The composition of cloud services plays a vital role in optimizing resource allocation, load balancing, task scheduling, and energy management. However, it remains a significant challenge due to the dynamic nature of workloads and the variability in resource demands, where addressing these challenges is essential for ensuring seamless service delivery. This research investigated the implementation of the Cuckoo Optimization Algorithm (COA) in a cloud computing environment to optimize service composition. In the proposed approach, each service was treated as an egg, where high-demand services represented the host’s original eggs, while low-demand services represented the cuckoo bird’s eggs that competed for the same resources. This implementation enabled the algorithm to balance workloads dynamically and allocate resources efficiently while optimizing load balancing, task scheduling, cost reduction, processing and response times, system stability, and energy management. The simulations were conducted using CloudSim 5.0, and the results were compared with the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms across key performance metrics. Experimental results clearly demonstrate that the COA outperformed both PSO and ACO across all evaluated metrics. The COA achieved higher efficiency in task scheduling, dynamic load balancing, and energy-aware resource allocation. It consistently maintained lower operational costs, reduced SLA violations, and achieved superior task completion and VM utilization rates. These findings underscore the COA’s potential as a robust and scalable approach for optimizing cloud service composition in dynamic and resource-constrained environments.

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge Systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge Systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge Systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge Systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge Systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults.

The Covid-19 vaccination process faced many problems; "who gets vaccinated" and "where is the nearest vaccination center" are two questions that entail a multi-Criteria problem. Such problems arise in the most inopportune time of the Covid-19 pandemic. The matching process depended on many variables: the current health state of the person, insurance company, location, and residency. Such variables are not static and differ from one country to another. Hence, this paper presents a new Multi-Criteria Hybrid Matching Method (MCHMM). MCHMM is a hybrid matching method extended from three multi-Criteria decision-making methods. MCHMM uses the strengths of three methods: Analytic Hierarchy Process (AHP), and Gale-Shapley algorithm. To fully automate the decision-making process of matching the MCHMM, which relies on CRITIC to develop the weight of Criteria, the weights are then used in AHP to develop the preference list of the Gale-Shapley algorithm (GS). Then the GS algorithm will produce the matched sets, eliminating the human factor and interference like favoritism in deciding. Such elimination is essential to reduce long waiting queues, proclivities, tendencies, biases, preconceptions, prejudices, and predispositions. Hence, reason and logic will guide the decision process, ridding it of impurities. MCHMM will match the person with the vaccination center most suited to her/his case, not based on distance only but also based on the Criteria s/he chooses. MCHMM is a unique hybrid algorithm that solves a multi-Criteria problem taking advantage of other algorithms. Furthermore, MCHMM can be generalized and can be used in any similar situation. Keywords : Covid-19, Gale-Shapley Algorithm, Analytic Hierarchy Process, Criteria Importance through Inter Criteria Correlation, Multi-Criteria Hybrid Matching Method DOI: https://doi.org/10.35741/issn.0258-2724.58.3.29

E-learning results from the integration of technology and education and has become an effective learning medium today. E-learning courses and systems with various services are on the rise owing to its importance. E-learning systems should be evaluated to assure successful delivery, effective usage, and positive impacts on learners. A holistic model that identifies various levels of success on a vast range of success determinants was proposed. The model was empirically validated using data obtained from 724 e-learning student users in Jordan. Structural Equation Modelling (SEM) was used in data analyses. Results showed that perceived usefulness of information systems, user training, system quality, and management support have positive effects on user’s behavioral intention; whereas perceived ease of use has not. Also, SEM displayed that user behavioral intention has a positive effect on information systems use, use on student satisfaction, and the latter on student loyalty. Machine Learning (ML) methods produce high correlation values reaching up to 80% in predicting Behavior Intention (BI) from the input factors, and student loyalty from student satisfaction factors. This indicates that the ML are promising techniques to forecast the future targets based on the input independent features.

This study investigates the factors that influence the sharing of information on social media platforms and examines the effects of perceived security, perceived privacy, and user awareness on users' trust in social media platforms, as well as the moderating effects of age, gender, educational attainment, and internet proficiency on information sharing. The study collected data from 837 social media users in Jordan and analyzed them using structural equation modeling (SEM), confirmatory factor analysis (CFA), and machine learning (ML) methods. The findings of the study indicate that perceived security, perceived privacy, and user awareness all have a significant impact on users' trust in social media platforms. Trust, in turn, has a significant impact on the amount of information shared on these platforms. Also, the findings of this study provide valuable insights into the dynamics of information sharing on social networks. This knowledge will be of interest to managers, policymakers, and developers of social media platforms. In addition, the findings of the study also have implications for the privacy and security of social media users. For example, social media users can be more careful about the information they share on social media platforms, and they can take steps to protect their privacy.

