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.

Abdelhameed Ibrahim, T. Horiuchi, S. Tominaga, and A. E. Hassanien, " Color Invariant Representation and Applications", Handbook of Research on Machine Learning Innovations and Trends,, USA, IGI, USA, pp.21, 2017. Abstract

Illumination factors such as shading, shadow, and highlight observed from object surfaces affect the appearance and analysis of natural color images. Invariant representations to these factors were presented in several ways. Most of these methods used the standard dichromatic reflection model that assumed inhomogeneous dielectric material. The standard model cannot describe metallic objects. This chapter introduces an illumination-invariant representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. The illumination color is estimated from two inhomogeneous surfaces to recover the surface reflectance of object without using a reference white standard. The overall performance of the invariant representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of the representation for effective edge detection is introduced and compared with the state-of-the-art illumination-invariant methods.

Issa, M., and A. E. Hassanien, " Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques: ", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

Mouhamed, M. R., A. Darwish, and A. E. Hassanien, "2D and 3D Intelligent Watermarking", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 652–669, 2017. Abstract
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Soliman, M. M., and A. E. Hassanien, "3D Watermarking Approach Using Particle Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

This work proposes a watermarking approach by utilizing the use of Bio-Inspired techniques such as swarm intelligent in optimizing watermarking algorithms for 3D models. In this proposed work we present an approach of 3D mesh model watermarking by introducing a new robust 3D mesh watermarking authentication methods by ensuring a minimal surface distortion at the same time ensuring a high robustness of extracted watermark. In order to achieve these requirements this work proposes the use of Particle Swarm Optimization (PSO) as Bio-Inspired optimization techniques. The experiments were executed using different sets of 3D models. In all experimental results we consider two important factors: imperceptibility and robustness. The experimental results show that the proposed approach yields a watermarked object with good visual definition; at the same time, the embedded watermark was robust against a wide variety of common attacks.

Soliman, M. M., and A. E. Hassanien, "3D Watermarking Approach Using Particle Swarm Optimization Algorithm", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 582–613, 2017. Abstract
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Václav Snášel, A. Keprt, A. Abraham, and A. E. Hassanien, "Approximate string matching by fuzzy automata", Man-Machine Interactions: Springer Berlin Heidelberg, pp. 281–290, 2009. Abstract
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Václav Snášel, A. Keprt, A. Abraham, and A. E. Hassanien, "Approximate string matching by fuzzy automata", Man-Machine Interactions: Springer Berlin Heidelberg, pp. 281–290, 2009. Abstract
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Mostafa, A., A. Fouad, M. A. Fattah, A. E. Hassanien, and H. Hefny, "Artificial Bee Colony Based Segmentation for CT Liver Images", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 409–430, 2016. Abstract
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Sharif, M. M., Alaa Tharwat, A. E. Hassanien, and H. A. Hefeny, "Automated Enzyme Function Classification Based on Pairwise Sequence Alignment Technique", Intelligent Data Analysis and Applications: Springer International Publishing, pp. 499–510, 2015. Abstract
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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.

Amin, I. I., A. E. Hassanien, S. K. Kassim, and H. A. Hefny, "Big DNA Methylation data analysis and visualizing in a common form of breast cancer", Big Data in Complex Systems: Springer International Publishing, pp. 375–392, 2015. Abstract
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Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends, USA, IGI, 2017. Abstract

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

Sahlol, A. T., and A. E. Hassanien, "Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters", Handbook of Research on Machine Learning Innovations and Trends: IGI Global, pp. 897–914, 2017. Abstract
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Sayed, G. I., M. Soliman, and A. E. Hassanien, "Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection", Medical Imaging in Clinical Applications: Springer International Publishing, pp. 487–506, 2016. Abstract
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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "Bio-inspiring Techniques in Watermarking Medical Images: A Review", Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations: Springer Berlin Heidelberg, pp. 93–114, 2014. Abstract
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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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Soliman, M. M., A. E. Hassanien, and H. M. Onsi, "A Blind Robust 3D-Watermarking Scheme Based on Progressive Mesh and Self Organization Maps", Advances in Security of Information and Communication Networks: Springer Berlin Heidelberg, pp. 131–142, 2013. Abstract
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Mouhamed, M. R., H. M. Zawbaa, E. T. Al-Shammari, A. E. Hassanien, and V. Snasel, "Blind watermark approach for map authentication using support vector machine", Advances in security of information and communication networks: Springer Berlin Heidelberg, pp. 84–97, 2013. 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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Peters, J. F., and S. K. Pal, "Cantor, fuzzy, near, and rough sets in image analysis", Rough fuzzy image analysis: Foundations and methodologies: CRC Press, pp. 1–1, 2010. Abstract
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Ayeldeen, H., O. Hegazy, and A. E. Hassanien, "Case selection strategy based on K-means clustering", Information Systems Design and Intelligent Applications: Springer India, pp. 385–394, 2015. Abstract
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Ayeldeen, H., O. Shaker, O. Hegazy, and A. E. Hassanien, "Case-Based Reasoning: A Knowledge Extraction Tool to Use", Information systems design and intelligent applications: Springer India, pp. 369–378, 2015. Abstract
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