Princess Sumaya University of Technology

Keshta I., Odeh A.

Electronic Medical Records (EMRs) can provide many benefits to physicians, patients and healthcare services if they are adopted by healthcare organizations. But concerns about privacy and security that relate to patient information can cause there to be relatively low EMR adoption by a number of health institutions. Safeguarding a huge quantity of health data that is sensitive at separate locations in different forms is one of the big challenges of EMR. A review is presented in this paper to identify the health organizations’ privacy and security concerns and to examine solutions that could address the various concerns that have been identified. It shows the IT security incidents that have taken place in healthcare settings. The review will enable researchers to understand these security and privacy concerns and solutions that are available.

Al-Dmour H., Masa’deh R., Salman A., Abuhashesh M., Al-Dmour R.

Background Despite the growing body of literature examining social media in health contexts, including public health communication, promotion, and surveillance, limited insight has been provided into how the utility of social media may vary depending on the particular public health objectives governing an intervention. For example, the extent to which social media platforms contribute to enhancing public health awareness and prevention during epidemic disease transmission is currently unknown. Doubtlessly, coronavirus disease (COVID-19) represents a great challenge at the global level, aggressively affecting large cities and public gatherings and thereby having substantial impacts on many health care systems worldwide as a result of its rapid spread. Each country has its capacity and reacts according to its perception of threat, economy, health care policy, and the health care system structure. Furthermore, we noted a lack of research focusing on the role of social media campaigns in public health awareness and public protection against the COVID-19 pandemic in Jordan as a developing country. Objective The purpose of this study was to examine the influence of social media platforms on public health protection against the COVID-19 pandemic via public health awareness and public health behavioral changes as mediating factors in Jordan. Methods A quantitative approach and several social media platforms were used to collect data via web questionnaires in Jordan, and a total of 2555 social media users were sampled. This study used structural equation modeling to analyze and verify the study variables. Results The main findings revealed that the use of social media platforms had a significant positive influence on public health protection against COVID-19 as a pandemic. Public health awareness and public health behavioral changes significantly acted as partial mediators in this relationship. Therefore, a better understanding of the effects of the use of social media interventions on public health protection against COVID-19 while taking public health awareness and behavioral changes into account as mediators should be helpful when developing any health promotion strategy plan. Conclusions Our findings suggest that the use of social media platforms can positively influence awareness of public health behavioral changes and public protection against COVID-19. Public health authorities may use social media platforms as an effective tool to increase public health awareness through dissemination of brief messages to targeted populations. However, more research is needed to validate how social media channels can be used to improve health knowledge and adoption of healthy behaviors in a cross-cultural context.

Alhijawi B., Awajan A.

A genetic algorithm (GA) is an evolutionary algorithm inspired by the natural selection and biological processes of reproduction of the fittest individual. GA is one of the most popular optimization algorithms that is currently employed in a wide range of real applications. Initially, the GA fills the population with random candidate solutions and develops the optimal solution from one generation to the next. The GA applies a set of genetic operators during the search process: selection, crossover, and mutation. This article aims to review and summarize the recent contributions to the GA research field. In addition, the definitions of the GA essential concepts are reviewed. Furthermore, the article surveys the real-life applications and roles of GA. Finally, future directions are provided to develop the field.

Hamdan S., Ayyash M., Almajali S.

The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient. Traditional cloud-computing resources are used to partially handle some of the IoT resource-limitation issues; however, using the resources in cloud centers leads to other issues, such as latency in time-critical IoT applications. Therefore, edge-cloud-computing technology has recently evolved. This technology allows for data processing and storage at the edge of the network. This paper studies, in-depth, edge-computing architectures for IoT (ECAs-IoT), and then classifies them according to different factors such as data placement, orchestration services, security, and big data. Besides, the paper studies each architecture in depth and compares them according to various features. Additionally, ECAs-IoT is mapped according to two existing IoT layered models, which helps in identifying the capabilities, features, and gaps of every architecture. Moreover, the paper presents the most important limitations of existing ECAs-IoT and recommends solutions to them. Furthermore, this survey details the IoT applications in the edge-computing domain. Lastly, the paper recommends four different scenarios for using ECAs-IoT by IoT applications.

Abu Zayyad H.M., Obeidat Z.M., Alshurideh M.T., Abuhashesh M., Maqableh M., Masa’deh R.

Corporate social responsibility (CSR) is a vital construct in the banking industry due to its influence on brand credibility, positive word of mouth, and repeat purchases. The purpose of this resea...

Albulayhi K., Abu Al-Haija Q., Alsuhibany S.A., Jillepalli A.A., Ashrafuzzaman M., Sheldon F.T.

The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with using two entropy-based approaches (i.e., information gain (IG) and gain ratio (GR)) to select and extract relevant features in various ratios. Then, mathematical set theory (union and intersection) is used to extract the best features. The model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms: Bagging, Multilayer Perception, J48, and IBk. Our approach has resulted in 11 and 28 relevant features (out of 86) using the intersection and union, respectively, on IoTID20 and resulted 15 and 25 relevant features (out of 41) using the intersection and union, respectively, on NSL-KDD. We have further compared our approach with other state-of-the-art studies. The comparison reveals that our model is superior and competent, scoring a very high 99.98% classification accuracy.