The expression "social media" refers to a software-based platform developed for users’ benefit. People use it to gain social power, market their products, conduct online business, and share information and ideas. This digital ecosystem has become helpful in various ways, but research indicates that it does not come for free. Addiction, depression, and anxiety are some of the adverse conditions discussed in many studies. The purpose of this study is to mark if there is a relationship between using social media networks and the numbering of people with anxiety or depression. Also, by addressing the need to learn more about what makes people use social networks and how that use affects anxiety and depression in Arabic-speaking users in Jordan, we can help people from different cultures understand each other better. This research uses TAM, telepresence, and survey data from 1050 people, mainly from Jordan. The research looks at how the usage of social media is related to supposed usefulness, supposed ease of use, trust, social influence, age, gender, level of education, marital status, the time spent on the internet, preferred social media network, and perceived usefulness of SNS. AMOS 20 methods of confirmatory factor analysis (CFA), structural equation modeling (SEM), and machine learning (ML), such as SMO, ANN, random forest, and the bagging reduced error pruning tree (RepTree), were used to test the proposed model hypotheses. According to the results, the researchers found high correlations between social network usage and depression and anxiety. The use of social networking sites is also affected by how useful they are seen to be, how easy they are to use, trust, social influence, and telepresence. Also, the moderator's age, gender, level of education, marital status, amount of time spent on the internet, experience with the internet, and favorite social networks all affect how they plan to use social networks.

Mobile banking is a service provided by a bank that allows full remote control of customers’ financial data and transactions with a variety of options to serve their needs. With m-banking, the banks can cut down on operational costs whilst maintaining client satisfaction. This research examined the most crucial factors that could predict the Jordanian customer’s continued intention toward the use of m-banking. Following the proposed model, the research was conducted by using a self-conducted questionnaire and the responses were collected electronically from a convenience sample of 403 Jordanian customers of m-banking through social networks. The suggested model was adapted from the theory of planned behavior (TPB), the unified theory of acceptance and use of technology (UTAUT), and the technology acceptance model (TAM). The research model was further expanded by considering the factors of service quality and moderating factors (age, gender, educational level, and Internet experience). The collected data of customers were analyzed, validated, and verified by using a structural equation modeling (SME) approach including a confirmatory factor analysis (CFA), in addition to machine learning (ML) methods, artificial neural network (ANN), support vector machine (SMO), bagging reduced error pruning tree (RepTree), and random forest. Results showed that effort expectancy, performance expectancy, perceived risk, perceived trust, social influence, and service quality impacted behavioral intention, whereas facilitating conditions did not. Furthermore, behavioral intention impacted upon word of mouth and facilitating conditions (the latter regarding the continued intention to use m-banking), and had the highest coefficient value. Results also confirmed that all moderating factors affect the behavioral intention to continue using m-banking applications.

Previous research has found support for depression and anxiety associated with social networks. However, little research has explored parents’ depression and anxiety constructs as mediators that may account for children’s depression and anxiety. The purpose of this paper is to test the influence of different factors on children’s depression and anxiety, extending from parents’ anxiety and depression in Jordan. The authors recruited 857 parents to complete relevant web survey measures with constructs and items and a model based on different research models TAM and extended with trust, analyzed using SEM, CFA with SPSS and AMOS, and ML methods, using the triangulation method to validate the results and help predict future applications. The authors found support for the structural model whereby behavioral intention to use social media influences the parent’s anxiety and depression which correlate to their offspring’s anxiety and depression. Behavioral intention to use social media can be enticed by enjoyment, trust, ease of use, usefulness, and social influences. This study is unique in exploring rumination in the context of the relationship between parent–child anxiety and depression due to the use of social networks.

