Application Of Confidence Range Algorithm In Recognizing User Behavior Through EPSB in Cloud Computing

Within the security scope, Authentication is considered as a core model to control accessing any system. Password is one of the most significant mechanisms which diagnose the authorized user from others. However, it is facing many problems such as spoofing and man in the middle attack(MitMA). When unauthorized user has got the correct password. Then, this user would be able to access into the data and change previous password which causes significant loss in efforts and cost. Similarly, the hacker "who don't have a password" is also trying to penetrate the system through predicted a set of words. In fact, both of authorized and hacker users work to input a wrong password, but authorized user may have only one or two wrong characters while the hacker inputs a whole wrong password. The aim of this paper, established an algorithm under the name of "Confidence Range ". The main tasks of this algorithm are monitoring all the activities which associated with the password on time, error, and style to the authorized user to recognize any suspicious activity. For that reason, a unique EPSB,” Electronic Personal Synthesis Behavior”, has been generated to the authorized user by the application of confidence range algorithm

A Systematic Review of Metamodelling in Software Engineering

Metamodelling has become a crucial technique to handle the complexity issues in the software development industry. This paper critically reviews and systematically classifies the recent metamodelling approaches to show their current status, limitations, and future trends. This systematic review retrieved and analyzed a total of 1157 research studies published on the topic of metamodelling. The retrieved studies were then critically examined to meet the inclusion and exclusion criteria, in which 69 studies were finally nominated for further critical analysis. The results showed that the main application domains of metamodelling are the cyber-physical and safety-critical systems development. Moreover, the majority of used approaches include metamodels formalization, adding spatial and time semantics, and considering nonfunctional properties. Further, the main trends of metamodelling development include the support of complex systems, behavior modeling, and multilevel modeling. The results of this systematic review would provide insights for scholars and software engineering practitioners looking into the state-of-the-art of metamodelling and assist them in improving their approaches

Model of security level classification for data in hybrid cloud computing

Organizations mainly rely on data and the mechanism of dealing with that data on cloud computing. Data in an organization has multi security levels, which is classified depending on nature of the data, and the impact of data on the organization. The security procedures which used for protecting data usually be complicated, and it had a direct and indirect influence on the usability level. This study aims to establish a model which has an ability to classify data dynamically according to the security form low till high levels. The security level classified it into five levels based on the policies and classification method. The purpose of classification is to apply a complex security procedure on data which has a high security level larger than data which has a low security level. It also has a potential to segregation an illegal data from the legal to support usability in system. Finally, several experiments have been conducted to evaluate the proposed approaches. Several experiments have been performed to empirically evaluate two feature selection methods (Chi-square (χ2), information gain (IG)) and five classification methods (decision tree classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) and meta-classifier combination) for Legal Documents Filtering The results show that all classifiers perform better with the information gain feature selection methods than their results with Chi-Square feature selection method. Results also show that Support Vector Machine (SVM) outperforms achieve the best results among all individual classifiers. However, the proposed meta-classifiers method achieves the best results among all classification approaches

Academics’ Awareness towards Mobile Learning in Oman

Mobile Learning (M-Learning) witnessed a booming evolution contributed to the evolvement of learning within the higher educational settings. M-Learning enhances the collaboration between the educators and their students on anytime anywhere settings. Attitudes towards the utilization of M-Learning help the decision makers in building the required infrastructure. Our literature review indicated that there are very few studies that address the issue of educators’ awareness towards M-Learning which is a very significant factor for the success of smart education. In this study, a questionnaire survey has been administrated at Al Buraimi University College, Oman in order to examine the educators’ awareness and attitudes towards the utilization of M-Learning. Findings revealed that female instructors were more positive than males towards the utilization of M-Learning. Moreover, results revealed that academics who indicated that mobile technology is an effective tool in education were highly optimistic towards the utilization of M-Learning than those who were not. Nevertheless, results were not pointed any significant differences with regard to the academics’ perceptions towards the utilization of M-Learning in relation to age and academic qualifications factors, showing that M-learning can be embraced by all academics irrespective of the age and qualifications factors

Extending the technology acceptance model with knowledge management factors to examine the acceptance of mobile learning

In today’s technological era, Mobile learning (M-learning) has become an essential tool that enables the students to access the learning materials on anytime anywhere settings. Determining the factors that affect the acceptance of M-learning is still one of the ongoing and critical issues by Information System (IS) scholars. The Technology Acceptance Model (TAM) has witnessed a lot of modifications and enhancements, which in turn contribute to the identification of the factors that affect the M-learning acceptance. Extending the TAM with other factors is still an open door for IS scholars to further examine the M-learning acceptance. Additionally, Knowledge Management (KM) is regarded as an essential component for developing M-learning systems. Besides, it is crucial for enhancing the students’ learning abilities that KM factors should be incorporated in M-learning systems. Research shows that KM factors (knowledge acquisition, knowledge sharing, knowledge application, and knowledge protection) have a significant effect on the adoption and success of many ISs. However, research has overlooked the impact of KM factors on M-learning acceptance. In line with this issue, the research objectives of this study are threefold. First, to analyze the students’ perceptions towards the integration of KM factors in M-learning systems through a preliminary study. Our research problem was motivated by the analysis of the preliminary study results, in which 93% of the students indicated that they would use the M-learning system in their studies if KM factors would be taken into consideration. Second, to develop a new model by extending the TAM with the KM factors as external variables. In that, it is suggested that the two main constructs of TAM (i.e., perceived usefulness and perceived ease of use) are affected by the four KM factors. Besides, the behavioral intention to use is suggested to be influenced by the two main constructs of TAM, whereas the behavioral intention itself is assumed to affect the actual system use. Third, to validate the proposed model through the development of M-learning application and the use of statistical analyses methods. This study employs the Partial Least Squares-Structural Equation Modeling (PLS-SEM) to validate the developed model. Data were collected through a questionnaire survey from 735 IT undergraduate students in two different universities in two different countries, namely Universiti Malaysia Pahang (UMP) in Malaysia and Al Buraimi University College (BUC) in Oman. The selection of these two samples is attributed to the intention to validate the developed model in a cross-cultural setting. The results suggest that knowledge acquisition, application, and protection have a positive effect on perceived ease of use and perceived usefulness of M-learning systems in both samples. However, knowledge sharing was found to be partially supported in both samples. Furthermore, perceived usefulness and perceived ease of use were found to be significant determinants of the behavioral intention to use M-learning systems. More interesting, the developed model explains a substantial variance (50%) in the actual use of M-learning systems in both samples, which clearly shows that the developed structural model is sound and valid, and hence, it could provide a plentiful explanation of the actual use of M-learning systems. The results of this study contribute to the existing literature by validating and extending the TAM with the KM factors in two different contexts (i.e., UMP and BUC) and provide various implications to the theory, research, and practice

