intelligence in Medical Imaging: Techniques and Applications: Chapman and Hall/CRC, pp. 47–87, 2009. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, and J. Kacprzyk, "Rough Sets in Medical Imaging: Foundations and Trends", Computational Intelligence in Medical Imaging: Techniques and Applications, USA, CRC, 2009. Abstract

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AboulElla, H., A. Abraham, J. F. Peters, and G. Schaefer, "Rough Sets in Medical Informatics Applications", Applications of Soft Computing - Advances in Intelligent and Soft Computing, pp 23-30, Berlin , Springer Berlin Heidelberg (ISSN: 978-3-540-89618-0), 2009. Abstract

Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative ‘youth’ compared to other techniques, provide a suitable method in such applications. In this paper, we provide a short summary on the use of rough sets in the medical informatics domain, focussing on applications of medical image segmentation, pattern classification and computer assisted medical decision making.

Hassanien, A. E., A. Abraham, J. F. Peters, and G. Schaefer, "Rough sets in medical informatics applications", Applications of soft computing: Springer Berlin Heidelberg, pp. 23–30, 2009. 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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Elhoseny, M., A. Farouk, A. Shehab, and A. E. Hassanien, "Secure Image Processing and Transmission Schema in Cluster-Based Wireless Sensor Network", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

WSN as a new category of computer-based computing platforms and network structures is showing new applications in different areas such as environmental monitoring, health care and military applications. Although there are a lot of secure image processing schemas designed for image transmission over a network, the limited resources and the dynamic environment make it invisible to be used with Wireless Sensor Networks (WSNs). In addition, the current secure data transmission schemas in WSN are concentrated on the text data and are not applicable for image transmission's applications. Furthermore, secure image transmission is a big challenging issue in WSNs especially for the application that uses image as its main data such as military applications. The reason why is because the limited resources of the sensor nodes which are usually deployed in unattended environments. This chapter introduces a secure image processing and transmission schema in WSN using Elliptic Curve Cryptography (ECC) and Homomorphic Encryption (HE).

Ali, A. F., and A. - E. Hassanien, "A Simplex Nelder Mead Genetic Algorithm for Minimizing Molecular Potential Energy Function", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 1–21, 2016. Abstract
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El-Bendary, N., Mohamed Mostafa M. Fouad, Rabie A. Ramadan, S. Banerjee, and A. E. Hassanien, "Smart Environmental Monitoring Using Wireless Sensor Networks.", Wireless Sensor Networks: Theory and Applications, pp. 733-755, , USA, CRC Press, Taylor and Francis Group, 2013. k15146_c025.pdf
Panda, M., N. El-Bendary, M. Salama, A. E. Hassanien, and A. Abraham, "Social Networks Analysis: Basics, Measures and Visualizing Authorship Networks in DBLP Data", Computational Social Networks: Mining and Visualization, London, Series in Computer Communications and Networks, Springer Verlag, 2012. Abstract

Social Network Analysis (SNA) is becoming an important tool for investigators, but all the necessary information is often available in a distributed environment. Currently there is no information system that helps managers and team leaders to monitor the status of a social network. This chapter presents an overview of the basic concepts of social networks in data analysis including social networks analysis metrics and performances. Different problems in social networks are discussed such as uncertainty, missing data and finding the shortest path in social network. Community structure, detection and visualization in social network analysis is also discussed and reviewed. This chapter bridges the gap among the users by combining social network analysis methods and information visualization technology to help user visually identify the occurrence of a possible relationship amongst the members in a social network. In addition, briefly describing the different performance measures that have been encountered during any network analysis in order to understand the fundamental behind the comprehension. This chapter also, presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science, which is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. Challenges to be ad dressed and future directions of research are also presented and an extensive bibliography is included.order to understand the fundamental behind the comprehension. This chapter also, presents an online analysis tool called Forcoa.NET, which is built over the DBLP dataset of publications from the field of computer science, which is focused on the analysis and visualization of the co-authorship relationship based on the intensity and topic of joint publications. Challenges to be ad dressed and future directions of research are also presented and an extensive bibliography is included.

