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Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.

Cancer is one of the most feared and aggressive diseases in the world and is responsible for more than 9 million deaths universally. Staging cancer early increases the chances of recovery. One staging technique is RNA sequence analysis. Recent advances in the efficiency and accuracy of artificial intelligence techniques and optimization algorithms have facilitated the analysis of human genomics. This paper introduces a novel optimized deep learning approach based on binary particle swarm optimization with decision tree (BPSO-DT) and convolutional neural network (CNN) to classify different types of cancer based on tumor RNA sequence (RNA-Seq) gene expression data. The cancer types that will be investigated in this research are kidney renal clear cell carcinoma (KIRC), breast invasive carcinoma (BRCA), lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). The proposed approach consists of three phases. The first phase is preprocessing, which at first optimize the high-dimensional RNA-seq to select only optimal features using BPSO-DT and then, converts the optimized RNA-Seq to 2D images. The second phase is augmentation, which increases the original dataset of 2086 samples to be 5 times larger. The selection of the augmentations techniques was based achieving the least impact on manipulating the features of the images. This phase helps to overcome the overfitting problem and trains the model to achieve better accuracy. The third phase is deep CNN architecture. In this phase, an architecture of two main convolutional layers for featured extraction and two fully connected layers is introduced to classify the 5 different types of cancer according to the availability of images on the dataset. The results and the performance metrics such as recall, precision and F1 score show that the proposed approach achieved an overall testing accuracy of 96.90%. The comparative results are introduced, and the proposed method outperforms those in related works in terms of testing accuracy for 5 classes of cancer. Moreover, the proposed approach is less complex and consume less memory.

Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.

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.

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.

Coronavirus COVID-19 is a global pandemic stated by the World Health Organization (WHO) in 2020. The COVID-19 devastating impact was not only affect human life but also many aspects of it such as social interaction, transportation options, personal saving and expenses, and more. The power of social media data in such world pandemic outbreaks provides an efficient source of tracking, raising awareness, and alerts with potentials infection location. Social networks can fight the pandemic by sharing helpful content and statistics based on demographics features of users around the world. There is an urgent need for such frameworks for tracking helpful content, detecting misleading content, ranking the trusted user content, presenting accurate demographics statistics of the outbreak. In this paper, the real-time tweets of Coronavirus pandemic (COVID-19) analysis will be presented. The proposed framework will be used to track the geographical infections, trends of the content, and the user’s categorization. The framework will include analysis, demographics features, statistical charts, classifying the content of tweets related to its usefulness. The performance of the proposed framework is evaluated based on different measures such as classification accuracy, sensitivity, and specificity. Finally, a set of recommendations will be presented to benefit from the proposed framework with its full potentials as a tool to stand against the COVID-19 spreading.

The outbreak of the new coronavirus disease (COVID-19) in 2019 resulted in more than 100,000 infections and thousands of deaths. The number of deaths and infections continues to rise rapidly since the virus date of appearance. COVID-19 threatens not only human health but also many aspects of life such as manufacturing, social performance, and international relations. Emerging technologies can help in the fight against COVID-19. Emerging technologies include blockchain, Internet of Things (IoT), artificial intelligence (AI), and big data technologies, and they proved its efficiency in practical fields. These fields include the fast aggregation of multi-source big data, fast visualization of epidemic information, diagnosing, remote treatment, and spatial tracking of confirmed cases. Every country in the world is still seeking realistic and cost-effective solutions to stand against COVID-19 under current epidemiological conditions. This chapter discusses the concepts of emerging technologies, applications, and contributions to combating COVID-19. Moreover, the challenges and future research directions are reviewed in detail. Also, a list of publicly available open-source COVID-19 datasets will be presented. Finally, this chapter concludes that cooperation among government, medical institutions, and the scientific community is significant and critical. Also, there is an urgent demand for improvement in the analytical algorithms and electronic devices to combat the COVID-19 pandemic.

