The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that

Abdominal Aortic Aneurysm (AAA) is a serious medical condition characterized by the abnormal enlargement of the abdominal aorta. If left untreated, AAA can have life-threatening consequences. Accurate segmentation of the aorta in Computed Tomography Angiography (CTA) images plays a vital role in treatment planning for AAA. However, manual and semi-automatic segmentation methods suffer from limitations in terms of time and accuracy. This study presents a deep learning pipeline that aims to fully automate the precise and efficient segmentation of the aorta and its branches within CTA images. A

Hypoglycemia in Type 1 Diabetes (T1D) refers to a condition where blood glucose (BG) levels drop to abnormally low levels, typically below 70 mg/dL. This can occur when there is an excessive amount of insulin relative to the blood glucose level, leading to an imbalance that can be dangerous and potentially life-threatening if not promptly treated. The availability of large amounts of data from continuous glucose monitoring (CGM), insulin doses, carbohydrate intake, and additional vital signs, together with deep learning (DL) techniques, has revolutionized algorithmic approaches for BG

According to the World Health Organization, several factors have affected the accurate reporting of SARS-CoV-2 outbreak status, such as limited data collection resources, cultural and educational diversity, and inconsistent outbreak reporting from different sectors. Driven by this challenging situation, this study investigates the potential expediency of using social network data to develop reliable early information surveillance and warning system for pandemic outbreaks. As such, an enhanced framework of three inherently interlinked subsystems is proposed. The first subsystem includes data

Concept drift detection is an essential step to maintain the accuracy of online machine learning. The main task is to detect changes in data distribution that might cause changes in the decision boundaries for a classification algorithm. Upon drift detection, the classification algorithm may reset its model or concurrently grow a new learning model. Over the past fifteen years, several drift detection methods have been proposed. Most of these methods have been implemented within the Massive Online Analysis (MOA). Moreover, a couple of studies have compared the drift detectors. However, such

Document forgery detection is becoming increasingly important in the current era, as forgery techniques are available to even inexperienced users. Source printer identification is a method for identifying the source printer and classifying the questioned document into one of the printer classes. According to what we know, most earlier studies segmented documents into characters, words, and patches or cropped them to obtain large datasets. In contrast, in this paper, we worked with the document as a whole and a small dataset. This paper uses three techniques dependent on CNN to find the

In this work, the stochastic dispersion of novel coronavirus disease 2019 (COVID-19) at the borders between France and Italy has been considered using a multi-input multi-output stochastic model. The physical effects of wind, temperature and altitude have been investigated as these factors and physical relationships are stochastic in nature. Stochastic terms have also been included to take into account the turbulence effect, and the r and om nature of the above physical parameters considered. Then, a method is proposed to identify the developed model's order and parameters. The actual data has

In the context of the Internet of Things (IoT), lightweight block ciphers are of vital importance. Due to the nature of the devices involved, traditional security solutions can add overhead and perhaps inhibit the application's objective due to resource limits. Lightweight cryptography is a novel suite of ciphers that aims to provide hardware-constrained devices with a high level of security while maintaining a low physical cost and high performance. In this paper, we are going to evaluate the performance of some of the recently proposed lightweight block ciphers (GIFT-COFB, Romulus, and

Energy efficiency of Radio Access Networks (RANs) is increasingly becoming a global strategic priority for Mobile Network Operators (MNOs) and governments to attain sustainable and uninterruptible network services. In this work, we propose an autonomous Machine Learning (ML)-based framework to maximize RAN energy efficiency via underutilized radio resource shutdown while maintaining an adequate network capacity with a preset Quality-Of-Service (QoS) level. This is achieved by dynamically switching radio resources on and off according to service demand. Training on a live network dataset and

Since late 2019, Covid-19 has broken out causing immense pressure on healthcare systems worldwide. Fast detection of Covid-19 has become crucial in controlling and slow-pacing the virus outbreak. Innovative methods that are cheap, fast, and accurate for Covid-19 detection are of high importance to aid in the efforts of containment of the disease. In this study a novel method is proposed for Covid-19 detection through analysis of ECG image records. Three models are introduced for three classification schemas, Normal vs Covid-19, Covid-19 vs non Covid-19, Normal vs Covid-19 vs Abnormal HeartBeat