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E., A. M. A., O. - H. E. - B. M. M., M. A. S. El-Kady, H. M. S. Hassan, and A. A. M. Abdalla, "Purificatio and production of ELISA reagents against Watermelon mosaic virus .", Egypt. J. Virol.,, vol. 2, pp. 101–111, 2005. Abstract
E., A., A. - M. S., H. A. I., and T. Y. M. 871, "Diffusion and transport of toluene through nano-particles and conventional metal oxide filled (EPDM)", International Journal of Applied Science and Engineering Research, vol. 4, issue 6, pp. 871-879, 2015.
E., H. M., M. S. Zaki, O. A. H., and M. Elshabrawy, "Bacteriological, histopathological and clinicopathological studies on respiratory affections in sheep and goats in Egypt", Egypt. Vet. Med. Assoc., vol. 63, pp. 97-109, 2003.
E., H. A., and 2Y. A. E. S. S., "Adverse Drug Reactions among Critically Ill Patients at Cairo University Hospital: Frequency and Outcomes", Journal of Biology, Agriculture and Healthcare, vol. Vol.3, No.13,, 2013.
E. A. MAHMOUD, M. A. HASSANIN, E. M. A. N. E. M. A. R. A. I. R., "Effect of organic and nitrogeneous fertilizers and plant density on yield and quality of sugar beet (Beta vulgaris L.).", Egyptian J. of agronomy, vol. 34, no. 1, pp. 89–103, 2012. Abstract
and E. A. Sobhy, A. Helmy, H. E. S. - S. S. K. E., "A 2.8 mW Sub-2 dB Noise Figure Inductorless Wideband CMOS LNA Using Double Capacitive Cross-Coupling and Positive Feedback", IEEE Trans. on Microwave Theory and Techniques, 2011.
E. Ayad, M. Mansy, D. Elwi, M.Salem, M. Salama, and K. Kayser, "Comparative Study between Quantitative Digital Image Analysis and Fluorescence In Situ Hybridization of Breast Cancer Equivocal HER2 2+ Cases”", J. Pathol Inform. 2015, vol. Jan, issue 3;6, pp. 31, 2015.
E. Emary, K. MOSTAFA, and H. Onsi, "A proposed multi-scale approach with automatic scale selection for image change detection", 16th IEEE International Conference on Image Processing (ICIP), Cairo, 7 November, 2009.
E. Emary, H. M. Zawbaa, C. Grosan, and A. E. Hassenian, "Feature Subset Selection Approach by Gray-Wolf Optimization", Afro-European Conference for Industrial Advancement(AECIA), Addis Ababa,, 2014.
E. Emary, H. M. Zawbaa, boul Ella Hassanien, M. F. Tolba, and V. Snasel, ""Retinal vessel segmentation based on flower pollination search algorithm"", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer), Ostrava, Czech Republic., 23-24 June, 2014. Abstractibica2014_p11.pdf

This paper presents an automated retinal blood vessels segmentation
approach based on flower pollination search algorithm (FPSA). The flower pollination
search is a new algorithm based on the flower pollination process of flowering
plants. The FPSA searches for the optimal clustering of the given retinal
image into compact clusters under some constrains. Shape features are used to
further enhance the clustering results using local search method. The proposed
retinal blood vessels approach is tested on a publicly available databases DRIVE
a of retinal images. The results demonstrate that the performance of the proposed
approach is comparable with state of the art techniques in terms of accuracy, sensitivity
and specificity.

E. Emary, H. M. Zawbaa, A. E. Hassanien, and B. PARV, " Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search, , ", Advances in Data Analysis and Classification, , issue (27 May 2016 on line), , pp. pp 1-17, 2017. AbstractWebsite

This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.

E. Emary, H. M. Zawbaa, A. E. Hassanien, and B. PARV, "Multi-Objective Retinal Vessel Localization Using Flower Pollination Search Algorithm With Pattern Search", Advances in Data Analysis and Classification, vol. doi:10.1007/s11634-016-0257-7, 2016.
E. Emary, Waleed Yamany, A. E. Hassanien, and V. Snasel, "Multi-Objective Gray-Wolf Optimization for Attribute Reduction", International Conference on Communications, management, and Information technology (ICCMIT'2015), 2015. Abstract

