Objective To assess the level and determinants of practice in diabetes self-management at primary health care centers (PHCCs) and to analyze the association of self-management with the level of glycemic control. Method A cross-sectional study was conducted among patients with type 1 and type 2 diabetes, aged ≥ 17 years, and being followed at PHCCs in Jeddah, Saudi Arabia, from December 1, 2019, to December 30, 2019. A multistage cluster sampling technique was used to select 350 participants from five PHCCs. The level of practice in self-management was assessed using the Arabic version of the Summary of Diabetes Self-care Activities (SDSCA) questionnaire. The tool was administered as a face-to-face interview, followed by the collection of sociodemographic and relevant clinical data. In addition, blood was collected to measure fasting blood glucose (FBG) and HbA1c levels. The association of the overall SDSCA score with diabetes control was analyzed using linear regression and the receiver operator characteristics (ROC) curve. Multivariate binary logistic regression was carried out to analyze independent factors of inadequate practice. Result The overall mean (SD) SDSCA score was 3.13 (1.13)/7. Of the five dimensions of self-care, medication adherence yielded the highest score (mean=5.39 days per week), followed by diet (2.83) and blood glucose monitoring (2.78), while footcare had the lowest level of practice (2.26). The SDSCA score showed a negative correlation with the level of HbA1c, with a correlation coefficient r-squared =0.530 and regression coefficient B=-0.648 (p <0.001). ROC curve analysis showed that optimal glycemic control was associated with SDSCA score cutoff ≥3.5 with 82.0% sensitivity and 77.0% specificity, and the model showed that 38.0% of participants had adequate practice in self-management. Inadequate practice in diabetes self-management was independently associated with age >50 years (OR=2.00 [95%CI=1.02, 3.89]), rental accommodation (OR=0.42 [95%CI=0.23, 0.76]), independent job (OR=3.98 [95%CI=1.66, 9.57]), and longer duration of diabetes (≥8 years) (OR=4.25 [95%CI=1.82, 9.92]). Conclusion There are low levels of diabetes self-management among patients being followed at Jeddah PHCCs. This is associated with suboptimal glycemic control among the majority of the patients, indicating the importance of self-management to improve diabetes control. Patient health literacy and education for self-management should be considered the standard of care for diabetic patients in all PHCCs, with specific attention to subcategories of patients with the lowest levels of practice in self-management such as those with a longer duration of diabetes and the elderly.

Technological advancement has transformed traditional vehicles into autonomous vehicles. Autonomous vehicles play an important role since they are considered an essential component of smart cities. The autonomous vehicle is an intelligent vehicle capable of maintaining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, autonomous vehicles collect information about the outside environment using sensors to ensure safe navigation. Autonomous vehicles reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, autonomous vehicles could be threatened by cyberattacks, posing risks to human life. For example, researchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW autonomous vehicles. Therefore, further research is needed to detect cyberattacks targeting the control components of autonomous vehicles to mitigate their negative consequences. This research will contribute to the security of autonomous vehicles by detecting cyberattacks in the early stages. First, we inject False Data Injection (FDI) attacks into an autonomous vehicle simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify false data cyber-attacks targeting the control system of autonomous vehicles through a compromised sensor. We utilize long short-term memory (LSTM) deep networks to detect False Data Injection (FDI) attacks in the early stage to ensure the stability of the operation of autonomous vehicles. Our method classifies the collected dataset into two classifications: normal and anomaly data. The experimental result shows that our proposed model’s accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

Nowadays, the Internet of Things (IoT) devices and applications have rapidly expanded worldwide due to their benefits in improving the business environment, industrial environment, and people’s daily lives. However, IoT devices are not immune to malicious network traffic, which causes potential negative consequences and sabotages IoT operating devices. Therefore, developing a method for screening network traffic is necessary to detect and classify malicious activity to mitigate its negative impacts. This research proposes a predictive machine learning model to detect and classify network activity in an IoT system. Specifically, our model distinguishes between normal and anomaly network activity. Furthermore, it classifies network traffic into five categories: normal, Mirai attack, denial of service (DoS) attack, Scan attack, and man-in-the-middle (MITM) attack. Five supervised learning models were implemented to characterize their performance in detecting and classifying network activities for IoT systems. This includes the following models: shallow neural networks (SNN), decision trees (DT), bagging trees (BT), k-nearest neighbor (kNN), and support vector machine (SVM). The learning models were evaluated on a new and broad dataset for IoT attacks, the IoTID20 dataset. Besides, a deep feature engineering process was used to improve the learning models’ accuracy. Our experimental evaluation exhibited an accuracy of 100% recorded for the detection using all implemented models and an accuracy of 99.4–99.9% recorded for the classification process.

