Publications

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Book Chapter
El-said, S. A., and A. E. Hassanien, " Artificial Eye Vision Using Wireless Sensor Networks", Wireless Sensor Networks: Theory and Applications, USA, , CRC Press, Taylor and Francis Group, 2013. Abstractk15146_c023.pdf

In the past few years, many wireless sensor networks (WSN) had been deployed. It has proved its usage in the future distributed computing environment. Some of its specific applications are habitat monitoring, object tracking, nuclear reactor controlling, fire detection, traffic monitoring, and health care. The main goals of this paper is to describe the major challenges and open research problems of using WSN in healthcare and survey advancements in using WSN to build a chronically implanted artificial retina for visually impaired people. Using WSN in vision repairing addresses two retinal diseases: Age-related Macular Degeneration (severe vision loss at the center of the retina in over 60) and Retinitis Pigmentosa (photoreceptor dysfunction → loss of peripheral vision). The use of WSN in artificial retina provides new features that have the potential to be an economically viable to assist people with visual impairments.

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

Ahmed M. Anter, M. A. Elsoud, and A. E. Hassanien, "Automatic Mammographic Parenchyma Classification According to BIRADS Dictionary", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, USA, IGI, pp. 22-37,, 2014. Abstract

Internal.density.of.the.breast.is.a.parameter.that.clearly.affects.the.performance.of.segmentation.and.
classification.algorithms.to.define.abnormality.regions..Recent.studies.have.shown.that.their.sensitivity.
is.significantly.decreased.as.the.density.of.the.breast.is.increased..In.this.chapter,.enhancement.and. segmentation.processis applied to increase the computation and focus onmammographic parenchyma.
This.parenchyma is analyzed to discriminate tissue density according to BIRADS using Local Binary
Pattern.(LBP),.Gray.Level.Co-Occurrence.Matrix.(GLCM),.Fractal.Dimension.(FD),.and.feature.fusion.
technique.is.applied.to.maximize.and.enhance.the.performance.of.the.classifier.rate..The.different.methods.
for.computing.tissue.density.parameter.are.reviewed,.and.the.authors.also.present.and.exhaustively.
evaluate.algorithms.using.computer.vision.techniques..The.experimental.results.based.on.confusion.
matrix.and.kappa.coefficient.show.a.higher.accuracy.is.obtained.by.automatic.agreement.classification.

Asmaa Hashem Sweidan, N. El-Bendary, O. M. Hegazy, and A. E. Hassanien, "Biomarker-Based Water Pollution Assessment System Using Case-Based Reasoning", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 547–557, 2015. Abstract
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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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Fouad, M. M., K. M. Amin, N. El-Bendary, and A. E. Hassanien, "Brain Computer Interface: A Review", Brain-Computer Interfaces: Springer International Publishing, pp. 3–30, 2015. Abstract
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Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "Classification Approach Based on Rough Mereology", Recent Advances in Intelligent Informatics: Springer International Publishing, pp. 175–184, 2014. Abstract
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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
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Salama, M., M. Panda, Y. Elbarawy, A. E. Hassanien, and A. Abraham, "Computational Social Networks: Security and Privacy", Computational Social Networks: Springer London, pp. 3–21, 2012. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. Abstract
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Panda, M., N. El-Bendary, M. A. Salama, A. E. Hassanien, and A. Abraham, "Computational social networks: Tools, perspectives, and challenges", Computational Social Networks: Springer London, pp. 3–23, 2012. 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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Ahmed, K., A. E. Hassanien, and E. Ezzat, "An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 1062–1075, 2017. Abstract
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Asmaa Osamaa, S. A. El-Said, and A. E. Hassanien, "Energy-Efficient Routing Techniques for Wireless Sensors Networks", Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization: IGI Global, pp. 37–62, 2016. Abstract
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Semary, N. A., Alaa Tharwat, Esraa Elhariri, and A. E. Hassanien, "Fruit-based tomato grading system using features fusion and support vector machine", Intelligent Systems' 2014: Springer International Publishing, pp. 401–410, 2015. Abstract
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Salama, M. A., H. F. Eid, R. A. Ramadan, A. Darwish, and A. E. Hassanien, "Hybrid intelligent intrusion detection scheme", Soft computing in industrial applications: Springer Berlin Heidelberg, pp. 293–303, 2011. Abstract
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abd elaziz, M., A. A. Ewees, and A. E. Hassanien, "Hybrid Swarms Optimization Based Image Segmentation", Hybrid Soft Computing for Image Segmentation: Springer International Publishing, pp. 1–21, 2016. 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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Mahmood, M. A., N. El-Bendary, Jan Platoš, A. E. Hassanien, and H. A. Hefny, "An Intelligent Multi-agent Recommender System", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 201–213, 2014. Abstract
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Eid, H. F., A. E. Hassanien, T. - H. Kim, and S. Banerjee, "Linear correlation-based feature selection for network intrusion detection model", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 240–248, 2013. Abstract
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Mahmoud, R., N. El-Bendary, H. M. O. Mokhtar, A. E. Hassanien, and H. A. Shaheen, "Machine Learning-Based Measurement System for Spinal Cord Injuries Rehabilitation Length of Stay", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 523–534, 2015. Abstract
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Esraa Elhariri, N. El-Bendary, M. M. M. Fouad, Jan Platoš, A. E. Hassanien, and A. M. M. Hussein, "Multi-class SVM based classification approach for tomato ripeness", Innovations in Bio-inspired Computing and Applications: Springer International Publishing, pp. 175–186, 2014. Abstract
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Salama, M. A., M. M. M. Fouad, N. El-Bendary, and A. E. O. Hassanien, "Mutagenicity Analysis Based on Rough Set Theory and Formal Concept Analysis", Recent Advances in Intelligent Informatics: Springer International Publishing, pp. 265–273, 2014. Abstract
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