The COVID-19 Pandemic has dramatically influenced the global market of energy production and consumption. This influence could be noticed obviously by the latest drop in crude oil prices. Furthermore, coronavirus has affected the supply chains and delayed the development of sustainable energy worldwide. Due to its relevance, academics have begun to study the associations regarding this crisis. The COVID-19 Pandemic offers a new chance to investigate the impacts of prolonged landscape-scale confusion on sustainability change paths in real-time. How the global renewable energy flexibility sector will respond to the COVID-19 Pandemic is a critical question. This crisis could inspire governance structures to plan adequately for other varieties of crises in the future. These improvements can drive research by spouting the generation of new disciplines stemming from the COVID-19 outbreak to expedite sustainability transitions and improve the recognition of governance’s main role in changes. Smart policies could transform COVID-19 threats into great opportunities for the world’s sustainable energy scenario towards green energy generation and use in the coming years. In this paper, the impacts of COVID-19 in terms of the energy sector, especially the electricity and oil sectors, will be explained. The major objective of this research is to shed light on future research on renewable energy.

One of the potential and necessary remote sensing topics is land cover classification. Using artificial intelligence algorithms in those type of applications decreases the required time of classification in a speedy and accurate manner. This paper assessing different deep convolutional neural network (CNN) to classify the land cover via a multispectral Landsat data. To evaluate deep learning convolutional models and better exploit existing deep CNNs in remote sensing image classification, we picked two of the most common current Convolutional Networks models: AlexNet and VGG-16. These two deep CNN models were first trained on the Landsat dataset, which had a limited number of images (approximately 500). The dataset images were derived manually from the Landsat 5 imagery and the features images categorized into five classes. The dataset was collected from the north-eastern region of Egypt along the Nile Valley and Delta regions. The testing classification accuracy reached 74.8% using AlexNet and 90.2% using VGG-16. Then, by using augmentation techniques, the Landsat dataset was expanded to be seven times larger than the original dataset, reaching approximately 3,500 images. On the augmented dataset, the testing classification accuracy was 90.0% using AlexNet and 94.6% using VGG-16.

This paper focuses on a four-class classification problem to automatically detect Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), DRUSEN, and NORMAL class in Optical Coherence Tomography (OCT) images. The proposed deep learning classification model adopted two transfer-learning architecture and a categorical hinge loss as Support Vector Machine (SVM) instances to correctly classify retinal diseases in the retinal OCT dataset. The experiments follow a patient-level 10-fold cross-validation process using a retinal OCT image dataset. The proposed model is tested on the benchmark data with exactly 84495 grey-scale images labelled into 4 classes. The experimental evaluation shows that the proposed model outperforms the conventional deep learning state of the art reported on the OCT dataset with an overall accuracy of 98% for the Xception model and 93% for The inception V3 model.

Potato leaf blight is one of the most devastating global plant diseases because it affects the productivity and quality of potato crops and adversely affects both individual farmers and the agricultural industry. Advances in the early classification and detection of crop blight using artificial intelligence technologies have increased the opportunity to enhance and expand plant protection. This paper presents an architecture proposed for potato leaf blight classification. This architecture depends on deep convolutional neural network. The training dataset of potato leaves contains three categories: healthy leaves, early blight leaves, and late blight leaves. The proposed architecture depends on 14 layers, including two main convolutional layers for feature extraction with different convolution window sizes followed by two fully connected layers for classification. In this paper, augmentation processes were applied to increase the number of dataset images from 1,722 to 9,822 images, which led to a significant improvement in the overall testing accuracy. The proposed architecture achieved an overall mean testing accuracy of 98%. More than 6 performance metrics were applied in this research to ensure the accuracy and validity of the presented results. The testing accuracy of the proposed approach was compared with that of related works, and the proposed architecture achieved improved accuracy compared to the related works.

