A Deterministic Algorithm for Arabic Character Recognition Based on Letter Properties

Handheld devices are flooding the market, and their use is becoming essential among people. Hence, the need for fast and accurate character recognition methods that ease the data entry process for users arises. There are many methods developed for handwriting character recognition especially for Latin-based languages. On the other hand, character recognition methods for Arabic language are lacking and rare. The Arabic language has many traits that differentiate it from other languages: first, the writing process is from right to left; second, the letter changes shape according to the position in the work; and third, the writing is cursive. Such traits compel to produce a special character recognition method that helps in producing applications for Arabic language. This research proposes a deterministic algorithm that recognizes Arabic alphabet letters. The algorithm is based on four categorizations of Arabic alphabet letters. Then, the research suggested a deterministic algorithm composed of 34 rules that can predict the character based on the use of all of categorizations as attributes assembled in a matrix for this purpose

Cyber Security Body of Knowledge and Curricula Development

The cyber world is an ever-changing world and cyber security is most important and touches the lives of everyone on the cyber world including researchers, students, businesses, academia, and novice user. The chapter suggests a body of knowledge that incorporates the view of academia as well as practitioners. This research attempts to put basic step and a framework for cyber security body of knowledge and to allow practitioners and academicians to face the problem of lack of standardization. Furthermore, the chapter attempts to bridge the gap between the different audiences. The gap is so broad that the term of cyber security is not agreed upon even in spelling. The suggested body of knowledge may not be perfect, yet it is a step forward

Depression and anxiety in social media: Jordan case study

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

Evaluating the influence of security considerations on information dissemination via social networks

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

Continued Intention to Use of M-Banking in Jordan by Integrating UTAUT, TPB, TAM and Service Quality with ML

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

Continued intention to use of m-banking in Jordan by integrating UTAUT, TPB, TAM and service quality with ML

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

Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM

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

Predictors of Smartphone Addiction and Social Isolation among Jordanian Children and Adolescents Using SEM and ML

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

Predictors of Smartphone Addiction and Social Isolation among Jordanian Children and Adolescents Using SEM and ML

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

An Empirical Study of Factors Influencing the Perceived Usefulness and Effectiveness of Integrating E-Learning Systems during the COVID-19 Pandemic Using SEM and ML: A Case Study in Jordan

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