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et.al., A. E. H., "AMLTA (2018): International Conference on Advanced Machine Learning Technologies and Applications", AMLTA (2018): International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Springer, 2018.
Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "Plant classification system based on leaf features", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 271–276, 2014. Abstract
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Esraa Elhariri, N. El-Bendary, A. M. M. Hussein, A. E. Hassanien, and A. Badr, "Bell pepper ripeness classification based on support vector machine", Engineering and Technology (ICET), 2014 International Conference on: IEEE, pp. 1–6, 2014. Abstract
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Esraa Elhariri, N. El-Bendary, A. E. Hassanien, and A. Abraham, "Grey wolf optimization for one-against-one multi-class support vector machines", Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of: IEEE, pp. 7–12, 2015. Abstract
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Esraa Elhariri, N. El-Bendary, A. E. Hassanien, A. Badr, Ahmed M. M. Hussein, and V. Snasel, "Random forests based classification for crops ripeness stage", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014. ibica2014p25.pdf
Esraa Elhariri, N. El-Bendary, and A. A. Aboul Ella Hassanien, "Grey Wolf Optimization for One-Against-One Multi-class Support Vector Machines", 7th IEEE International Conference of Soft Computing and Pattern Recognition, , Kyushu University, Fukuoka, Japan, , November 13 - 15, 2015. Abstract

Grey Wolf Optimization (GWO) algorithm is a
new meta-heuristic method, which is inspired by grey wolves,
to mimic the hierarchy of leadership and grey wolves hunting
mechanism in nature. This paper presents a hybrid model that
employs grey wolf optimizer (GWO) along with support vector
machines (SVMs) classification algorithm to improve the classification
accuracy via selecting the optimal settings of SVMs
parameters. The proposed approach consists of three phases;
namely pre-processing, feature extraction, and GWO-SVMs
classification phases. The proposed classification approach was
implemented by applying resizing, remove background, and
extracting color components for each image. Then, feature
vector generation has been implemented via applying PCA
feature extraction. Finally, GWO-SVMs model is developed
for selecting the optimal SVMs parameters. The proposed
approach has been implemented via applying One-againstOne
multi-class SVMs system using 3-fold cross-validation. The
datasets used for experiments were constructed based on real
sample images of bell pepper at different stages, which were
collected from farms in Minya city, Upper Egypt. Datasets
of total 175 images were used for both training and testing
datasets. Experimental results indicated that the proposed
GWO-SVMs approach achieved better classification accuracy
compared to the typical SVMs classification algorithm.

Esraa Elhariri, N. El-Bendary, A. E. Hassanien, A. Badr, A. M. M. Hussein, and Václav Snášel, "Random forests based classification for crops ripeness stages", Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014: Springer International Publishing, pp. 205–215, 2014. Abstract
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Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "Bio-inspired optimization for feature set dimensionality reduction", Advances in Computational Tools for Engineering Applications (ACTEA), 2016 3rd International Conference on: IEEE, pp. 184–189, 2016. 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

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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Esraa Elhariri, N. El-Bendary, A. E. Hassanien, and V. Snasel, "An Assistive Object Recognition System for Enhancing Seniors Quality of Life", Procedia Computer Science, vol. 65: Elsevier, pp. 691–700, 2015. Abstract
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Esraa Elhariri, N. El-Bendary, A. M. M. Hussein, A. E. Hassanien, and A. Badr, "Bell Pepper Ripeness Classification based on Support Vector Machine ", The second International Conference on Engineering and Technology , German Uni - Cairo Egypt, 19 Apr - 20 Apr , 2014.
Esraa Elhariri, N. El-Bendary, Mohamed Mostafa 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. Advances in Intelligent Systems and Computing(Springer) , Czech republic , 2013.
Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "A Hybrid Classification Model for EMG Signals Using Grey Wolf Optimizer and SVMs", The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt: Springer International Publishing, pp. 297–307, 2016. Abstract
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Esraa Elhariri, N. El-Bendary, and A. E. Hassanien, "A Hybrid Classification Model for EMG signals using Grey Wolf Optimizer", the 1st International Conference on Advanced Intelligent Systems and Informatics (AISI’15) Springer, Beni Suef University, Beni Suef, Eg, Nov. 28-30, 2015.
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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Eslam Hassan, A. Hafez, A. E. H. and, and A. Fahmy, " Nature inspired algorithms for solving the community detection problem, ", Logic Journal of the IGPL: Oxford Journals, 2017.
Emarya, E., H. M. Zawbaab, and A. E. Hassanien, "Binary Gray Wolf Optimization Approaches for Feature Selection", Neurocomputing, 2015. AbstractWebsite

In this work, a novel binary version of the gray wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Gray wolf optimizer (GWO) is one of the latest bio-inspired optimization techniques, which simulate the hunting process of gray wolves in nature. The binary version introduced here is performed using two different approaches. In the first approach, individual steps toward the first three best solutions are binarized and then stochastic crossover is performed among the three basic moves to find the updated binary gray wolf position. In the second approach, sigmoidal function is used to squash the continuous updated position, then stochastically threshold these values to find the updated binary gray wolf position. The two approach for binary gray wolf optimization (bGWO) are hired in the feature selection domain for finding feature subset maximizing the classification accuracy while minimizing the number of selected features. The proposed binary versions were compared to two of the common optimizers used in this domain namely particle swarm optimizer and genetic algorithms. A set of assessment indicators are used to evaluate and compared the different methods over 18 different datasets from the UCI repository. Results prove the capability of the proposed binary version of gray wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

Emary, E., H. M. Zawbaa, C. Grosan, and A. E. H. Ali, "Feature subset selection approach by Gray-wolf optimization", The 1st Afro-European Conference for Industrial Advancement, , Addis Ababa, Ethiopia, November 17-19, , 2014.
Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Binary grey wolf optimization approaches for feature selection", Neurocomputing, vol. 172, issue 8, pp. 371–381, 2016. Website
Emary, E., Waleed Yamany, A. E. Hassanien, and V. Snasel, "Multi-objective gray-wolf optimization for attribute reduction", Procedia Computer Science, vol. 65: Elsevier, pp. 623–632, 2015. Abstract
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Emary, E., Waleed Yamany, and A. E. Hassanien, "New approach for feature selection based on rough set and bat algorithm", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 346–353, 2014. Abstract
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Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Binary ant lion approaches for feature selection", Neurocomputing, vol. 213: Elsevier, pp. 54–65, 2016. Abstract
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Emary, E., H. M. Zawbaa, and A. E. Hassanien, "Possibilistic fuzzy c-means clustering optimized with Cuckoo search for retinal vessel segmentation", The annual IEEE International Joint Conference on Neural Networks (IJCNN) –, Beijing, China, July 6-11, , 2014. Abstract

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Emary, E., R. E. Elesawy, S. A. M. El Ella, and A. E. Hassanien, "Aquatic weeds prediction: A comparative study", Computer Engineering & Systems (ICCES), 2014 9th International Conference on: IEEE, pp. 259–265, 2014. Abstract
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