Highlights • A novel deep learning model for medical face mask detection. • The model can help governments to prevent the COVID-19 transmission. • Two medical face mask datasets have been tested. • The YOLO-v2 with ResNet-50 model achieves high average precision., Deep learning has shown tremendous potential in many real-life applications in different domains. One of these potentials is object detection. Recent object detection which is based on deep learning models has achieved promising results concerning the finding of an object in images. The objective of this paper is to annotate and localize the medical face mask objects in real-life images. Wearing a medical face mask in public areas, protect people from COVID-19 transmission among them. The proposed model consists of two components. The first component is designed for the feature extraction process based on the ResNet-50 deep transfer learning model. While the second component is designed for the detection of medical face masks based on YOLO v2. Two medical face masks datasets have been combined in one dataset to be investigated through this research. To improve the object detection process, mean IoU has been used to estimate the best number of anchor boxes. The achieved results concluded that the adam optimizer achieved the highest average precision percentage of 81% as a detector. Finally, a comparative result with related work has been presented at the end of the research. The proposed detector achieved higher accuracy and precision than the related work.

Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.

Due to the expansion of the Internet of Educational Things (IoET) systems, the importance of using security provisioning became a mandatory issue for supporting various software applications in connected heterogeneous devices. Traditional authentication methods are not efficient in dealing with the security risks in IoT because of their dependence on both complex computations and static security mechanisms. Moreover, the isolation of security mechanisms in each layer while ignoring an integration methodology for overall system protection will lead to the rise of both communication latency and security risks. In this paper, we propose an authentication model to utilize the IoT resources in educational environment while putting energy efficiency into consideration, and provide a mutual verification technique based on a hash function that there is no limitations on the operation performance, compute and network. This research verifies the security and power efficiency of this model through security analysis and performance evaluation, this is mainly done through comparing our proposed method with already existing models. The proposed model has a huge value in its applicability as an authentication security model for IoET environment.

Sustainable dietary plays an essential role in protecting the environment to be healthier. Moreover, it protects human life and health in its widest sense. Fruits and vegetables are basic components of sustainable dietary as it is considered one of the main sources of healthy food for humans. The classifications of fruits and vegetables are most helpful for dietary assessment and guidance which will reflect in increasing the awareness of sustainable dietary for consumers. In this chapter, a robust deep transfer model based on deep convolutional neural networks for fruits and vegetable classification is introduced. This presented model is considered a first step to build a useful mobile software application that will help in raising the awareness of sustainable dietary. Three deep transfer models were selected for experiments in this research and they are Alexnet, Squeeznet, and Googlenet. They were selected as they contained a small number of layers which will decrease the computational complexity. The dataset used in this research is FruitVeg-81 which contains 15,737 images. The number of extracted classes from the dataset is 96 class by expanding three layers of classifications from the original dataset. Augmentation technique (rotation) was adopted in this research to reduce the overfitting and increase the number of images to be 11 times larger than the original dataset. The experiment results show that the Googlenet achieves the highest testing accuracy with 99.82%. Moreover, it achieved the highest precision, recall, and F1 performance score if it is compared with other models. Finally, A comparison results were carried out at the end of the research with related work which used the same dataset FruitVeg-81. The presented work achieved a superior result than the related work in terms of testing accuracy.

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models will be presented. The study will be conducted over a limited COVID-19 x-ray. The study relies on neutrosophic set and theory to convert the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images, and they are the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. The dataset used in this research has been collected from different sources. The dataset is classified into four classes {COVID-19, normal, pneumonia bacterial, and pneumonia virus}. This study aims to review the effect of neutrosophic sets on deep transfer learning models. The selected deep learning models in this study are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures. To test the performance of the conversion to the neutrosophic domain, more than 36 trials have been conducted and recorded. A combination of training and testing strategies by splitting the dataset into (90-10%, 80-20%, 70-30) is included in the experiments. Four domains of images are tested, and they are, the original domain, the True (T) domain, the Indeterminacy (I) domain, and the Falsity (F) domain. The four domains with the different training and testing strategies were tested using the selected deep transfer models. According to the experimental results, the Indeterminacy (I) neutrosophic domain achieves the highest accuracy possible with 87.1% in the testing accuracy and performance metrics such as Precision, Recall, and F1 Score. The study concludes that using the neutrosophic set with deep learning models may be an encouraging transition to achieve better testing accuracy, especially with limited COVID-19 datasets.

