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Taha, M. H. N., A. A. Mawgoud, A. Abu‑Talleb, M. H. N. Taha, and Y. ‑D. Zhang, "A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports", International Journal of Computational Intelligence Systems, vol. 16, issue 96, pp. 1-11, 2023.
Taha, M. H. N., N. E. M. Khalifa, M. Loey, and M. H. N. Taha, Cyber-Physical Systems for Industrial Transformation: Fundamentals, Standards, and Protocols, : CRC Press, 2023.
Taha, M. H. N., M. H. N. Taha, A. E. Hassanien, and S. Elghamrawy, "Detection of coronavirus (COVID-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest X-ray dataset", Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022 , pp. 234–247, 2023.
Taha, M. H. N., M. H. N. Taha, and N. E. Khalifa, "A Linear Programming Methodology to Optimize Decision-Making for Ready-Mixed Cement Products: a Case Study on Egypt’s New Administrative Capital", Process Integration and Optimization for Sustainability, vol. 7, pp. 177–190, 2023.
Taha, M. H. N., N. E. M. Khalifa, M. H. N. Taha, and A. M. I. R. A. KOTB, "Optimization of Task Scheduling in Cloud Computing Using the RAO-3 Algorithm", The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies: 164, 2023.
Taha, M. H. N., M. H. N. Taha, and N. E. M. Khalifa, "Blockchain Technology and Machine Learning for Fake News Detection", Implementing and Leveraging Blockchain Programming, Singapore, Springer, 2022.
Taha, M. H. N., M. H. N. Taha, A. Abu‑Talleb, and A. M. I. R. A. KOTB, "A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system", Journal of Cloud Computing, vol. 11, issue 97, 2022.
Taha, M. H. N., G. Manogaran, M. H. N. Taha, and M. Loey, "A deep learning semantic segmentation architecture for COVID‐19 lesions discovery in limited chest CT datasets", Expert Systems, vol. 39, issue 6, 2022.
Taha, M. H. N., M. Loey, R. K. Chakrabortty, and M. H. N. Taha, "Within the Protection of COVID-19 Spreading: A Face Mask Detection Model Based on the Neutrosophic RGB with Deep Transfer Learning.", Neutrosophic Sets and Systems, vol. 50, pp. 320-335, 2022.
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, and N. E. M. Khalifa, Enabling AI Applications in Data Science, : Springer Cham, 2021.
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. 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, 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.
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.
Khalifa, N. E. M., M. H. N. Taha, and A. E. Hassanien, "Aquarium Family Fish Species Identification System Using Deep Neural Networks", Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, Cham, Springer International Publishing, pp. 347–356, 2019. Abstract

In this paper, a system for aquarium family fish species identification is proposed. It identifies eight family fish species along with 191 sub-species. The proposed system is built using deep convolutional neural networks (CNN). It consists of four layers, two convolutional and two fully connected layers. A comparative result is presented against other CNN architectures such as AlexNet and VggNet according to four parameters (number of convolution and fully connected layers, the number of epochs in training phase to achieve 100{%} accuracy, validation accuracy, and testing accuracy). Through the paper, it is proven that the proposed system has competitive results against the other architectures. It achieved 85.59{%} testing accuracy while AlexNet achieves 85.41{%} over untrained benchmark dataset. Moreover, the proposed system has less trained images, less memory, less computational complexity in training, validation, and testing phases.

Khalifa, N. E. M., M. H. N. Taha, and A. E. Hassanien, "Automatic Counting and Visual Multi-tracking System for Human Sperm in Microscopic Video Frames", Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018, Cham, Springer International Publishing, pp. 525–531, 2019. Abstract

In this paper, a proposed system for automatic counting and visual multi-tracking for human sperm in microscopic video frames is presented. It can be easily turned into a commercial computer-assisted sperm analysis (CASA) system. CASA systems help in detecting infertility in human sperm according to clinical parameters. The proposed system consists of nine phases and it counts sperm in every single frame of video in real time and calculates the average sperm count through the whole video with accuracy 94.3{%} if it is compared to the manual counting. Also, it tracks all identified sperm in video frames in real time. It works with different frame rates above 15 frame/s to track visually the movements of the sperm. The dataset consists of three high-quality 1080p videos with different frame rates and durations. Finally, the open challenging research points are addressed.

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.

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, and N. E. M. Khalifa, "Towards Objective-Dependent Performance Analysis on Online Sentiment Review", Machine Learning Paradigms: Theory and Application, Cham, Springer International Publishing, 2019.
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.
El-Din, D. M., M. H. N. Taha, and N. E. M. Khalifa, "A Blockchain Technology Evolution Between Business Process Management (BPM) and Internet-of-Things (IoT)", International Journal of Advanced Computer Science and Applications(IJACSA), vol. 9, issue 9, 2018. Abstractpaper_56-a_blockchain_technology_evolution_1.pdf

A Blockchain is considered the main mechanism for Bitcoin concurrency. A Blockchain is known by a public ledger and public transactions stored in a chain. The properties of blockchain demonstrate in decentralization as distribution blocks, stability, anonymity, and auditing. Blockchain can enhance the results of network efficiency and improve the security of network. It also can be applied in several fields like financial and banking services, healthcare systems, and public services. However, the research is still opening at this point. It includes a big number of technical challenges which prevents the wide application of blockchain, for example, scalability problem, privacy leakage, etc. This paper shows a proposed comprehensive study of blockchain technology. It also examines the research efforts in blockchain. It presents a proposed blockchain lifecycle which refers to an evolution and a linked ring between business process management improvement and Internet-of-Things concepts. Then, this paper presents a practical proof of this relationship for smart city. It presents a new algorithm and a proposed blockchain framework for 38 blocks (which recognized as smart-houses). Finally, the future directions are well presented in blockchain field.

Mohamed, D. M. E. - D., and M. H. N. El-din, "Performance Analysis for Sentiment Techniques Evaluation Perspectives", Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017, Cham, Springer International Publishing, pp. 448–457, 2018. Abstract

This paper presents proposed performance criteria evaluation based on a comparison between sentiment techniques. The target is measuring the sentiments performance through several significant perspectives in sentiment analysis. This measurement is very tight of accuracy evaluating for sentiments. However, evaluating sentiments is a hard challenge for language technologies, and achieving good results is much more difficult than some human think. Also, we introduce a comprehensive study for different sentiment techniques based on proposed performance criteria. The performance evaluation plays a vital role in accuracy measurement through a sentiment analysis word level. The performance criteria include two types of performance measurement namely F-measure and Runtime. These criteria include the balance of performance perspectives priorities. These types include a relationship between perspectives of performance to improve it. There are different performance perspectives: F-measure and speed of run time, memorability, and sentiment analysis challenges. It helps in understanding the contextual meaning and getting a score in less time and higher accuracy. The comparisons are based on the sentiment analysis word-level. They can understand some phrases as do not directly through caring with the classification of reviews. Finally, we show the efficiency and effectiveness of the proposed criteria.

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