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Zaki, S. A., H. Zhu, M. A. Fakih, A. R. Sayed, and J. Yao, "Deep‐learning–based method for faults classification of PV system", IET Renewable Power Generation, vol. 15, issue 1, pp. 193-205, 2021. Abstract
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Abdalla, M., A. Hendawi, H. M. O. Mokhtar, N. ElGamal, J. Krumm, and M. Ali, "DeepMotions: A Deep Learning System For Path Prediction Using Similar Motions.", IEEE Access, vol. 8, pp. 23881 - 23894, 2020/01/15. Abstract

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Aziz, M., P. Ferrandis, A. Mesli, R. H. Mari, J. F. Felix, A. Sellai, D. Jameel, N. Al Saqri, A. Khatab, D. Taylor, et al., "Deep-level transient spectroscopy of interfacial states in “buffer-free” pin GaSb/GaAs devices", Journal of Applied Physics, vol. 114, no. 13: AIP Publishing, pp. 134507, 2013. Abstract
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Shafi, M., R. H. Mari, A. Khatab, D. Taylor, and M. Henini, "Deep-level Transient Spectroscopy of GaAs/AlGaAs Multi-Quantum Wells Grown on (100) and (311) B GaAs Substrates", Nanoscale research letters, vol. 5, no. 12: Springer, pp. 1948–1951, 2010. Abstract
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Shafi, M., R. H. Mari, A. Khatab, D. Taylor, and M. Henini, "Deep-level Transient Spectroscopy of GaAs/AlGaAs Multi-Quantum Wells Grown on (100) and (311) B GaAs Substrates", Nanoscale research letters, vol. 5, no. 12: Springer, pp. 1948–1951, 2010. Abstract
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Taha, M. H. N., M. Loey, M. H. N. Taha, and H. N. E. T. Mohamed, "Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection.", Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, vol. 27, issue 5, pp. 327-332, 2019. Abstract6-1579457250.pdf

Introduction: Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic patients will increase to 552 million by 2034, as per the International Diabetes Federation (IDF).

Aim: With advances in computer science techniques, such as artificial intelligence (AI) and deep learning (DL), opportunities for the detection of DR at the early stages have increased. This increase means that the chances of recovery will increase and the possibility of vision loss in patients will be reduced in the future.

Methods: In this paper, deep transfer learning models for medical DR detection were investigated. The DL models were trained and tested over the Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset. According to literature surveys, this research is considered one the first studies to use of the APTOS 2019 dataset, as it was freshly published in the second quarter of 2019. The selected deep transfer models in this research were AlexNet, Res-Net18, SqueezeNet, GoogleNet, VGG16, and VGG19. These models were selected, as they consist of a small number of layers when compared to larger models, such as DenseNet and InceptionResNet. Data augmentation techniques were used to render the models more robust and to overcome the overfitting problem.

Results: The testing accuracy and performance metrics, such as the precision, recall, and F1 score, were calculated to prove the robustness of the selected models. The AlexNet model achieved the highest testing accuracy at 97.9%. In addition, the achieved performance metrics strengthened our achieved results. Moreover, AlexNet has a minimum number of layers, which decreases the training time and the computational complexity.

Loey, M., G. Manogaran, and N. E. M. Khalifa, "A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images.", Neural computing & applications, pp. 1-13, 2020. Abstract

The Coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with Conditional Generative Adversarial Nets (CGAN) based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for COVID-19 especially in chest CT images are the main motivation of this research. The main idea is to collect all the possible images for COVID-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the Coronavirus-infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%, sensitivity 77.66%, and specificity of 87.62%.