Highlights • A hybrid deep and machine learning model proposed for face mask detection. • The model can impede the Coronavirus transmission, specially COVID-19. • Three face mask datasets have experimented with this research. • The introduced model achieves high performance in the experimental study., 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.

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%.

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a neutrosophic with a deep learning model for the detection of COVID-19 from chest X-ray medical digital images is presented. The proposed model relies on neutrosophic theory by converting the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images and they are, the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. Using neutrosophic images has positively affected the accuracy of the proposed model. The dataset used in this research has been collected from different sources as there is no benchmark dataset for COVID-19 chest X-ray until the writing of this research. The dataset consists of four classes and they are COVID-19, Normal, Pneumonia bacterial, and Pneumonia virus. After the conversion to the neutrosophic domain, the images are fed into three different deep transfer models and they are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures and they have been used with related work. To test the performance of the conversion to the neutrosophic domain, four scenarios have been tested. The first scenario is training the deep transfer models with True (T) neutrosophic images only. The second one is training on Indeterminacy (I) neutrosophic images, while the third scenario is training the deep models over the Falsity (F) neutrosophic images. The fourth scenario is training over the combined (T, I, F) neutrosophic images. According to the experimental results, the combined (T, I, F) neutrosophic images achieved the highest accuracy possible for the validation, testing and all performance metrics such Precision, Recall and F1 Score using Resnet18 as a deep transfer model. The proposed model achieved a testing accuracy with 78.70%. Furthermore, the proposed model using neutrosophic and Resnet18 had achieved superior testing accuracy with a related work which achieved 52.80% with the same experimental environmental setup and the same deep learning hyperparameters.

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

Skin cancer is the most common type of cancer. One in every three cancers diagnosed is a skin cancer according to skin cancer foundation statistics globally. The early detection of this type of cancer would help in raising the opportunities of curing it. The advances in computer algorithms such as deep learning would help doctors to detect and diagnose skin cancer automatically in early stages. This paper introduces an empirical study and enhancement on deep transfer learning for skin lesions detection. The study selects different pre-trained deep convolutional neural network models such as resnet18, squeezenet, google net, vgg16, and vgg19 to be applied into two different datasets. The datasets are MODE-NODE and ISIC skin lesion datasets. Data augmentation techniques have been adopted in this study to enlarge the total number of images in the datasets to be 5 times larger than the original datasets. The adopted augmentation techniques make the DCNN models more robust and prevent overfitting. Moreover, seven accredited performance matrices in deep learning have been used to conclude an optimal selection of the most appropriate DCNN model that fits the nature of the skin lesions datasets. The study concludes that vgg19 is the most appropriate DCNN according to testing accuracy measurement and achieved 98.8%. The seven performance matrices strengthen this result. Also, a comparative result was introduced with related works. The vgg19 overcomes the related work in terms of testing accuracy and the performance matrices on both datasets. Finally, the vgg19 model was trained on a smaller number of images than the related work by 10 times, which proved that the choice of data augmentation techniques played an important role in achieving better results. That would reflect on reducing the training time, memory consumption and the calculation complexity.

The COVID-19 coronavirus is one of the latest viruses that hit the earth in the new century. It was declared as a pandemic by the World Health Organization in 2020. In this chapter, a model for the detection of COVID-19 virus from CT chest medical images will be presented. The proposed model is based on Generative Adversarial Networks (GAN), and a fine-tuned deep transfer learning model. GAN is used to generate more images from the available dataset. While deep transfer models are used to classify the COVID-19 virus from the normal class. The original dataset consists of 746 images. The is divided into two parts; 90% for the training and validation phase, while 10% for the testing phase. The 90% then is divided into 80% percent for the training and 20% percent for the validation after using GAN as image augmenter. The proposed GAN architecture raises the number of images in the training and validation phase to be 10 times larger than the original dataset. The deep transfer models which are selected for experimental trials are Resnet50, Shufflenet, and Mobilenet. They were selected as they include a medium number of layers on their architectures if they are com-pared with large deep transfer models such as DenseNet, and Inception-ResNet. This will reflect on the performance of the proposed model in terms of reducing training time, memory and CPU usage. The experimental trials show that Shufflenet is selected to be the optimal deep transfer learning in the proposed model as it achieves the highest possible for testing accuracy and performance metrics. Shufflenet achieves an overall testing accuracy with 84.9, and 85.33% in all performance metrics which include recall, precision, and F1 score.

