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Mohammed, A., and R. Kora, "Deep Learning approaches for Arabic Sentiment Analysis", Springer journal: Social Network Analysis and Mining, vol. 9(52), issue 1869-5469, pp. https://doi.org/10.1007/s13278-019-0596-4, 2019.
Shawky, D., "A Deep Learning Approach to Software Clone Detection", International Conference on Recent Advances in Engineering and Technology (ICRAET)., Dubai, December 26, 2018.
Hamed, H., A. M. Hafez, and A. Mohammed, "Deep learning approach for Translating Arabic HolyQuran into Italian language", International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC 2021)2.In press ( IEEE Xplorer), 26 May, 2021.
Saeed, B., M. E. - E. Deen, and A. Mohammed, "A deep Learning Approach for Text Generation", The 53rd annual Conference on Statistics, Computer Sciences and Operation Research, Cairo University, 5 Dec, 2018.
Salem, M., and N. Tsurusaki, "Deep Learning approach for Modeling Land Use/Land Cover Change Using Remote Sensing Techniques", International Symposium on Earth Science and Technology 2021, Fukuoka, Japan, pp. 250-253, 2021.
Alabrak, M., M. Megahed, A. Mohammed, H. Elfandy, N. Tahoun, and Hoda Ismail, "Deep Learning Approach for Classifying Thyroid Nodules", Modern Pathology 2022, Los Angeles, California, Springer-Nature, March19-24, 2022.
Mohammed, A., and et al, "Deep Learning Approach for Breast CancerDiagnosis from Microscopy Biopsy Images", 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), Cairo, Egypt, pp. 216-222, 26 May, 2021.
M. Gobashy, U. Casten, and F. M. Neubauer, "Deep lateral remote sensing using borehole gravimetry in the KTB wel", the second international conference on the geology of Africa, (Geological society of Africa/ Assiut university),, Asuite, egypt, 28-30 October, 2001.
Taha, M. H. N., M. H. N. Taha, A. E. Hassanien, and H. N. E. T. Mohamed, "Deep Iris: Deep Learning for Gender Classification Through Iris Patterns.", Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, vol. 27, issue 2, pp. 96-102, 2019. Abstract

Introduction: One attractive research area in the computer science field is soft biometrics.

Aim: To Identify a person's gender from an iris image when such identification is related to security surveillance systems and forensics applications.

Methods: In this paper, a robust iris gender-identification method based on a deep convolutional neural network is introduced. The proposed architecture segments the iris from a background image using the graph-cut segmentation technique. The proposed model contains 16 subsequent layers; three are convolutional layers for feature extraction with different convolution window sizes, followed by three fully connected layers for classification.

Results: The original dataset consists of 3,000 images, 1,500 images for men and 1,500 images for women. The augmentation techniques adopted in this research overcome the overfitting problem and make the proposed architecture more robust and immune from simply memorizing the training data. In addition, the augmentation process not only increased the number of dataset images to 9,000 images for the training phase, 3,000 images for the testing phase and 3,000 images for the verification phase but also led to a significant improvement in testing accuracy, where the proposed architecture achieved 98.88%. A comparison is presented in which the testing accuracy of the proposed approach was compared with the testing accuracy of other related works using the same dataset.

Conclusion: The proposed architecture outperformed the other related works in terms of testing accuracy.

Khalifa, N. E. M., M. H. N. Taha, A. E. Hassanien, and H. N. E. T. Mohamed, "Deep Iris: Deep Learning for Gender Classification Through Iris Patterns", Acta Informatica Medica, vol. 27, issue 2, pp. 96-102, 2019. AbstractWebsite

Introduction: One attractive research area in the computer science field is soft biometrics. Aim: To
Identify a person’s gender from an iris image when such identification is related to security surveillance
systems and forensics applications. Methods: In this paper, a robust iris gender-identification method
based on a deep convolutional neural network is introduced. The proposed architecture segments
the iris from a background image using the graph-cut segmentation technique. The proposed model
contains 16 subsequent layers; three are convolutional layers for feature extraction with different
convolution window sizes, followed by three fully connected layers for classification. Results: The
original dataset consists of 3,000 images, 1,500 images for men and 1,500 images for women. The
augmentation techniques adopted in this research overcome the overfitting problem and make the
proposed architecture more robust and immune from simply memorizing the training data. In addition,
the augmentation process not only increased the number of dataset images to 9,000 images for the
training phase, 3,000 images for the testing phase and 3,000 images for the verification phase but
also led to a significant improvement in testing accuracy, where the proposed architecture achieved
98.88%. A comparison is presented in which the testing accuracy of the proposed approach was
compared with the testing accuracy of other related works using the same dataset. Conclusion: The
proposed architecture outperformed the other related works in terms of testing accuracy.

