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Elhoseny, M., N. Metawa, and A. E. Hassanien, "An automated information system to ensure quality in higher education institutions", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec. 2016. Abstract

Despite the great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an automated information system. The present work introduces a framework for an automated information system that manages the quality assurance in higher educations institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance center in a higher education institution to apply its qualitys standards, and to make sure that they are being maintained and enhanced. This information system contains a core module and 17 sub-modules, which are described in this paper.

Elhoseny, M., N. Metawa, and A. E. Hassanien, "An automated information system to ensure quality in higher education institutions", Computer Engineering Conference (ICENCO), 2016 12th International: IEEE, pp. 196–201, 2016. Abstract
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Elhoseny, M., N. Metawa, and A. E. Hassanien, "An automated information system to ensure quality in higher education institutions,", 2016 12th International Computer Engineering Conference (ICENCO), , Cairo, 28-29 Dec, 2016. Abstract

Despite the great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an automated information system. The present work introduces a framework for an automated information system that manages the quality assurance in higher educations institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance center in a higher education institution to apply its qualitys standards, and to make sure that they are being maintained and enhanced. This information system contains a core module and 17 sub-modules, which are described in this paper.

Esraa Elhariri, N. Elbendary, A. E. Hassanien, and A. Badr, "Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, pp. 101-131, 2014. Abstract

One.of.the.prime.factors.in.ensuring.a.consistent.marketing.of.crops.is.product.quality,.and.the.process.of.
determining.ripeness.stages.is.a.very.important.issue.in.the.industry.of.(fruits.and.vegetables).production,.
since.ripeness.is.the.main.quality.indicator.from.the.customers’.perspective..To.ensure.optimum.yield.of.
high.quality.products,.an.objective.and.accurate.ripeness.assessment.of.agricultural.crops.is.important..
This.chapter.discusses.the.problem.of.determining.different.ripeness.stages.of.tomato.and.presents.a.
content-based.image.classification.approach.to.automate.the.ripeness.assessment.process.of.tomato.via.
examining.and.classifying.the.different.ripeness.stages.as.a.solution.for.this.problem..It.introduces.a.
survey.about.resent.research.work.related.to.monitoring.and.classification.of.maturity.stages.for.fruits/
vegetables.and.provides.the.core.concepts.of.color.features,.SVM,.and.PCA.algorithms..Then.it.describes.
the.proposed.approach.for.solving.the.problem.of.determining.different.ripeness.stages.of.tomatoes..The.
proposed.approach.consists.of.three.phases,.namely.pre-processing,.feature.extraction,.and.classification.
phase..The.classification.process.depends.totally.on.color.features.(colored.histogram.and.color.moments),.
since.the.surface.color.of.a.tomato.is.the.most.important.characteristic.to.observe.ripeness..This.approach.
uses.Principal.Components.Analysis.(PCA).and.Support.Vector.Machine.(SVM).algorithms.for.feature.
extraction.and.classification,.respectively

Esraa Elhariri, N. El-Bendary, A. E. Hassanien, and A. Badr, "Automated ripeness assessment system of tomatoes using PCA and SVM techniques", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, IGI global, pp. 101–130, 2014. Abstract
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Alaa Tharwat, H. M. Zawbaa, T. Gaber, A. E. Hassanien, and V. Snasel, "Automated zebrafish-based toxicity test using bat optimization and adaboost classifier", Computer Engineering Conference (ICENCO), 2015 11th International: IEEE, pp. 169–174, 2015. Abstract
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Anter, A. M., A. T. Azar, A. E. Hassanien, N. El-Bendary, and M. A. Elsoud, "Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 193–198, 2013. Abstract
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Anter, A. M., A. T. Azar, A. E. Hassanien, N. El-Bendary, and M. A. Elsoud, "Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques", Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on: IEEE, pp. 193–198, 2013. Abstract
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Zawbaa, H. M., M. Hazman, M. Abbass, and A. E. Hassanien, "Automatic fruit classification using random forest algorithm", Hybrid Intelligent Systems (HIS), 2014 14th International Conference on: IEEE, pp. 164–168, 2014. Abstract
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Zawbaa, H. M., M. Abbass, M. Hazman, and A. E. Hassenian, "Automatic Fruit Image Recognition System based on Shape and Color Features ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
El-Bendary, N., T. - H. Kim, A. E. Hassanien, and M. Sami, "Automatic image annotation approach based on optimization of classes scores", Computing -Spriner , vol. 96, issue 5, pp. 381-402 , 2014. Website
El-Bendary, N., T. - H. Kim, A. E. Hassanien, and M. Sami, "Automatic image annotation approach based on optimization of classes scores", Computing, vol. 96, no. 5: Springer Vienna, pp. 381–402, 2014. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization ", World Congress on Information and Communication Technologies (WICT), pp. 790 - 794, India, Oct. 30 2012-Nov. Abstract

