Publications

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2014
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, 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. 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

Yasser Mahmoud Awad, A. A. Abdullah, T. Y. Bayoumi, K. Abd-Elsalam, and A. E. Hassanien, "Early Detection of Powdery Mildew Disease in Wheat (Triticum aestivum L.) Using Thermal Imaging Technique", Intelligent Systems'2014 Advances in Intelligent Systems and Computing Volume 323, 2015, pp 755-765, Poland , 2014. Abstract

Powdery mildew caused by Erysiphe graminis f. sp. tritici is one of the most harmful disease causing great losses in wheat yield. Currently, thermal spectral sensing of plant disease under different environmental conditions in field is a cutting-edge research. Objectives of this study were to assess thermal imaging of normal and infected leaves for early detection of powdery mildew in wheat after the artificial infection with Erysiphe graminis fungus in a pot experiment under greenhouse conditions. Pot experiment lasting for 30 days was conducted. Additionally, wheat seedlings were artificially infected with pathogen at 10 days from sowing. This is the first study in Egypt to use thermal imaging technique for early detection of powdery mildew disease on leaf using thermal signatures of artificial infected leaves as a reference images. Particularly, the variations in temperature between infected and healthy leaves of wheat and the variation between air and leaf-surface temperatures under greenhouse conditions were sensed for early detection of disease. Results revealed that infection with powdery mildew pathogen induced changes in leaf temperature (from 0.37 °C after one hour from the infection to 0.78 °C at 21 days after infection with the pathogen) and metabolism, contributing to a distinct thermal signature characterizing the early and late phases of the infection.

Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members", Social Networking, London, Intelligent Systems Reference Library Springer, 2014.
Ali, M. A., A. Assefa, D. Assefa, L. Bal{\'ık, A. Basu, O. Berger, E. Berhan, B. Beshah, E. Birhan, T. Buriánek, et al., "Abraham, Ajith 183, 293,303, 315, 371 Ahmed, Nada 315 Aldosari, Hamoud M. 303 Alhamedi, Adel H. 303", Afro-European Conference for Industrial Advancement: Proceedings of the First International Afro-European Conference for Industrial Advancement AECIA 2014, vol. 334: Springer, pp. 383, 2014. Abstract
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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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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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Sarkar, M., S. Banerjee, and A. E. Hassanien, "Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members", Social Networking: Springer International Publishing, pp. 201–213, 2014. Abstract
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Reham Gharbia, A. T. Azar, A. E. Baz, and A. E. Hassanien, "Image fusion techniques in remote sensing", arXiv preprint arXiv:1403.5473, 2014. Abstract
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Schaefer, G., Bartosz Krawczyk, E. M. Celebi, H. Iyatomi, and A. E. Hassanien, "Melanoma classification based on ensemble classification of dermoscopy image features", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 291–298, 2014. Abstract
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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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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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2013
Banerjee, S., N. Elbendary, A. E. Hassanien, and M. Tolba, "Decision Support System for Customer Churn Reduction Approach", 13th IEEE International Conference on Hybrid Intelligent Systems |(HIS13) Tunisia, 4-6 Dec. pp.229-234, 2013, Tunisia, , 4-6 Dec, 2013.
W. Ghonaim, N. I.Ghali, A. E. Hassanien, and S. Banerjee:, "An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack", Memetic Computing Springer, vol. 5, issue 3, pp. 179-185, 2013. Website
El-Bendary, N., Mohamed Mostafa M. Fouad, Rabie A. Ramadan, S. Banerjee, and A. E. Hassanien, "Smart Environmental Monitoring Using Wireless Sensor Networks.", Wireless Sensor Networks: Theory and Applications, pp. 733-755, , USA, CRC Press, Taylor and Francis Group, 2013. k15146_c025.pdf
Awad, A. I., A. E. Hassanien, and K. Baba, "Advances in Security of Information and Communication Networks", Communications in Computer and Information Science, vol. 381, 2013. Abstract
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Banerjee, S., N. El-Bendary, A. E. Hassanien, and M. F. Tolba, "Decision support system for customer churn reduction approach", Hybrid Intelligent Systems (HIS), 2013 13th International Conference on: IEEE, pp. 228–233, 2013. Abstract
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Ghonaim, W., N. I. Ghali, A. E. Hassanien, and S. Banerjee, "An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 179–185, 2013. Abstract
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Ghonaim, W., N. I. Ghali, A. E. Hassanien, and S. Banerjee, "An improvement of chaos-based hash function in cryptanalysis approach: An experience with chaotic neural networks and semi-collision attack", Memetic Computing, vol. 5, no. 3: Springer Berlin Heidelberg, pp. 179–185, 2013. 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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El-Bendary, N., M. M. M. Fouad, R. A. Ramadan, S. Banerjee, and A. E. Hassanien, "Smart environmental monitoring using wireless sensor networks", Wireless Sensor Networks: From Theory to Applications: CRC Press, pp. 731–754, 2013. Abstract
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El-Bendary, N., M. M. M. Fouad, R. A. Ramadan, S. Banerjee, and A. E. Hassanien, "Smart environmental monitoring using wireless sensor networks", Wireless Sensor Networks: From Theory to Applications: CRC Press, pp. 731–754, 2013. Abstract
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2012
Sami, M., N. El-Bendary, R. C. Berwick, and A. E. Hassanien, "Incorporating Random Forest Trees with Particle Swarm Optimization for Automatic Image Annotation", IEEE Federated Conference on Computer Science and Information Systems, pp. 791–797, Wroclaw - Poland, 9-13 Sept, 2012. Abstractincorporating_random_forest_trees_with.pdf

This paper presents an automatic image annotation approach that integrates the random forest classifier with particle swarm optimization algorithm for classes’ scores weighting.
The proposed hybrid approach refines the output of multiclass classification that is based on the usage of random forest 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. Images feature vectors are clustered into K clusters and a random forest classifier is trained for each cluster. Particle swarm optimization algorithm is employed as a search strategy to identify an optimal weighting for classes’ scores from random forest classifiers. 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 other approaches, considering annotation accuracy, for the
experimented dataset.

Banerjee, S., N. Ghali, and A. E. Hassanien, Investigating Optimization in Retail Inventory: A Bio-inspired Perspective towards Retail Recommender System, , 2012. Abstract

Interaction with different person leads to different kinds of ideas and sharing or some nourishing effects which might influence others to believe or trust or even join some association and subsequently become the member of that community. This will facilitate to enjoy all kinds of social privileges. These concepts of grouping similar objects can be experienced as well as could be implemented on any social networks. The concept of homophily
could assist to design the affiliation graph with similar and close similar entities of every member of any social network which tends identifying the most popular community. This paper propose and discuss a novel data-mining algorithm from the perspective of graph properties of a social network such as
embeddedness, betweenness and graph occupancy. Finally, the implication of homophily graph for cultivating leading community of social network has also been solicited.