Using mobile applications in e-government for the purpose of health protection is a new idea during COVID-19 epidemic. Hence, the goal of this study is to examine the various factors that influence the use of SANAD App As a health protection tool. The factors were adopted from well-established models like UTAUT, TAM, and extended PBT. Using survey data from 442 SANAD App from Jordan, the model was empirically validated using AMOS 20 confirmatory factor analysis, structural equation modeling (SEM) and machine learning (ML) methods were performed to assess the study hypotheses. The ML methods used are ANN, SMO, the bagging reduced error pruning tree (RepTree), and random forest. The results suggested several key findings: the respondents' performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, trust, and perceived service quality of this digital technology were significant antecedents for their attitude to using it. The strength of these relationships is affected by the moderating variables, including age, gender, educational level, and internet experience on behavioral intention. Yet, perceived risk did not have a significant effect on attitude towards SANAD App The study adds to literature by empirically testing and theorizing the effects of SANAD App on public health protection.

YouTube usage as a learning tool is evident among students. Hence, the goal of this study is to examine the various factors that influence the use of YouTube as a learning tool, which influences academic achievement in a bilingual academic context. Using survey data from 704 YouTube users from Jordan’s bilingual academic institutes, the research model was empirically validated. Using Amos 20, structural equation modeling (SEM) was performed to assess the study hypotheses. SEM permits concurrent checking of the direct and indirect effects of all hypotheses. Confirmatory factor analysis (CFA) was used to validate the instrument items’ properties in addition to machine learning methods: ANN, SMO, the bagging reduced error pruning tree (RepTree), and random forest. The empirical results offer several key findings: academic achievement (AA) is influenced by the information adoption (IA) of YouTube as a learning tool. Information adoption (IA) is influenced by information usefulness (IU). Source credibility (SC) and information quality (IQ) both influence information usefulness (IU), while information language (IL) does not. Information quality (IQ) is influenced by intrinsic, contextual, and accessibility information quality. This study adds to the literature by empirically testing and theorizing the effects of YouTube as a learning tool on the academic achievement of Jordanian university students who are studying in bilingual surroundings.

The purpose of this research paper is to identify and test the factors influencing the perceived usefulness and perceived effectiveness of adopting an e-learning system from the perspective of teachers in public and private schools as well as the United Nations Relief and Works Agency for Palestinian Refugees in the Near East (UNRWA) in Jordan during the first wave of the COVID-19 pandemic in the academic year 2019/2020. Based on the findings and best practices, the study intends to make appropriate recommendations to decision-makers. Its significance stems from the use of scientific tools of research and investigation, and it aims to ensure the quality and effectiveness of Jordanian schools’ e-learning systems. The study’s hypotheses were verified by electronically collecting 551 questionnaires from teachers in Jordan. To test the study hypotheses, the empirical validity of the research model was set up, and the data were analyzed with SPSS version 21.0. Structural equation modeling (SEM), confirmatory factor analysis (CFA), and machine learning (ML) methods were used to test the study hypotheses and validate the properties of the instrument items. Nineteen variables and one mediating variable were studied. The study found that independent variables pertaining to technology (relative advantage, compatibility, top management support, communication technologies, competitive pressure, technology competence, information intensity, and work flexibility) and moderating variables pertaining to the teacher’s personal income and those pertaining to school (school size, education program, and work sector) had a positive effect on teachers’ perceived usefulness of adopting e-learning systems during the COVID-19 pandemic. On the other hand, independent variables pertaining to technology (complexity and collaboration technology), moderating variables pertaining to the teacher (age, education level, and gender), and moderating variables pertaining to school (educational stage, number of students) were not supported.

Smartphone addiction has become a major problem for everyone. According to recent studies, a considerable number of children and adolescents are more attracted to smartphones and exhibit addictive behavioral indicators, which are emerging as serious social problems. The main goal of this study is to identify the determinants that influence children’s smartphone addiction and social isolation among children and adolescents in Jordan. The theoretical foundation of this study model is based on constructs adopted from the Technology Acceptance Model (TAM) (i.e., perceived ease of use and perceived usefulness), with social influence and trust adopted from the TAM extended model along with perceived enjoyment. In terms of methodology, the study uses data from 511 parents who responded via convenient sampling, and the data was collected via a survey questionnaire and used to evaluate the research model. To test the study hypotheses, the empirical validity of the research model was set up, and the data were analyzed with SPSS version 21.0 and AMOS 26 software. Structural equation modeling (SEM), confirmatory factor analysis (CFA), and machine learning (ML) methods were used to test the study hypotheses and validate the properties of the instrument items. The ML methods used are support vector machine (SMO), the bagging reduced error pruning tree (REPTree), artificial neural network (ANN), and random forest. Several major findings were indicated by the results: perceived usefulness, trust, and social influence were significant antecedent behavioral intentions to use the smartphone. Also, findings prove that behavioral intention is statistically supported to have a significant influence on smartphone addiction. Furthermore, the findings confirm that smartphone addiction positively influences social isolation among Jordanian children and adolescents. Yet, perceived ease of use and perceived enjoyment did not have a significant effect on behavioral intention to use the smartphone among Jordanian children and adolescents. The research contributes to the body of knowledge and literature by empirically examining and theorizing the implications of smartphone addiction on social isolation. Further details of the study contribution, as well as research future directions and limitations, are presented in the discussion section.

