Taha, M. H. N., M. H. N. Taha, M. R. Mouhamed, and A. E. Hassanien, "Robust Deep Transfer Models for Fruit and Vegetable Classification: A Step Towards a Sustainable Dietary", Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications: Springer Cham, 2021.
Taha, M. H. N., M. H. N. Taha, and N. E. M. Khalifa, Enabling AI Applications in Data Science, : Springer Cham, 2021.
Taha, M. H. N., M. H. N. Taha, L. A. M. El-Maged, and A. E. Hassanien, "Artificial Intelligence in Potato Leaf Disease Classification: A Deep Learning Approach", Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges : Springer, Cham, 2021.
Taha, M. H. N., M. H. N. Taha, A. E. Hassanien, and S. H. N. Taha, "The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach", Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Studies in Big Data: Springer, Cham, 2020.
Taha, M. H. N., R. Bhatnagar, N. E. M. Khalifa, and M. H. N. Taha, Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, : Springer, Cham, 2020.
Taha, M. H. N., G. Manogaran, M. H. N. Taha, and N. E. M.Khalifa, "A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic", Measurement, vol. 167, 2021. Abstract

The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy.

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.

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.

Hassanien, A. E., R. Bhatnagar, N. E. M. Khalifa, and M. H. N. Taha, Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, , Cham, Springer, 2020.
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