Sustainable learning and education (SLE) is a relatively new ideology based on sustainability principles and developed in response to the United Nations’ recently proclaimed Sustainable Development Goals (SDGs). As a result, technologies should be adopted to equip educational institutions with the tools necessary to attain SLE. Recently, the coronavirus (COVID-19) pandemic has affected educational systems globally, leading them to embrace more innovative technological methods to meet academic demands while maintaining SLE principles. Mobile learning apps (MLA) refers to using the unique capabilities of mobile apps to engage and collaborate towards establishing robust online learning. However, the effectiveness of MLA depends on learners’ acceptance. Therefore, the purpose of this study is to investigate the factors that could affect MLA acceptance. In order to analyze the collected data from 415 Jordanian students among schools and universities, structural equation modeling (SEM) was used. The empirical findings confirm that perceived usefulness and perceived ease of use are significantly influenced by self-efficacy and perceived compatibility. Furthermore, perceived usefulness is significantly influenced by perceived convenience and perceived ease of use. Additionally, perceived enjoyment significantly influences the behavioral intention to use MLA. On the other hand, perceived compatibility has no significant influence on perceived enjoyment. Finally, perceived ease of use, perceived usefulness, and perceived compatibility have no significant effect on behavioral intention to use MLA. This study addresses a critical research gap in the distance learning acceptance literature by proposing an exhaustive model in the post-COVID-19 era that can help to improve students’ performance and outcomes in Jordanian schools and universities.

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Our social life and the way of people communicate are greatly affected by the social media technologies. The variety of stand-alone and built-in social media services such as Facebook, Twitter, LinkedIn, and alike facilitate users to create highly interactive platforms. However, these overwhelming technologies made us sank in an enormous amount of information. Recently, Facebook exposed data on 50 million Facebook unaware users for analytical purposes. Fake profiles are also used by Scammers to infiltrate networks of friends to wreak all sorts of havoc as stealing valuable information, financial fraud, or entering other user's social graph. In this paper, we turn our focus to Facebook fake profiles, and proposed a smart system (FBChecker) that enables users to check if any Facebook profile is fake. To achieve that, FBChecker utilizes the data mining approach to analyze and classify a set of behavioral and informational attributes provided in the personal profiles. Specifically, we empirically examine these attributes using four supervised data mining algorithms (e.g., k-NN, decision tree, SVM, and naïve Bayes) to determine how successfully we can recognize the fake profiles. To demonstrate the validity of our conceptual work, the selected classifiers have been implemented using RapidMiner data science platform with a dataset of 200 profiles collected from the authors' profile and a honeypot page. Two experiments are developed; in the first one, the k-NN schema is applied as an estimator model for imputation the missing data with substituted values, whereas in the second experiment a filtering operator is applied to exclude the profiles with missing values. Results showed high accuracy rate with the all classifiers, however, the SVM outperforms other classifiers with an accuracy rate of 98.0% followed by Naïve Bayes.Povzetek: Opisana je metoda iskanja lažnih profilov na Facebooku s pomočjo strojnega učenja.

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Facebook, the popular online social network, has changed our lives. Users can create a customized profile to share information about themselves with others that have agreed to be their 'friend'. However, this gigantic social network can be misused for carrying out malicious activities. Facebook faces the problem of fake accounts that enable scammers to violate users' privacy by creating fake profiles to infiltrate personal social networks. Many techniques have been proposed to address this issue. Most of them are based on detecting fake profiles/accounts, considering the characteristics of the user profile. However, the limited profile data made publicly available by Facebook makes it ineligible for applying the existing approaches in fake profile identification. Therefore, this research utilized data mining techniques to detect fake profiles. A set of supervised (ID3 decision tree, k-NN, and SVM) and unsupervised (k-Means and k-medoids) algorithms were applied to 12 behavioral and non-behavioral discriminative profile attributes from a dataset of 982 profiles. The results showed that ID3 had the highest accuracy in the detection process while kmedoids had the lowest accuracy.

Abstract-Recent years have witnessed a widespread availability of electronic healthcare data record (EHR) systems. Vast amounts of health data were generated in the process of treatment in medical centers such hospitals, clinics, or other institutions. To improve the quality of healthcare service, EHRs could be potentially shared by a variety of users. This results in significant privacy issues that should be addressed to make the use of EHR practical. In fact, despite the recent research in designing standards and regulations directives concerning security and privacy in EHR systems, it is still, however, not completely settled out the privacy challenges. In this paper, a systematic literature review was conducted concerning the privacy issues in electronic healthcare systems. More than 50 original articles were selected to study the existing security approaches and figure out the used security models. Also, a novel Context-aware Access Control Security Model (CARE) is proposed to capture the scenario of data interoperability and support the security fundamentals of healthcare systems along with the capability of providing fine-grained access control.

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