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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.

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.
2019
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.
2018
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.

2017
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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2015
Taha, M. H. N., H. E. N. mahdy, N. E. - D. M. Khalifa, M. H. N. Taha, and M. A. Lotfi, "Pediatric Online Evidence-Based Medicine Assignment Is a Novel Effective Enjoyable Undergraduate Medical Teaching Tool: A SQUIRE Compliant Study", Medicine (Baltimore) Journal, vol. 94, issue 29, pp. 1178 - 1185, 2015. AbstractPediatric Online Evidence-Based MedicineWebsite

Abstract: Evidence-based medicine (EBM) is delivered through a didactic, blended learning, and mixed models. Students are supposed to construct an answerable question in PICO (patient, intervention, comparison, and outcome) framework, acquire evidence through search of literature, appraise evidence, apply it to the clinical case scenario, and assess the evidence in relation to clinical context.

Yet these teaching models have limitations especially those related to group work, for example, handling uncooperative students, students who fail to contribute, students who domineer, students who have personal conflict, their impact upon progress of their groups, and inconsistent individual acquisition of required skills.

At Pediatrics Department, Faculty of Medicine, Cairo University, we designed a novel undergraduate pediatric EBM assignment online system to overcome shortcomings of previous didactic method and aimed to assess its effectiveness by prospective follow-up during academic years 2012 to 2013 and 2013 to 2014.

The novel web-based online interactive system was tailored to provide sequential single and group assignments for each student. Single assignment addressed a specific case scenario question, while group assignment was teamwork that addressed different questions of same case scenario. Assignment comprised scholar content and skills.

We objectively analyzed students’ performance by criterion-based assessment and subjectively by anonymous student questionnaire.

A total of 2879 were enrolled in 5th year Pediatrics Course consecutively, of them 2779 (96.5%) logged in and 2554 (88.7%) submitted their work. They were randomly assigned to 292 groups. A total of 2277 (89.15%) achieved ≥80% of total mark (4/5), of them 717 (28.1%) achieved a full mark. A total of 2178 (85.27%) and 2359 (92.36%) made evidence-based conclusions and recommendations in single and group assignment, respectively (P < 0.001). A total of 1102 (43.1%) answered student questionnaire, of them 898 (81.48%) found e-educational experience satisfactory, 175 (15.88%) disagreed, and 29 (2.6%) could not decide. A total of 964 (87.47%) found single assignment educational, 913 (82.84%) found group assignment educational, and 794 (72.3%) enjoyed it.

Web-based online interactive undergraduate EBM assignment was found effective in teaching medical students and assured individual student acquisition of concepts and skills of pediatric EMB. It was effective in mass education, data collection, and storage essential for system and student assessment.

2013
Taha, M. H. N., Improving QoS of Data Transmission over Wireless Sensor Networks, , Cairo, Cairo University, 2013. Abstractmnasrtaha_phd_thesis

Wireless Sensor Networks (WSNs) consist of sensor nodes that are spatially distributed. These sensor nodes are connected to each other through wireless technology. They are an important emerging technology that will revolutionize sensing for a wide range of military, scientific, industrial and civilian applications. In many WSN applications, the sensor nodes are deployed in an ad hoc style without careful any pre-planning and engineering. Once deployed, the sensor nodes must have the ability to autonomously organize themselves into a wireless communication network.

New packet scheduling schemes have been developed for real-time data communication. These schemes work on prioritizing packets according to their deadlines. Packet prioritizing cannot support real time applications or assure network lifetime. In extreme traffic environments, large queues may lead to packet delay and packet dropping. Packet dropping leads to energy loss, as a packet could have consumed high energy in order to be delivered to its destination.

The continuous decrease in the size and cost of sensors has motivated intensive research addressing the potential of collaboration among sensors in data. Current research on routing and scheduling in wireless sensor networks focused on energy aware protocols to maximize the lifetime of the network. These researches are scalable to accommodate a large number of sensor nodes. In addition, they are tolerant to sensor damage and battery exhaustion. Sensor networks are deployed to gather information for later analysis, monitoring or tracking of phenomena in real-time.
In WSNs, transmitted packets are queued at intermediate nodes. Each node schedules the queued packets by assigning priorities to each packet. Priorities are assigned to packets according to their deadlines. This method in packet prioritization does not take into consideration either the network life time or energy consumption. Besides, it may lead to dropping high energy valuable packets. In many applications, WSN lifetime is considered a very critical issue, while setting up the network.