Students’ perceptions towards the integration of knowledge management processes in M-Learning Systems: A preliminary study

It is highly important for the higher educational institutions to understand the factors that affect the students’ acceptance of Mobile Learning (M-learning) systems as a prior step to the implementation of such systems. From the M-learningperspective, the literature ignores some f actors that could contribute to improve the knowledge acquisition, sharing, application, and protection, and how such factors could affect the M-learning acceptance. From the Knowledge Management (KM) perspective, research shows that KM processes have a positive impact on the acceptance an implementation of many Information Systems (IS). We observed that the existing literature overlooks the impact of KM processes on M-learning acceptance. Accordingly, the main objective of this study is to analyse the students’ perceptions towards the integration of KM processes in M-learning systems. A mixed method (questionnaire and interviews) was used for data collection. An online survey has been sent to IT undergraduate students in two different universities in two different regions namely, Universiti Malaysia Pahang (UMP) in Malaysia and Al Buraimi University College (BUC) in Oman. 305 students responded to the survey. Results indicated that around 74% of students reported that it is interesting to incorporate KM processes in M-learning systems. Additionally, results pointed out that 93% of the students indicated that they would use the M-learning system in their studies if all KM processes will be taken into consideration. It is imperative that these results will assist the M-learning systems developers to take these factors into their consideration while designing and developing such systems. Furthermore, educators may need to elaborate their teaching strategies in a way that suits ths use of these systems and meets the students’ needs

A systematic review of social media acceptance from the perspective of educational and information systems theories and models

The study of social media acceptance and adoption is not a new research topic. However, the analysis of the educational and information systems (IS) theories/models that are used to examine the social media acceptance and adoption is considered an important research direction. To examine these theories/models and provide researchers with a clear vision of this research topic, we should be aware of the leading educational and IS theories/models used in this line of research. To this end, this systematic review retrieved and analyzed 2,382 articles. The retrieved articles were then critically examined to meet the inclusion and exclusion criteria, in which 122 articles published between 2009 and 2018 were eventually selected for further critical analysis. The main findings indicated that the uses and gratifications theory (U&G) and the social constructivism theory were considered the most widely used educational theories in social media. Besides, the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) were considered the most extensively used IS models in studying the social media acceptance and adoption. These results afford a better understanding of social media studies related to the educational and IS theories/models and form a constructive reference for future research

Mining Social Media Text: Extracting Knowledge from Facebook

Social media websites allow users to communicate with each other through several tools like chats, discussion forums, comments etc. This results in learning and sharing of important information among the users. The nature of information on such social networking websites can be straight forward categorized as unstructured and fuzzy. In regular day-to-day discussions, spellings, grammar and sentence structure are usually neglected. This may prompt various sorts of ambiguities, for example, lexical, syntactic, and semantic, which makes it difficult to analyse and extract data patterns from such datasets. This study aims at analyzing textual data from Facebook and attempts to find interesting knowledge from such data and represent it in different forms. 33815 posts from 16 news channels pages over Facebook were extracted and analyzed. Different text mining techniques were applied on the collected data. Findings indicated that Fox news is the most news channel that share posts on Facebook, followed by CNN and ABC News respectively. Results revealed that the most frequent linked words are focused on the USA elections. Moreover, results revealed that most of the people are highly interested in sharing the news of Mohammed Ali Clay through all the news channels. Other implications and future perspectives are presented within the study

Attitudes Towards the Use of Mobile Learning: A Case Study from the Gulf Region

In the last few years, the way we learn has been shifted dramatically from traditional classrooms depending on printed papers into E-learning depending on digital pages. Mobile learning (M-learning) is a recent technology that has been developed rapidly to deliver E-learning using personal mobile devices without posing any restrictions on time and location. In this work, we investigate students and faculty members’ attitudes towards the use of M-learning in higher educational institutions within two countries in the Gulf Region (Oman & UAE). Two questionnaire surveys have been conducted: one for students and another for faculty members. In these surveys, 383 students and 54 instructors have taken part within the study. An independent sample t-test was performed to examine whether there exist a significant difference among the students’ attitudes and the faculty members’ attitudes towards the use of M-learning with regard to gender and country. Results indicated that students in the UAE were more positive towards the use of M-learning than those in Oman. Moreover, results revealed that 99% of the students own mobile devices, in particular smartphones and tablets, while only 1% has not. Results of this study could help policy makers for better decision making in building the M-learning infrastructure in the higher educational institutions in general and specifically within the Arab Gulf region