Over the last few years, MENA region became an attractive target for cyber-attacks perpetrators. Hackers focus on governmental high valued sectors (i.e. oil and gas) alongside with other critical industries. MENA nations are increasingly investing in Information and Communication Technologies (ICTs) sector, social infrastructure, economic sector, schools and hospitals in the area are now completely based on the Internet. Currently, the position of ICTs became an essential phase of the domestic future and global security structure in the MENA Region, emphasizing the real need for a tremendous development in cybersecurity at a regional level. This environment raises questions about the developments in cybersecurity and offensive cyber tactics; this paper examines and investigates (1) the essential cybersecurity threats in MENA region, (2) the major challenges that faces both governments and organizations (3) the main countermeasures that governments follow to achieve the protection and business continuity in the region. It stresses the need for the importance of cybercrime legislation and higher defenses techniques towards cyberterrorism for MENA nations. It argues for the promotion of a cybersecurity awareness for the individuals as an effective mechanism for facing the current risks of cybersecurity in MENA region.

Within the trials of the utilization of technology for the society, efforts have proven advantages of the new wave of rapid-growth change that began and is anticipating to proliferate with more potent connectivity and interoperability of diverse devices, named as the Social internet of things (SIoT). It is a rising paradigm of IoT wherein distinctive IoT devices interact and set up relationships with each in the academic field nowadays. Objects are establishing their social relationships in an unbiased way. The main issue is to understand how the objects in Social IoT can interact in the higher education systems to implement a secure system. Consequently, focus on the trustworthiness models. Because of the billions of linked devices, there may be a huge risk of identity and information leakage, device manipulation, records falsification, server/network attack and subsequent effect to application platforms. Whilst the variety of these interconnected devices keeps to grow each day in the academic learning field, So, does the wide variety of security threats and vulnerabilities posed to these devices at universities. Security is one of the most paramount technological studies troubles that exist nowadays for IoT. Security has many aspects; security built in the device, security of data transmission and data storage inside the systems and its applications. There may be an intensive quantity of literature that exists on the problem with endless issues as well as proposed solutions; however, maximum of the existing paintings does no longer provide a holistic view of protection and data privacy issues in the IoT. The primary aim of this research work is to state the risks and threats that faces SIoT by identifying (a) The essential domains in which SIoT is highly used in higher education, (b) The security necessities and challenges that SIoT is currently dealing with, (c) the existing security solutions which have been proposed or applied with their barriers.

Terrorist attacks are the most significant challenging issue for the humankind across the world, which need the whole attention of all nations. No doubt, that the terrorism attacks in Egypt negatively affect its economy and decreases its opportunities for foreign investments in the last decade. In this paper, statistical techniques will be applied to terrorism attacks occurred in Egypt in the last four decades. The used database in this study is the Global Terrorism Database (GTD), which is published by the University of Maryland. Statistical insights graphs will be presented in the paper. In addition, association-mining algorithms will be used to find the frequent hidden patterns in the database. Those algorithms help in generating composite rules by mining the database, which generates deeper meaningful insights. The statistical insights and association mining will help to understand the nature of terrorist attacks in Egypt. Moreover, it will give the decision-makers the knowledge to overcome and step ahead the terrorist attacks before occurring. This will reflect on Egypt economy positively and increases the opportunities to have better investments and encourage the return of tourism.

