In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for finding frequent patterns and confidence for finding association rules. A shortcoming of confidence is that it does not capture the correlation that exists between the left-hand side (LHS) and the right-hand side (RHS) of an association rule. On the other hand, the interestingness measure lift captures such as correlation in the sense that it tells us whether the LHS influences the RHS positively or negatively. Therefore, using Lift instead of confidence as a criteria for discovering association rules can be more effective. It also gives the user more choices in determining the kind of association rules to be discovered. This in turn helps to narrow down the search space and consequently, improves performance. In this paper, we describe a new approach for discovering association rules that is based on Lift and not based on confidence.

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Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field.

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The aim of this research is to study the acceptance of university students to use Microsoft Teams e-Learning system and their intention to use it as a Learning Management System (LMS) for education during the COVID-19 pandemic in Jordan. An ex-tended Technology Acceptance Model (TAM) with a blend of external factors that are used together for the first time was developed and used for the purpose of this study. TAM was used because of its wide use and success during the past few years for evaluating the influence of different factors affecting the acceptance and intention to use e-Learning platforms within educational institutes. However, all the studies were examining the variables and factors affecting the behavioral intention and acceptance to use LMSs when normal and conventional classroom study is available. In this research, seven external variables, in addition to the four TAM variables, were introduced in a model including one external variable, Internet Connectivity (IC), used for the first time in the field of education. A model is constructed by extending TAM with the introduced external variables, hypotheses are constructed and a questionnaire for 396 students at two universities in Jordan is conducted. Reliability, confirmatory factor, model fit, and hypothesized structural model analyses are presented. Results show that all the variables tested affect, either directly or indirectly, the acceptance and intention to use MS Teams during the pandemic. 21 hypotheses were tested between the constructs and found significant except the relations between (Social Norm - Perceived Usefulness) and (Technical Support - Perceived Usefulness).

Abstract-Many presentation these days are done with the help of a presentation tool. Lecturers at Universities and researchers in conferences use such tools to order the flow of the presentation and to help audiences follow the presentation points. