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Mokhtar, U., M. A. S. Ali, A. E. Hassanien, and H. Hefny, "Identifying two of tomatoes leaf viruses using support vector machine", Information Systems Design and Intelligent Applications: Springer India, pp. 771–782, 2015. Abstract
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Moustafa Zeina, A. A. Fatma Yakouba, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", International Conference on Communications, management, and Information technology (ICCMIT'2015) Volume 65, 2015, Pages 582–591, Ostrava, Czech Republic, 2015. Abstract

Geotagged photos carry hidden data about the surrounding area, and the owner of the photo. Moreover; Geotagged photos have background information about the user, where the alternative resources of Geo-spatial data lack background information. In this study, we propose identification for the circles of relations of the smartphone user from Geotagged photos. The proposed solution mainly depends on a framework, which is based on smartphone photo gallery. The framework extracts a degree of relation between smartphone user and circles of relations entities. Circles of relations incorporate closest people, places, where the participant visits, and interests. The circles of relations are represented in a social graph, which shows the clusters of social relations and interests of smartphone user. The social graph clarifies the nature and the degree of the relations for the participants. The results of framework introduced the relation between the level of variety of participant social relations, and the degree of relations.

Moustafa Zein, F. Yakoub, A. Adl, A. E. Hassanien, and V. Snasel, "Identifying Circles of Relations from Smartphone Photo Gallery", Procedia Computer Science, vol. 65: Elsevier, pp. 582–591, 2015. Abstract
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and ella S. Udhaya Kumar, H. Hannah Inbarani, A. T. A. A. H., "Identification of Heart Valve Disease using Bijective, Soft sets Theory ", International Journal of Rough Sets and Data Analysis, vol. 1, issue 2, pp. , 1(2), 1-13, 2014. Abstract

Major complication of heart valve diseases is congestive heart valve failure. The heart is of essential significance to human beings. Auscultation with a stethoscope is considered as one of the techniques used in the analysis of heart diseases. Heart auscultation is a difficult task to determine the heart condition and requires some superior training of medical doctors. Therefore, the use of computerized techniques in the diagnosis of heart sounds may help the doctors in a clinical environment. Hence, in this study computer-aided heart sound diagnosis is performed to give support to doctors in decision making. In this study, a novel hybrid Rough-Bijective soft set is developed for the classification of heart valve diseases. A rough set (Quick Reduct) based feature selection technique is applied before classification for increasing the classification accuracy. The experimental results demonstrate that the overall classification accuracy offered by the employed Improved Bijective soft set approach (IBISOCLASS) provides higher accuracy compared with other classification techniques including hybrid Rough-Bijective soft set (RBISOCLASS), Bijective soft set (BISOCLASS), Decision table (DT), Naïve Bayes (NB) and J48.

Kumar, U. S., H. H. Inbarani, A. T. Azar, and A. E. Hassanien, "Identification of heart valve disease using bijective soft sets theory", International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 1, no. 2: IGI Global, pp. 1–14, 2014. Abstract
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El-Baz, A. H., A. E. Hassanien, and G. Schaefer, "Identification of Diabetes Disease Using Committees of Neural Network-Based Classifiers", Machine Intelligence and Big Data in Industry: Springer International Publishing, pp. 65–74, 2016. Abstract
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Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, and A. E. Hassanien, "ICF based automation system for spinal cord injuries rehabilitation", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 192–197, 2014. Abstract
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