Alauthman M., Aslam N., Al-kasassbeh M., Khan S., Al-Qerem A., Raymond Choo K.

The use of bot malware and botnets as a tool to facilitate other malicious cyber activities (e.g. distributed denial of service attacks, dissemination of malware and spam, and click fraud). However, detection of botnets, particularly peer-to-peer (P2P) botnets, is challenging. Hence, in this paper we propose a sophisticated traffic reduction mechanism, integrated with a reinforcement learning technique. We then evaluate the proposed approach using real-world network traffic, and achieve a detection rate of 98.3%. The approach also achieves a relatively low false positive rate (i.e. 0.012%).

Maayah B., Moussaoui A., Bushnaq S., Abu Arqub O.

Abstract COVID-19, a novel coronavirus disease, is still causing concern all over the world. Recently, researchers have been concentrating their efforts on understanding the complex dynamics of this widespread illness. Mathematics plays a big role in understanding the mechanism of the spread of this disease by modeling it and trying to find approximate solutions. In this study, we implement a new technique for an approximation of the analytic series solution called the multistep Laplace optimized decomposition method for solving fractional nonlinear systems of ordinary differential equations. The proposed method is a combination of the multistep method, the Laplace transform, and the optimized decomposition method. To show the ability and effectiveness of this method, we chose the COVID-19 model to apply the proposed technique to it. To develop the model, the Caputo-type fractional-order derivative is employed. The suggested algorithm efficacy is assessed using the fourth-order Runge-Kutta method, and when compared to it, the results show that the proposed approach has a high level of accuracy. Several representative graphs are displayed and analyzed in two dimensions to show the growth and decay in the model concerning the fractional parameter α values. The central processing unit computational time cost in finding graphical results is utilized and tabulated. From a numerical viewpoint, the archived simulations and results justify that the proposed iterative algorithm is a straightforward and appropriate tool with computational efficiency for several coronavirus disease differential model solutions.

Alshurideh M.T., Al Kurdi B., AlHamad A.Q., Salloum S.A., Alkurdi S., Dehghan A., Abuhashesh M., Masa’deh R.

Recent years have seen an increasingly widespread use of online learning technologies. This has prompted universities to make huge investments in technology to augment their position in the face of extensive competition and to enhance their students’ learning experience and efficiency. Numerous studies have been carried out regarding the use of online and mobile phone learning platforms. However, there are very few studies focusing on how university students will accept and adopt smartphones as a new platform for taking examinations. Many reasons, but most recently and importantly the COVID-19 pandemic, have prompted educational institutions to move toward using both online and mobile learning techniques. This study is a pioneer in examining the intention to use mobile exam platforms and the prerequisites of such intention. The purpose of this study is to expand the Technology Acceptance Model (TAM) by including four additional constructs: namely, content quality, service quality, information quality, and system quality. A self-survey method was prepared and carried out to obtain the necessary basic data. In total, 566 students from universities in the United Arab Emirates took part in this survey. Smart PLS was used to test the study constructs and the structural model. Results showed that all study hypotheses are supported and confirmed the effect of the TAM extension factors within the UAE higher education setting. These outcomes suggest that the policymakers and education developers should consider mobile exam platforms as a new assessment platform and a possible technological solution, especially when considering the distance learning concept. It is good to bear in mind that this study is initial and designed to explore using smartphones as a new platform for student examinations. Furthermore, mixed-method research is needed to check the effectiveness and the suitability of using the examination platforms, especially for postgraduate higher educational levels.

Aljarah I., Habib M., Faris H., Al-Madi N., Heidari A.A., Mafarja M., Elaziz M.A., Mirjalili S.

Developing intelligent analytical tools requires pre-processing data and finding relevant features that best reinforce the performance of the predictive algorithms. Feature selection plays a significant role in maximizing the accuracy of machine learning algorithms since the presence of redundant and irrelevant attributes deteriorates the performance of the learning process and increases its complexity. Feature selection is a combinatorial optimization problem that can be formulated as a multi-objective optimization problem with the purpose of maximizing the classification performance and minimizing the number of irrelevant features. It is considered an NP hard optimization problem since having a number of (n) features produces a large search space of size ( 2 n ) of different permutations of features. An eminent type of optimizer for tackling such an exhausting search process is evolutionary, which mimic evolutionary processes in nature to solve problems in computers. Salp Swarm Algorithm (SSA) is a well-established metaheuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating global optima for optimization problems. The objective of this article is to promote and boost the performance of the multi-objective SSA for feature selection. Therefore, it proposes an enhanced multi-objective SSA algorithm (MODSSA-lbest) that adopts two essential components: the dynamic time-varying strategy and local fittest solutions. These components assist the SSA algorithm in balancing exploration and exploitation. Thus, it converges faster while avoiding locally optimal solutions. The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets and compared with the well-regarded Multi-Objective Evolutionary Algorithms (MOEAs). The results show that the MODSSA-lbest achieves significantly promising results versus its counterpart algorithms.