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images. We developed a novel computationally light and optimized deep Convolutional Neural Networks (CNNs) based framework for chest X-ray analysis. We proposed a new COV-Net to learn COVID-specific patterns from chest X-rays and employed several machine learning classifiers to enhance the discrimination power of the presented framework. Systematic exploitation of max-pooling operations facilitates the proposed COV-Net in learning the boundaries of infected patterns in chest X-rays and helps for multi-class classification of two diverse infection types along with normal images. The proposed framework has been evaluated on a publicly available benchmark dataset containing X-ray images of coronavirus-infected, pneumonia-infected, and normal patients. The empirical performance of the proposed method with developed COV-Net and support vector machine is compared with the state-of-the-art deep models which show that the proposed deep hybrid learning-based method achieves 96.69% recall, 96.72% precision, 96.73% accuracy, and 96.71% F-score. For multi-class classification and binary classification of COVID-19 and pneumonia, the proposed model achieved 99.21% recall, 99.22% precision, 99.21% F-score, and 99.23% accuracy.

Segmentation of retinal vessels plays a crucial role in detecting many eye diseases, and its reliable computerized implementation is becoming essential for automated retinal disease screening systems. A large number of retinal vessel segmentation algorithms are available, but these methods improve accuracy levels. Their sensitivity remains low due to the lack of proper segmentation of low contrast vessels, and this low contrast requires more attention in this segmentation process. In this paper, we have proposed new preprocessing steps for the precise extraction of retinal blood vessels. These proposed preprocessing steps are also tested on other existing algorithms to observe their impact. There are two steps to our suggested module for segmenting retinal blood vessels. The first step involves implementing and validating the preprocessing module. The second step applies these preprocessing stages to our proposed binarization steps to extract retinal blood vessels. The proposed preprocessing phase uses the traditional image-processing method to provide a much-improved segmented vessel image. Our binarization steps contained the image coherence technique for the retinal blood vessels. The proposed method gives good performance on a database accessible to the public named DRIVE and STARE. The novelty of this proposed method is that it is an unsupervised method and offers an accuracy of around 96% and sensitivity of 81% while outperforming existing approaches. Due to new tactics at each step of the proposed process, this blood vessel segmentation application is suitable for computer analysis of retinal images, such as automated screening for the early diagnosis of eye disease.

In wireless personal area networks (WPANs), devices can communicate with each other without relying on a central router or access point. They can improve performance and efficiency by allowing devices to share resources directly; however, managing resource allocation and optimizing communication between devices can be challenging. This paper proposes an intelligent load-based resource optimization model to enhance the performance of device-to-device communication in 5G-WPAN. Intelligent load-based resource optimization in device-to-device communication is a strategy used to maximize the efficiency and effectiveness of resource usage in device-to-device (D2D) communications. This optimization strategy is based on optimizing the network’s resource load by managing resource utilization and ensuring that the network is not overloaded. It is achieved by monitoring the current load on the network and then adjusting the usage of resources, such as bandwidth and power, to optimize the overall performance. This type of optimization is essential in D2D communication since it can help reduce costs and improve the system’s performance. The proposed model has achieved 86.00% network efficiency, 93.74% throughput, 91.94% reduced latency, and 92.85% scalability.

Surveillance cameras have recently been utilized to provide physical security services globally in diverse private and public spaces. The number of cameras has been increasing rapidly due to the need for monitoring and recording abnormal events. This process can be difficult and time-consuming when detecting anomalies using human power to monitor them for special security purposes. Abnormal events deviate from normal patterns and are considered rare. Furthermore, collecting or producing data on these rare events and modeling abnormal data are difficult. Therefore, there is a need to develop an intelligent approach to overcome this challenge. Many research studies have been conducted on detecting abnormal events using machine learning and deep learning techniques. This study focused on abnormal event detection, particularly for video surveillance applications, and included an up-to-date state-of-the-art that extends previous related works. The major objective of this survey was to examine the existing machine learning and deep learning techniques in the literature and the datasets used to detect abnormal events in surveillance videos to show their advantages and disadvantages and summarize the literature studies, highlighting the major challenges.

Web readers usually skim through the text to keep up with the amount of available content. The effectiveness of skim reading is ensured by keeping the focus on the meaningful part of the text rather than the less meaningful part. To assure if the skim reading shows efficient results for a particular screen resolution, this research presents variations in the memory of significant data when a text is read on a mobile screen or a desktop screen. Moreover, the study aims to understand the knowledge gained from the text at a given time. In total, sixty participants contributed to the study and it is found that, relative to reading the text on a mobile screen, skimming showed improved memory for the ideas defined in the text on a computer screen. A software prototype is developed in this research study to analyze the impact of skim reading on a desktop computer screen versus a mobile device screen. The findings of the study have been interpreted as evidence in support of a skimming process called satisficing.