In this thesis, a new scheduling scheme, named Energy Based Scheduling scheme is introduced. In this scheme, packets are not only prioritized according to their deadlines but also according to some energy measures related to the network, that are obtained from the network nodes and are used in packet prioritization. The proposed scheme is integrated with the AODV routing protocol. The unused bits in the AODV packets are used by the proposed scheme in assigning sending priorities to each packet in the network. Through this thesis, the proposed scheduling scheme is compared with the Basic Priority Scheduling scheme, using NS-2. Comparisons are done according the network life time, energy consumption and the fairness index measure. The results prove that the Energy Based scheduling scheme increase the network life time and decrease the energy consumption for the goodput packets. On the other hand, the fairness index was affected.

Khalifa, N. E. M., M. H. N. Hamed, H. E. N. mahdy, and I. A. Tharwat, "A Secure Energy Efficient Schema for Wireless Multimedia Sensor Networks", CiiT International Journal of Wireless Communication, vol. 5, issue 6, pp. 235–246, 2013.
2012
Taha, M. H. N., N. E. Mahmoud, H. N. Elmahdy, and I. A. Saroit, "Energy Based Scheduling Scheme for Wireless Sensor Networks", CiiT International Journal of Wireless Communication, vol. 4, issue 16, pp. 973-975, 2012. AbstractCU-PDF

Wireless sensor networks (WSNs) have become an attracted research and industry interest. In WSN, each node is attached to a battery, which supply the node with energy required for data sensing, processing and transmission. Transmitted packets are queued at intermediate nodes. Each node schedules the queued packets by assigning priorities to each packet. Priorities are assigned to packets according to their deadlines. This method in packet prioritization does not take into consideration either the network life time or energy consumption. Besides, it may lead to dropping high energy valuable packets. In many applications, WSN lifetime is considered a very critical issue, while setting up the network. In this paper, we paper we introduce new scheduling schema, called Energy Based Scheduling schema. In this schema, packets are not only prioritized according to their deadlines but also to some energy measures related to the network. These energy measures are obtained from the network nodes and are used in packet prioritization. The proposed schema is integrated with the AODV routing protocol. The unused bits in the AODV packets are used by the proposed schema in assigning sending priorities to each packet in the network. Through this paper, we will compare the proposed scheduling schema against the Basic Priority Scheduling schema, using NS-2. Comparisons are done according the network life time and energy consumption.

Mahmoud, N. E., M. H. N. Taha, H. N. Elmahdy, and I. A. Saroit, "A Secure Energy Mechanism for WSN and Its Implementation in NS-2", CiiT International Journal of Wireless Communication, vol. 4, issue 16, pp. 984–990, 2012. Abstract

Wireless sensor networks (WSNs) are usually deployed for gathering data from unattended or hostile environments. Therefore, securing data transmission across these environments is a must. Due to the fact that the sensors have a limited power, any security mechanism for sensor network must be energy efficient. In this paper, a secure energy efficient mechanism is introduced with a proposed scenario which leads to a significant improvement in network energy consumption. The mechanism constructs its security features in the application and transport layer as the information that the attackers seek ultimately resides within these layers. We modified the packet format for WSN. Data payload was encrypted by Advanced Encryption Standard (AES) and Message Authentication Code (MAC) was generated to assure data confidentiality and integrity. The energy consumption metric has been taken into considerations while designing and testing the mechanism to make it energy efficient as much as possible. The energy efficiency was achieved by giving a higher priority to the secured packet over the normal packet in the Interface Queue (IFQ). Through this paper, a detailed structure of the proposed mechanism is introduced and implemented using Network Simulator-2 (NS-2). This is the first research that implements security algorithms within NS-2. Since NS-2 does not support any security features before, this research will be a good start to begin using NS-2 as a security simulator.

2009
Elmahdy, H. N., and M. H. N. Taha, "The Impact of Packet Size and Packet Dropping Probability on Bit Loss of VoIP Networks", ICGST-CNIR Journal, vol. 8, issue 2, no. 2, pp. 25–29, 2009. Abstractmnasrtaha

The demand for voice over IP (VoIP) applications has increased tremendously through the last two decades. This great demand leads to a great increase in the Quality of Service (QoS) researches and other related fields. One of these fields is the Differentiated Services (DiffServ). In this paper, we studied the effect of the packet size and the effect of random early detection (RED) parameters on the Two Rate Three Color Marker (trTCM) and Single Rate Three Color Marker (srTCM). This is done via a computer simulation using a network simulator (NS-2). Through this paper, we introduce a new simulation model. We will try through this model to find the most suitable parameters such as dropping probability and packet size, in order to achieve better fairness and better goodput. Beside that, we will introduce the standard deviation (SD) as another fairness measuring technique

Tourism