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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.

Cloud computing refers to the services and applications that are accessible throughout the world from data centers. All services and applications are available online. Virtual machine migration is an important part of virtualization which is considered as essential part in cloud computing environment. Virtual Machine Migration means transferring a running Virtual Machine with all its applications and the operating system state as it is to target destination machine where it continues to run as if nothing happened. It makes balancing between servers. This improves the performance by redistributing the workload among available servers. There are many algorithms of load balancing classified into two types: static load balancing algorithms and dynamic load balancing algorithms. This paper presents the algorithm (Balanced Throttled Load Balancing Algorithm- BTLB). It compares the results of the BTLB with round robin algorithm, AMLB algorithm and throttled load balancing algorithm. The results of these four algorithms would be presented in this paper. The proposed algorithm shows the improvement in response time (75 µs). Cloud analyst simulator is used to evaluate the results. BTLB was developed and tested using Java.

In this paper, an approach for classifying gastrointestinal (GI) diseases from endoscopic images is proposed. The proposed approach is built using a convolutional neural network (CNN) with batch normalization (BN) and an exponential linear unit (ELU) as the activation function. The proposed approach consists of eight layers (six convolutional and two fully connected layers) and is used to identify eight types of GI diseases in version two of the Kvasir dataset. The proposed approach was compared with other CNN architectures (VGG16, VGG19, and Inception-v3) using five elements (number of convolutional layers, number of total parameters of the convolutional layers, number of epochs, validation accuracy and test accuracy). The proposed approach achieved good results compared to the compared architectures. It achieved a validation accuracy of 88%, which is superior to other architectures and a test accuracy of 87%, which outperforms the Inception-v3 architecture. Therefore, the proposed approach has less trained images and less computational complexity in the training phase.

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.

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.

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.

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.

Convolutional neural network has proven to be highly efficient in image classification, but this approach has some disadvantages. Notably, a large number of images are required for training, and great time is need for training to achieve high degree of accuracy in the classification. Transfer learning with a pre-trained model can be used to overcome these problems. There are two approaches to transfer learning, feature extraction approach and fine-tuning approach. Based on the used dataset, both approaches were evaluated, and the two approaches yielded high accuracy with a little better result for fine-tuning approach. To overcome overfitting issues that may occur during transfer learning, data augmentation and dropout techniques were applied. The dataset studied contained 6899 images with 8 distinct unbalanced classes, but only 5600 images with balanced classes were analyzed. The classes include airplane, car, cat, dog, flower, fruit, motorbike and person. We used Google’s Inception-v3 model that is trained on ImageNet database and can classify images into 1000 object categories like pencil, Zebra and many animals. We retrained the Inception model to classify the used natural images using the Keras Library with Tensorflow as backend and achieved an overall accuracy of 99.7% for the test data.

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.

This paper is an extended version of "Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks". In this paper, a robust deep convolutional neural network architecture for galaxy morphology classification is presented. A galaxy can be classified based on its features into one of three categories (Elliptical, Spiral, or Irregular) according to the Hubble galaxy morphology classification from 1926. The proposed convolutional neural network architecture consists of 8 layers, including one main convolutional layer for feature ex-traction with 96 filters and two principle fully connected layers for classification. The architecture is trained over 4238 images and achieved a 97.772% testing accuracy. In this version, "Deep Galaxy V2", an augmentation process is applied to the training data to overcome the overfitting problem and make the proposed architecture more robust and immune to memorizing the training data. A comparative result is present, 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.

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.