Cloud computing is a ubiquitous network access model to a shared pool of configurable computing resources where available resources must be checked and scheduled using an efficient task scheduler to be assigned to clients. Most of the existing task schedulers, did not achieve the required standards and requirements as some of them only concentrated on waiting time or response time reduction or even both neglecting the starved processes at all. In this paper, we propose a novel hybrid task scheduling algorithm named (SRDQ) combining Shortest-Job-First (SJF) and Round Robin (RR) schedulers considering a dynamic variable task quantum. The proposed algorithms mainly relies on two basic keys the first having a dynamic task quantum to balance waiting time between short and long tasks while the second involves splitting the ready queue into two sub-queues, Q1 for the short tasks and the other for the long ones.Assigning tasks to resources from Q₁ or Q₂ are done mutually two tasks from Q₁ and one task from Q₂. For evaluation purpose, three different datasets were utilized during the algorithm simulation conducted using CloudSim environment toolkit 3.0.3 against three different scheduling algorithms SJF, RR and Time Slice Priority Based RR (TSPBRR) Experimentations results and tests indicated the superiority of the proposed algorithm over the state of art in reducing waiting time, response time and partially the starvation of long tasks.

Energy consumption always represents a challenge in the ad hoc networks which spurred the researchers to benefit from the bio-inspired algorithms and their fitness functions to evaluate nodes energy through the path discovery stage. In this paper we propose energy efficient routing protocol based on the well-known Ad Hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol and a bio-inspired algorithm called Elephant Herding Optimization (EHO). In the proposed EHO-AOMDV the overall consumed energy of nodes is optimized by classifying nodes into two classes, while paths are discovered from the class of the fittest nodes with sufficient energy for transmission to reduce the probability of path failure and the increasing number of dead nodes through higher data loads. The EHO updating operator updates classes based on separating operator that evaluates nodes based on residual energy after each transmission round. Experiments were conducted using Ns-3 with five evaluation metrics routing overhead, packet delivery ratio, average energy consumption, end-to-end delay and number of dead nodes and four implemented protocols the proposed protocol, AOMDV and two bio-inspired protocols ACO-FDRPSO and FF-AOMDV. Results indicated that the proposed EHO-AOMDV attained higher packet delivery ratio with less routing overhead, average energy consumption and number of dead nodes over the state of art while in the end-to-end delay AOMDV has outperformed the proposed protocol.

This study presents a secret key sharing protocol that establishes cryptographically secured communication between two entities. A new symmetric key exchange scenario for smart city applications is presented in this research. The protocol is based on the specific properties of the Fuss‐Catalan numbers and the Lattice Path combinatorics. The proposed scenario consists of three phases: generating a Fuss‐Catalan object based on the grid dimension, defining the movement in the Lattice Path Grid and defining the key equalisation rules. In the experimental part, the authors present the security analysis of the protocol as well as its test. Also, they examine the equivalence of the proposed with Maurer's satellite scenario and suggest a new scenario that implements an information‐theoretical protocol for the public key distribution. Additionally, a comparison with related studies and methods is provided, as well as a comparison with satellite scenario, which proves the advantages of solution presented by the authors. Finally, they propose further research directions regarding key management in smart city applications.

Healthcare is one of the industries that seeks to deliver medical services to patients on time. One of the issues it currently grapples with is real-time patient data exchange between various healthcare organizations. This challenge was solved by both centralized and decentralized cloud computing architecture solutions. In this paper, we review the current state of these two cloud computing architectures in the health sector with regard to the effect on the efficiency of Health Information Exchange (HIE) systems. Our study seeks to determine the relevance of these cloud computing approaches in assisting healthcare facilities in the decision-making process to adopt HIE systems. This paper considers the system performance, patient data privacy, and cost and identifies research directions in each of the architectures. This study shows that there are some benefits in both cloud architectures, but there are also some drawbacks. The prominent characteristic of centralized cloud computing is that all data and information are stored together at one location, known as a single data center. This offers many services, such as integration, effectiveness, simplicity, and rapid information access. However, it entails providing data privacy and confidentiality aspects because it will face the hazard of a single point of failure. On the other hand, decentralized cloud computing is built to safeguard data privacy and security whereby data are distributed to several nodes as a way of forming mini-data centers. This increases the system’s ability to cope with a node failure. Thus, continuity and less latency are achieved. Nevertheless, it poses integration issues because managing data from several sites could be a problem, and the costs of operating several data centers are higher and complex. This paper also pays attention to the differences in aspects like efficiency, capacity, and cost. This paper assists healthcare organizations in determining the most suitable cloud architecture strategy for deploying secure and effective HIE systems.

Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.