Feature sets are always dependent, redundant and noisy in almost all application domains. These problems in The data always declined the performance of any given classifier as it make it difficult for the training phase to converge effectively and it affect also the running time for classification at operation and training time. In this work a system for feature selection based on multi-objective gray wolf optimization is proposed. The existing methods for feature selection either depend on the data description; filter-based methods, or depend on the classifier used; wrapper approaches. These two main approaches lakes of good performance and data description in the same system. In this work gray wolf optimization; a swarm-based optimization method, was employed to search the space of features to find optimal feature subset that both achieve data description with minor redundancy and keeps classification performance. At the early stages of optimization gray wolf uses filter-based principles to find a set of solutions with minor redundancy described by mutual information. At later stages of optimization wrapper approach is employed guided by classifier performance to further enhance the obtained solutions towards better classification performance. The proposed method is assessed against different common searching methods such as particle swarm optimization and genetic algorithm and also was assessed against different single objective systems. The proposed system achieves an advance over other searching methods and over the other single objective methods by testing over different UCI data sets and achieve much robustness and stability.

E. Emary, H. M. Zawbaa, and A. E. Hassanien, "Binary ant lion approaches for feature selection", Neurocomputing, vol. 213, 2016. AbstractWebsite

In this paper, binary variants of the ant lion optimizer (ALO) are proposed and used to select the optimal feature subset for classification purposes in wrapper-mode. ALO is one of the recently bio-inspired optimization techniques that imitates the hunting process of ant lions. Moreover, ALO balances exploration and exploitation using a single operator that can adaptively searches the domain of solutions for the optimal solution. Binary variants introduced here are performed using two different approaches. The first approach takes only the inspiration of ALO operators and makes the corresponding binary operators. In the second approach, the native ALO is applied while its continuous steps are threshold using suitable threshold function after squashing them. The proposed approaches for binary ant lion optimizer (BALO) are utilized in the feature selection domain for finding feature subset that maximizing the classification performance while minimizing the number of selected features. The proposed binary algorithms were compared to three common optimization algorithms hired in this domain namely particle swarm optimizer (PSO), genetic algorithms (GAs), binary bat algorithm (BBA), as well as the native ALO. A set of assessment indicators is used to evaluate and compare the different methods over 21 data sets from the UCI repository. Results prove the capability of the proposed binary algorithms to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

E. Emary, Waleed Yamany, A. E. Hassanien, and V. Snasel, "Multi-Objective Gray-Wolf Optimization for Attribute Reduction", Journal of Procedia Computer Science, vol. 65, pp. 623–632, 2015.
and E. Emery, H. Onsi, M. A. K. S., "Refining Classification Accuracy of Satellite images Using Neuro Fuzzy System", the 38th annual conference on Statistics, Computer Science, and Operation Research, 2003. Abstract
E. H. Shaheen, K. M. Elsayed, A. S. H., "Effective Text Extraction from Video Scenes", Egyptian informatics Journal, vol. 8, pp. 100-117, 2007. Abstract
E. K. Soliman, Y. E. - A. Gendi, N. A. Afifi, and O. M. Mohamed, "Effect of prolonged administration of halofuginone and maduramicin on progesterone levels in Bovans laying hens", Egypt. J. Agric., vol. 82(4), pp. 42- 46, 2004.
E. K. Soliman, A. Y. E. - Gendi, and N. A. Afifi, "EffeEffect of prolonged administration of halofuginone and maduramicin on semen quality & serum testosterone levels in Bovans cockerels", Egypt. J. Agric., vol. vol. 82(4), pp. 64- 68, 2004.
E. Khalifa, S., R. A. Khairy, and R. Ramadan, "FGFR3, a marker suggestive of favorable prognosis in urothelial carcinoma", Comparative Clinical Pathology, 07, 2017. Abstract
E. M. Almetwally, M. A. H. Sabry, R. A. harby, D. Alnagar, and E. H. Hafez, "Marshall–Olkin Alpha Power Weibull Distribution: Different Methods of Estimation based on Type-I and Type-II Censoring", Hindawi-Complexity, , vol. 2021, issue Article ID 5533799, pp. 18 pages, 2021.
E.A, M., E. A.A., R. H. El-Gebaly, and A. A., "Study the change in the mosquito larvae (Culex pipiens) in water treated with short pulses electric filed", Electromagnetic Biology and Medicine, vol. 41, issue 1, pp. 80-92, 2022.
E.A.; El-Kashoury, M.A., A. M.; El- Fishawy, and F. M. Soliman, "Alkaloids, Coumarins and Lignans of the genus Haplophyllum ", Bull. Fac. Pharm., Cairo Univ., vol. 29, pp. 1, 1991.