Alkhatip, A. A. A. M. M., M. G. Kamel, E. M. Farag, M. Elayashy, A. Farag, H. M. Yassin, M. H. Bahr, M. Abdelhaq, A. Sallam, A. M. Kamal, et al., "Deep Hypothermic Circulatory Arrest in the Pediatric Population Undergoing Cardiac Surgery With Electroencephalography Monitoring: A Systematic Review and Meta-Analysis.", Journal of cardiothoracic and vascular anesthesia, 2021. Abstract

OBJECTIVE: Cardiac surgery for repair of congenital heart defects poses unique hazards to the developing brain. Deep hypothermic circulatory arrest (DHCA) is a simple and effective method for facilitating a bloodless surgical field during congenital heart defect repair. There are, however, some concerns that prolonged DHCA increases the risk of nervous system injury. The electroencephalogram (EEG) is used in adult and, to a lesser extent, pediatric cardiac procedures as a neuromonitoring method. The present study was performed to assess outcomes following DHCA with EEG monitoring in the pediatric population.

DESIGN: In this systematic review and meta-analysis, the PubMed, Cochrane Central Register of Controlled Trials, Scopus, Institute of Science Index, and Embase databases were searched from inception for relevant articles. A fixed- or random-effects model, as appropriate, was used.

SETTING: Surgical setting.

PARTICIPANTS: Pediatric population (≤18 y old).

INTERVENTIONS: DHCA (18°C) with EEG monitoring.

MEASUREMENTS AND MAIN RESULTS: Nineteen articles with 1,267 pediatric patients ≤18 years were included. The event rate of clinical and EEG seizures among patients who underwent DHCA was 12.9% and 14.9%, respectively. Mortality was found to have a 6.3% prevalence. A longer duration of DHCA was associated with a higher risk of EEG seizure and neurologic abnormalities. In addition, seizures were associated with increased neurologic abnormalities and neurodevelopmental delay.

CONCLUSIONS: EEG and neurologic abnormalities were common after DHCA. A longer duration of DHCA was found to lead to more EEG seizure and neurologic abnormalities. Moreover, EEG seizures were more common than clinical seizures. Seizures were found to be associated with increased neurologic abnormalities and neurodevelopmental delay.

Naiem, A., and M. El-Beltagy, "Deep greedy switching: A fast and simple approach for linear assignment problems", 7th International Conference of Numerical Analysis and Applied Mathematics, 2009. Abstract
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Khalifa, N. E. M., M. H. N. Taha, A. E. Hassanien, and I. M. Selim, "Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks", arXiv preprint arXiv:1709.02245, 2017. Abstract
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Khalifa, N. E., M. H. Taha, A. E. Hassanien, and I. Selim, "Deep Galaxy V2: Robust Deep Convolutional Neural Networks for Galaxy Morphology Classifications", The IEEE International Conference on Computing Sciences and Engineering (ICCSE), Kuwait City, Kuwait, pp. 122-127, 2018. Abstractfinal_ieee.10.1109iccse1.2018.8374210.pdf

This paper is an extended version of "Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks". In this paper, a robust deep convolutional neural network architecture for galaxy morphology classification is presented. A galaxy can be classified based on its features into one of three categories (Elliptical, Spiral, or Irregular) according to the Hubble galaxy morphology classification from 1926. The proposed convolutional neural network architecture consists of 8 layers, including one main convolutional layer for feature ex-traction with 96 filters and two principle fully connected layers for classification. The architecture is trained over 4238 images and achieved a 97.772% testing accuracy. In this version, "Deep Galaxy V2", an augmentation process is applied to the training data to overcome the overfitting problem and make the proposed architecture more robust and immune to memorizing the training data. A comparative result is present, and the testing accuracy was compared with those of other related works. The proposed architecture outperformed the other related works in terms of its testing accuracy.

Taha, M. H. N., M. H. N. Taha, A. E. Hassanien, and I. Selim, "Deep Galaxy V2: Robust Deep Convolutional Neural Networks for Galaxy Morphology Classifications", The IEEE International Conference on Computing Sciences and Engineering (ICCSE), Kuwait City, Kuwait, 122-127, 2018.
Wifi, A. S., A. H. Gomaa, and R. K. Abdel-Magied, "Deep drawing process design and capp system for box-shaped parts", Proceedings of the Tehran International “Congress on Manufacturing Engineering” TICME, pp. 1–9, 2005. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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Salama, M. A., A. E. Hassanien, and A. A. Fahmy, "Deep belief network for clustering and classification of a continuous data", Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on: IEEE, pp. 473–477, 2010. Abstract
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Khalifa, N. E. M., M. H. N. Taha, A. E. Hassanien, and A. A. Hemedan, "Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset", International Journal of Reasoning-based Intelligent Systems, vol. 11, issue 3, pp. 256-264, 2019.
Iida, K., K. Oyamatsu, and B. Abu-Ibrahim, "Deducing the Density Dependence of the Symmetry Energy from Unstable Nuclei ", Progress of Theoretical Physics Supplement, vol. 156, pp. 139, 2004. AbstractWebsite

We explore a possible method to deduce from unstable nuclei the parameter L characterizing the density dependence of the symmetry energy near normal nuclear density.