Web service composition allows developers to create and deploy applications that take advantage of the capabilities of service-oriented computing. Such applications provide the developers with reusability opportunities as well as seamless access to a wide range of services that provide simple and complex tasks to meet the clients’ requests in accordance with the service-level agreement (SLA) requirements. Web service composition issues have been addressed as a significant area of research to select the right web services that provide the expected quality of service (QoS) and attain the clients’ SLA. The proposed model enhances the processes of web service selection and composition by minimizing the number of integrated Web Services, using the Multistage Forward Search (MSF). In addition, the proposed model uses the Spider Monkey Optimization (SMO) algorithm, which improves the services provided with regards to fundamentals of service composition methods symmetry and variations. It achieves that by minimizing the response time of the service compositions by employing the Load Balancer to distribute the workload. It finds the right balance between the Virtual Machines (VM) resources, processing capacity, and the services composition capabilities. Furthermore, it enhances the resource utilization of Web Services and optimizes the resources’ reusability effectively and efficiently. The experimental results will be compared with the composition results of the Smart Multistage Forward Search (SMFS) technique to prove the superiority, robustness, and effectiveness of the proposed model. The experimental results show that the proposed SMO model decreases the service composition construction time by 40.4%, compared to the composition time required by the SMFS technique. The experimental results also show that SMO increases the number of integrated ted web services in the service composition by 11.7%, in comparison with the results of the SMFS technique. In addition, the dynamic behavior of the SMO improves the proposed model’s throughput where the average number of the requests that the service compositions processed successfully increased by 1.25% compared to the throughput of the SMFS technique. Furthermore, the proposed model decreases the service compositions’ response time by 0.25 s, 0.69 s, and 5.35 s for the Excellent, Good, and Poor classes respectively compared to the results of the SMFS Service composition response times related to the same classes.

E-textbooks are becoming increasingly important in the learning and teaching environments as the globe shifts to online learning. The key topic is what elements influence students' behavioral desire to use e-textbooks, and how the whole operation affects academic achievement when using e-textbooks. This research aims to investigate the various factors that influence the behavioral intention to use an e-textbook, which in turn influences academic achievement in a bilingual academic environment. The research model was empirically validated using survey data from 625 e-textbook users from bilingual academic institutes from Jordan. Structural equation modeling (SEM) analysis was employed to test the research hypotheses by using Amos 20. To validate the results, artificial intelligence (AI) was employed via five machine learning (ML) techniques: artificial neural network (ANN), linear regression, and sequential minimal optimization algorithm for support vector machine (SMO), bagging with REFTree model, and random forest. The empirical results offer several key findings. First, the behavioral intention of using an e-textbook positively influences academic achievement. Second, attitude toward e-textbooks, subjective norms toward e-textbooks, and perceived behavior control toward e-textbooks positively influence behavioral intention toward using e-textbooks. Attitude toward using e-textbooks and perceived behavioral control both are positively influenced by independent factors. This study contributes to the literature by theorizing and empirically testing the impacts of e-textbooks on the academic achievement of university students in a bilingual environment in Jordan.

Service Oriented Architecture (SOA) is a style of software design where Web Services (WS) provide services to the other components through a communication protocol over a network. WS components are managed, updated, and rearranged at runtime to provide the business processes as SCs, which consist of a set of WSs that can be invoked in a specific order to fulfill the clients' requests. According to the Service Level Agreement (SLA) requirements, WS selection and composition are significant perspectives of research to meet the clients' expectations. This paper presents an effective technique using SMFS that attempts to improve the WS selection as well as SC construction and ultimately optimize the WS resource utilization. The results show that the proposed SMFS technique enhances the WS resource utilization by 9.6% compared to the standard Multistage Forward Search (MFS) technique. Similarly, the number of constructed SCs using the proposed SMFS technique are increased by 36.97% compared to the number of constructed SCs with the standard MFS technique.