This paper presents an automatic image annotation approach that integrates the Naive Bayes classifier with particle swarm optimization algorithm for classes' probabilities weighting. The proposed hybrid approach refines the output of multi-class classification that is based on the usage of Naive Bayes classifier for automatically labeling images with a number of words. Each input image is segmented using the normalized cuts segmentation algorithm in order to create a descriptor for each segment. One Naive Bayes classifier is trained for all the classes. Particle swarm optimization algorithm is employed as a search strategy in order to identify an optimal weighting for classes probabilities from Naive Bayes classifier. The proposed approach has been applied on Corel5K benchmark dataset. Experimental results and comparative performance evaluation, for results obtained from the proposed approach and other related researches, demonstrate that the proposed approach outperforms the performance of the other approaches, considering annotation accuracy, for the experimented dataset.

Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 2012. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 2012. Abstract
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Sami, M., N. El-Bendary, and A. E. Hassanien, "Automatic image annotation via incorporating Naive Bayes with particle swarm optimization", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 790–794, 2012. Abstract
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Own, H., and A. E. Hassanien, "Automatic Image Registration Algorithm Based on Multiresolution Local Contrast Entropy and Mutual Information", International Journal of Computers and Their Applications, vol. 12, issue 1, pp. 9-15, 2005.
El-Masry1, W. H., Eid Emary, and A. E. Hassanien, "Automatic Liver CT Image Clustering based on Invasive Weed Optimization Algorithm ", The second International Conference on Engineering and Technology (ICET 2014), German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
El-Masry, W. H., Eid Emary, and A. E. Hassanien, "Automatic liver CT image clustering based on invasive weed optimization algorithm", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–5, 2014. Abstract
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Ahmed M. Anter, M. A. Elsoud, and A. E. Hassanien, "Automatic Liver Parenchyma Segmentation from Abdominal CT Images", The 9th IEEE International Computer Engineering Conference (ICENCO 2013) pp. 32 – 36, Cairo, EGYPT -, December 29-30, , 2013.
Anter, A. M., M. A. Elsoud, and A. E. Hassanien, "Automatic liver Parenchyma segmentation from abdominal CT images", Computer Engineering Conference (ICENCO), 2013 9th International: IEEE, pp. 32–36, 2013. Abstract
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Anter, A. M., A. E. Hassanien, A. T. Azar, and M. A. Elsoud, "Automatic Liver Parenchyma Segmentation System from Abdominal CT Scans using Hybrid Techniques", International Journal of Biomedical Engineering and Technology, vol. 17, issue 2, 2015. AbstractWebsite

In this paper, a multi–layer heuristic approach is introduced to segment liver region from other tissues in multi–slice CT images. Image noise is a principal factor which hampers the visual quality of medical images and can therefore lead to misdiagnosis. To address this issue, we first utilise an algorithm based on median filter to remove noise and enhance the contrast of the CT image. This is followed by performing an adaptive threshold algorithm and morphological operators to preserve the liver structure and remove the fragments of other organs. Then, connected component labelling algorithm was applied to remove false positive regions and focused on liver region. To evaluate the performance of the proposed system, we present tests on different liver CT scans images. The experimental results show that the overall accuracy offered by the employed system is high compared with other related works as well as very fast which segment liver from abdominal CT in less than 0.6 s/slice.

Anter, A. M., A. E. Hassanien, M. A. Elsoud, and A. T. Azar, "Automatic liver parenchyma segmentation system from abdominal CT scans using hybrid techniques", International Journal of Biomedical Engineering and Technology, vol. 17, no. 2: Inderscience Publishers, pp. 148–167, 2015. Abstract
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Soliman, O. S., Jan Platoš, A. E. Hassanien, and Václav Snášel, "Automatic localization and boundary detection of retina in images using basic image processing filters", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 169–182, 2013. Abstract
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Soliman, O. S., Jan Platoš, A. E. Hassanien, and Václav Snášel, "Automatic localization and boundary detection of retina in images using basic image processing filters", Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011: Springer Berlin Heidelberg, pp. 169–182, 2013. Abstract
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