Nasr, M. A., and M. A. Swillam, "Deep subwavelength focusing using optical nanoantenna with enhanced characteristics for near and far field applications", SPIE Photonics West(OPTO), San Francisco, California, United States, February, 2013.
Badr, Y. A., I. M. Taher, M. M. Bahgat, and D. F. Ghoneim, "Deep sclerectomy using Erbium: YAG laser in pig's eyes", Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 5, no. 3, pp. 253-261, 2004. Abstract
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Teeri, T. H., H. Bashandy, and S. Martens, "Deep RNA sequencing and flavonoid metabolons in Gerbera hybrida", SPPS Congress, Helsingør, Denmark, 2013.
Duhayyim, M. A., T. A. E. Eisa, F. N. Al-Wesabi, A. Abdelmaboud, M. A. Hamza, A. S. Zamani, M. Rizwanullah, and R. Marzouk, "Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification", Computers, Materials & Continua, vol. 71, issue 3, pp. .5699-5715, 2022.
Mohammed, A., and et al, "Deep Reinforcement Learning Approach for Augmented Reality Games", International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC 2021). In press ( IEEE Xplorer), 26 May, 2021.
Leheta, T. M., R. M. Abdel Hay, and Y. F. El Garem, "Deep peeling using phenol versus percutaneous collagen induction combined with trichloroacetic acid 20% in atrophic post-acne scars; a randomized controlled trial", Journal of Dermatological Treatment, vol. 25, no. 2: Informa Healthcare USA on behalf of Informa UK Ltd. London, pp. 130–136, 2014. Abstract

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Leheta, T. M., R. M. A. B. D. E. L. HAY, and Y. F. El Garem, "Deep peeling using phenol versus percutaneous collagen induction combined with trichloroacetic acid 20% in atrophic post-acne scars; a randomized controlled trial", Journal of Dermatological Treatment, vol. 25, no. 2: Taylor & Francis, pp. 130–136, 2014. Abstract
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El-Dawlatly, M. M., M. S. M. Fayed, and Y. A. Mostafa, Deep overbite malocclusion: Analysis of the underlying components., , 2012.
, "The deep lymphatic system of the lower limb in filarial lymphoedema", The Egyptian Journal of Surgery, vol. 4, pp. 11-15, 1985.
Shafi, M., R. H. Mari, A. Khatab, M. Henini, A. Polimeni, M. Capizzi, and M. Hopkinson, "Deep levels in H-irradiated GaAs1-xNx (x< 0.01) grown by molecular beam epitaxy", Journal of Applied Physics, vol. 110, no. 12: AIP Publishing, pp. 124508, 2011. Abstract
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Shafi, M., R. H. Mari, A. Khatab, M. Henini, A. Polimeni, M. Capizzi, and M. Hopkinson, "Deep levels in H-irradiated GaAs1-xNx (x< 0.01) grown by molecular beam epitaxy", Journal of Applied Physics, vol. 110, no. 12: AIP Publishing, pp. 124508, 2011. Abstract
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Marzouk, M., N. Elshaboury, A. Abdel-Latif, and S. Azab, "Deep Learning Model for Forecasting COVID-19 Outbreak in Egypt", Process Safety and Environmental Protection, vol. 153, pp. 363-375, 2021.
Abd El-Aziz, A. A., N. A. Azim, M. A. Mahmood, and H. Alshammari, "A Deep Learning Model for Face Mask Detection", IJCSNS International Journal of Computer Science and Network Security, vol. 21, issue 10, pp. 101--106, 2021.
El-Aziz, A. A. A., N. A. Azim, M. A. Mahmood, and H. Alshammari, "A Deep Learning Model for Face Mask Detection ", IJCSNS, vol. 21, issue 10, pp. 101-106, 2021.
Gabr, R. H., A. I. Shahin, A. A. Sharawi, and M. A. AOUF, "A DEEP LEARNING IDENTIFICATION SYSTEM FOR DIFFERENT EPILEPTIC SEIZURE DISEASE STAGES", JOURNAL OF ENGINEERING AND APPLIED SCIENCE, vol. 67, issue 4, pp. 925-944, 2020.
Rashwan, M. A. A., A. A. A. Sallab, H. M. Raafat, and A. Rafea, "Deep learning framework with confused sub-set resolution architecture for automatic Arabic diacritization", IEEE Transactions on Audio, Speech, and Language Processing, vol. 23, issue 2329-9290, pp. 505-516, 2015. deep_learning_framework_with_confused_sub-set.pdf
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