Service Oriented Architecture (SOA) introduced the web services as distributed computing components that can be independently deployed and invoked by other services or software to execute composite services that represent an end-to-end business process. An important problem in the SOA is the web service selection and composition problem which involves selecting the right web services mix to construct the best service composition that achieves the consumers Service Level Agreement (SLA) requirements. In this chapter we propose different methods to solve the problem of the business processes execution engine web service selection and services composition construction in the Service Oriented Architecture (SOA). The chapter provides different mechanisms to improve the web services selection and composition using dynamic techniques. The main goal is to satisfy the services' requirements expressed as the SLA and to enhance the services selection and composition by increasing the availability and decreasing the response time of the service compositions.

Service-oriented architecture (SOA) has emerged as a flexible software design style. SOA focuses on the development, use, and reuse of small, self-contained, independent blocks of code called web services that communicate over the network to perform a certain set of simple tasks. Web services are integrated as composite services to offer complex tasks and to provide the expected services and behavior in addition to fulfilling the clients’ requests according to the service-level agreement (SLA). Web service selection and composition problems have been a significant area of research to provide the expected quality of service (QoS) and to meet the clients’ expectations. This research paper presents a hybrid web service composition model to solve web service selection and composition problems and to optimize web services’ resource utilization using k-means clustering and knapsack algorithms. The proposed model aims to maximize the service compositions’ QoS and minimize the number of web services integrated within the service composition using the knapsack algorithm. Additionally, this paper aims to track the service compositions’ QoS attributes by evaluating and tracking the web services’ QoS using the reward function and, accordingly, use the k-means algorithm to decide to which cluster the web service belongs. The experimental results on a real dataset show the superiority and effectiveness of the proposed algorithm in comparison with the results of the state–action–reward–state–action (SARSA) and multistage forward search (MFS) algorithms. The experimental results show that the proposed model reduces the average time of the web service selection and composition processes to 37.02 s in comparison to 47.03 s for the SARSA algorithm and 42.72 s for the MFS algorithm. Furthermore, the average of web services’ resource utilization results increased by 4.68% using the proposed model in comparison to the resource utilization by the SARSA and MFS algorithms. In addition, the experimental results showed that the average number of service compositions using the proposed model improved by 26.04% compared with the SARSA and MFS algorithms.

The use of information and communication technology( ICTs) in the public sector is one of the most important effects were caused by IT .Nowadays, with the development of e-government, set of information are available about the public sector in electronic databases Significantly, that may be agree with each other . Public sector information is considered the most important thing in e-government environment ,it has a great value. (Shao & Wang, 2010). Moreover, The government environment become is using the Internet and information technology, mainly on their interactions with businesses and citizens, and among themselves. One of the e-Government initiatives is the development of the government-to-business (G2B) system, which is an agenda sets to promote a higher service quality between government entities and the business sector (Dong, Xiong & Han, 2010).This paper aims to discover the importance of the government's small and medium enterprises under the umbrella of e-services G2B. G2B transactions include various services exchanged between the government and the business community, including the deployment of policies and memos, rules and regulations. It includes commercial services provided access to current business information, application forms are loaded, renewal of licenses, registration of companies, to obtain permits, pay taxes, e-Procurement service ERAs (slaves and Abu Shanab, 2010).

Although Technology-Organization-Environment framework has been commonly used by several researchers for organizational adoption of digital business technologies, there is a lack of academic research tackled the Technology-Organization-Environment in developing countries. This article attempts to fill this gap by investigating the relations among several Technology-Organization-Environment variables at Aqaba five star hotels located in Jordan namely relative advantage, complexity, compatibility, top management support, firm size, technology competence, competitive pressure, critical mass, information intensity, age, gender, educational level, personal income, and work position in enhancing perceived usefulness, and the latter on continuous intention to use mobile hotel reservation system of five star hotels in Aqaba, Jordan. An empirical study among 390 usable responses containing 36 items was analyzed using multiple regression analysis and machine learning techniques to test the research hypotheses. Results expose a positive effect of relative advantage, information intensity, gender, age, and personal income on perceived usefulness, and the latter on continuous intention to use mobile hotel reservation system. This research will enable decision makers to identify which variables should be emphasized in order to impact hotels’ perceived usefulness of adopting mobile hotel reservation system, and in turn on continuous intention to use mobile hotel reservation system in service companies, especially in the hotel sector.