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A
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.

Adham Mohamed, M. M. M. Fouad, Esraa Elhariri, N. El-Bendary, H. M. Zawbaa, Mohamed Tahoun, and A. E. Hassanien, "RoadMonitor: an intelligent road surface condition monitoring system", Intelligent Systems' 2014: Springer International Publishing, pp. 377–387, 2015. Abstract
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Adham Mohamed, H. M. Zawbaa, M. M. M. Fouad, Esraa Elhariri, N. El-Bendary, Mohamed Tahoun, and A. E. Hassanine, "RoadMonitor: An Intelligent Road Surface Condition Monitoring System", IEEE Conf. on Intelligent Systems (2) 2014: 377-387, Poland - Warsaw , 24 -26 Sept. , 2014. Abstract

Well maintained road network is an essential requirement for the safety and consistency of vehicles moving on that road and the wellbeing of people in those vehicles. On the other hand, guaranteeing an adequate maintenance by road managers can be achieved via having sufficient and accurate information concerning road infrastructure quality that can be as well utilized concurrently by the widespread means of users’ mobile devices both locally and worldwide. This article proposes a road condition monitoring framework that detects the road anomalies such as speed bumps. In the proposed approach, the main indicator for road anomalies is the gyroscope around gravity rotation in addition to the accelerometer sensor as a cross-validation method to confirm the detection results that were gathered from the gyroscope.

Ahmed, Z., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets.", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, 8-10 Dec. , 2012. Abstract3220052.pdf

The risk of hepatitis-C virus is considered as a challenge in
the field of medicine. Applying feature reduction technique and generating
rules based on the selected features were considered as an important
step in data mining. It is needed by medical experts to analyze the generated
rules to find out if these rules are important in real life cases.
This paper presents an application of a rough set analysis to discover
the dependency between the attributes, and to generate a set of reducts
consisting of a minimal number of attributes. The experimental results
obtained, show that the overall accuracy offered by the rough sets is high.

Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Ali, J., and A. E. Hassanien, "Rough Set Approach for Generation of Classification Rules of Breast Cancer Data.", Informatica, vol. 15, issue 1, pp. 23-38, 2004. Abstract

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases.

In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.

Asad, A. H., Eid Elamry, and A. E. Hassanien, "Retinal vessels segmentation based on water flooding model", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 43–48, 2013. Abstract
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Asad, A. H., Eid Elamry, and A. E. Hassanien, "Retinal vessels segmentation based on water flooding model", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) pp. 43 - 48 , Cairo, EGYPT -, December 29-30, , 2013.
Awad, A. I., H. M. Zawbaa, H. A. Mahmoud, E. H. H. A. Nabi, R. H. Fayed, and A. E. Hassanien, "A robust cattle identification scheme using muzzle print images", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 529–534, 2013. Abstract
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Awad, A. I., H. M. Zawbaa, H. A. Mahmoud, E. H. H. A. Nabi, R. H. Fayed, and A. E. Hassanien, "A robust cattle identification scheme using muzzle print images", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 529–534, 2013. Abstract
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Azar, A. T., H. I. Elshazly, A. E. Hassanien, and A. M. Elkorany, "Random Forest Classifier for Lymph Diseases", Computer Methods and Programs in Biomedicine , Elsevier 2013 , vol. 113, issue 2, pp. 465-473 , 2014. Website
Azar, A. T., H. I. Elshazly, A. E. Hassanien, and A. M. Elkorany, "A random forest classifier for lymph diseases", Computer methods and programs in biomedicine, vol. 113, no. 2: Elsevier, pp. 465–473, 2014. Abstract
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B
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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Banu, P. K. N., H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough set based feature selection for egyptian neonatal jaundice", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 367–378, 2014. Abstract
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C
Chakraborty, M., A. - E. Hassanien, D. Slezak, and W. Zhu, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, : Springer-Verlag Berlin Heidelberg, 2009. Abstract
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Chakraborty, M., A. - E. Hassanien, D. Slezak, and W. Zhu, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, : Springer-Verlag Berlin Heidelberg, 2009. Abstract
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E
E. Emary, H. M. Zawbaa, boul Ella Hassanien, M. F. Tolba, and V. Snasel, ""Retinal vessel segmentation based on flower pollination search algorithm"", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer), Ostrava, Czech Republic., 23-24 June, 2014. Abstractibica2014_p11.pdf

This paper presents an automated retinal blood vessels segmentation
approach based on flower pollination search algorithm (FPSA). The flower pollination
search is a new algorithm based on the flower pollination process of flowering
plants. The FPSA searches for the optimal clustering of the given retinal
image into compact clusters under some constrains. Shape features are used to
further enhance the clustering results using local search method. The proposed
retinal blood vessels approach is tested on a publicly available databases DRIVE
a of retinal images. The results demonstrate that the performance of the proposed
approach is comparable with state of the art techniques in terms of accuracy, sensitivity
and specificity.

Eid Emary, H. M. Zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, "Retinal blood vessel segmentation using bee colony optimisation and pattern search", Neural Networks (IJCNN), 2014 International Joint Conference on: IEEE, pp. 1001–1006, 2014. Abstract
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Eid Emary, H. M. Zawbaa, A. E. Hassanien, M. F. Tolba, and Václav Snášel, "Retinal vessel segmentation based on flower pollination search algorithm", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 93–100, 2014. Abstract
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Eid Emary, H. M. Zawbaa, A. E. Hassanien, G. Schaefer, and A. T. Azar, "Retinal vessel segmentation based on possibilistic fuzzy c-means clustering optimised with cuckoo search", Neural Networks (IJCNN), 2014 International Joint Conference on: IEEE, pp. 1792–1796, 2014. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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El Bakrawy, L. M., N. I. Ghali, A. E. Hassanien, and T. - H. Kim, "A rough k-means fragile watermarking approach for image authentication", Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on: IEEE, pp. 19–23, 2011. Abstract
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Ella Hassanien, A., M. E. Abdelhafez, and H. S. Own, "Rough set analysis in knowledge discovery: a case of Kuwaiti diabetic children patients", Advances in Fuzzy Systems, pp. 1–13, 2007. Abstract
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