This chapter represents a new object dependent for online review evaluation for improving performance by a proposed performance criterion. This criterion can introduce an alternative solution for measuring sentiment accuracy. The problem illustrates in the accuracy comparison measurement between different sentiment techniques and frameworks. Each technique or framework targets one sentiment challenge or more. Another challenge appears in constructing database and its characteristics as memorability. For example, if two sentiment techniques are equal percentage of accuracy, the problem is that meaning they are achieved to the same classification, polarity and score. Is the sentiment challenge accuracy with 10% is bad? So, this study compares between several techniques based on proposed performance assessment. This assessment puts them in the same environment with respect three perspective. It is a new proposed criterion for performance measurement, which includes aggregation of performance measurement types: F-measure and Runtime with respect to speed of run time, memorability, and sentiment analysis challenge type. A comparison between several sentiment techniques is in movie domain in English language. It works on word-level sentiment analysis to measure the proposed performance criteria. It applies two experiments to evaluate the percentage degree of different techniques performance on measuring sentiments.

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (in the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection.

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.

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.

Bacterial colony classification is an important problem in microbiology. With the advances in computer-aided software's, similar problems have been solved in a speedy and accurate manner during the last decade. In this paper, deep neural network architecture will be presented to solve the bacterial colony classification problem. In addition, the training and testing strategy that relies on the strong use of data augmentation will be introduced. The used dataset was limited as it contains 660 images for 33 classes of a bacterial colony. Any neural network cannot learn from this data directly and in case of learning the neural network will overfit. The adopted training and testing strategy lead to a significant improvement in the training and testing phases. It raised the dataset images to 6,600 images for the training phase and 5,940 images for verification phase. The proposed neural network with the adopted augmentation techniques achieved 98.22% in testing accuracy. A comparative result is presented, 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.

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

The nature of playing video streams over a network in a real time requires that the transmitted frames are sent in a bounded delay. Also, video frames need to be displayed at a certain rate; therefore, sending and receiving encrypted packets must be sent in a certain amount of time in order to utilize the admissible delay. There are two main components for securing multimedia transmissions. Key management protocols such as Transport Layer Security (TLS) protocol is used as cryptographic protocol that provides security and data integrity for communications over IP Networks. Advanced Encryption Standard (AES) is used as a symmetric block cipher, that can encrypt (encipher) and decrypt (decipher) video frames. The Real-time Transport Protocol (RTP) protocol is used as a standardized packet format for delivering video over the IP Networks. This makes secure video encryption feasible for real-time applications without any extra dedicated hard-ware. In this paper; we studied the impact of frame rate on a secured real time transmission of video over IP networks. Through this paper, we will try to find the most suitable frame rate in order to achieve better data rate and fewer frame and packet loss. This is done via java as a programming language for implementation.

Agriculture is one of the most important sources for human food throughout the history of humankind. In many countries, agriculture is the foundation of its economy, and more than 90% of its population deriving their livelihoods from it. Insect pests are one of the main factors affecting agricultural crop production. With the advances of computer algorithms and artificial intelligence, accurate and speedy recognition of insect pests in early stages may help in avoiding economic losses in short and long term. In this paper, an insect pest recognition based on deep transfer learning models will be presented. The IP102 insect pest dataset was selected in this research. The IP102 dataset consists of 27500 images and contains 102 classes of insect pests, it is considered one the biggest dataset for insect pest and was launched in 2019. Through the paper, AlexNet, GoogleNet, and SqueezNet were the selected deep transfer learning models. Those models were selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Data augmentation techniques were used to render the models more robust and to overcome the overfitting problem by increasing the dataset images up to 4 times than original images. 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 89.33%. In addition, it has a minimum number of layers, which decreases the training time and computational complexity. Moreover, the choice of data augmentation techniques played an important role in achieving better results. Finally, A comparison results were carried out at the end of the research with related work which used the same dataset IP102. The presented work achieved a superior result than the related work in terms of testing accuracy, precision, recall, and F1 score.

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.