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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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Social Networks Security and Privacy: Basics,Threats and Case Study to Visualize Foreign Terrorist Network dataset", Computational Social Networks: Security and Privacy, London, Series in Computer Communications and Networks, Springer Verlag, , 2012. Abstract

The continuous self-growing nature of social networks makes it hard to define a line of safety around these networks. Users in social networks are not interacting with the web only, but also with trusted groups that may contain enemies. There are different kinds of attacks on these networks including causing damage to the computer systems and steeling information about users. These attacks are not affecting individuals only, but also the organizations they are belonging to. Protection from these attacks should be performed by the users and security experts of the network. Advices should be provided to users of these social networks. Also security-experts should be sure that the contents transmitted through the network do not contain malicious or harmful data. This chapter shows the security risks and the tasks applied to minimize those risks. Explain the most famous ways that attackers and malicious use. Then show the security measures for each way. Also present a security guide and a social network security and privacy made in 2011, and finally a case study about the list of Foreign Terrorist Network dataset.

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.

Abdelhameed Ibrahim, T. Horiuchi, S. Tominaga, and A. E. Hassanien, "Spectral Reflectance Images and Applications", Image Feature Detectors and Descriptors: Springer International Publishing, pp. 227–254, 2016. 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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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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Ali, A. F., and A. - E. Hassanien, "A Survey of Metaheuristics Methods for Bioinformatics Applications", Applications of Intelligent Optimization in Biology and Medicine: Springer International Publishing, pp. 23–46, 2016. Abstract
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Reham Gharbia, and A. E. Hassanien, "Swarm Intelligence Based on Remote Sensing Image Fusion: Comparison between the Particle Swarm Optimization and the Flower Pollination Algorithm ", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

This chapter presents a remote sensing image fusion based on swarm intelligence. Image fusion is combining multi-sensor images in a single image that has most informative. Remote sensing image fusion is an effective way to extract a large volume of data from multisource images. However, traditional image fusion approaches cannot meet the requirements of applications because they can lose spatial information or distort spectral characteristics. The core of the image fusion is image fusion rules. The main challenge is getting suitable weight of fusion rule. This chapter proposes swarm intelligence to optimize the image fusion rule. Swarm intelligence algorithms are a family of global optimizers inspired by swarm phenomena in nature and have shown better performance. In this chapter, two remote sensing image fusion based on swarm intelligence algorithms, Particle Swarm Optimization (PSO) and flower pollination algorithm are presented to get an adaptive image fusion rule and comparative between them.

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.

Hafez, A. I., A. E. Hassanien, and A. A. Fahmy, "Testing community detection algorithms: A closer look at datasets", Social Networking: Springer International Publishing, pp. 85–99, 2014. Abstract
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El-Dahshan, E. - S. A., A. E. Hassanien, A. Radi, and S. Banerjee, "Ultrasound Biomicroscopy Glaucoma Images Analysis Based on Rough Set and Pulse Coupled Neural Network", Foundations of Computational Intelligence, Volume 2, pp. 275-293 , London, Springer , 2009. Abstract

The objective of this book chapter is to present the rough sets and pulse coupled neural network scheme for Ultrasound Biomicroscopy glaucoma images analysis. To increase the efficiency of the introduced scheme, an intensity adjustment process is applied first using the Pulse Coupled Neural Network (PCNN) with a median filter. This is followed by applying the PCNN-based segmentation algorithm to detect the boundary of the interior chamber of the eye image. Then, glaucoma clinical parameters have been calculated and normalized, followed by application of a rough set analysis to discover the dependency between the parameters and to generate set of reduct that contains minimal number of attributes. Finally, a rough confusion matrix is designed for discrimination to test whether they are normal or glaucomatous eyes. Experimental results show that the introduced scheme is very successful and has high detection accuracy.

El-Dahshan, E. - S. A., A. E. Hassanien, A. Radi, and S. Banerjee, "Ultrasound biomicroscopy glaucoma images analysis based on rough set and pulse coupled neural network", Foundations of Computational Intelligence Volume 2: Springer Berlin Heidelberg, pp. 275–293, 2009. Abstract
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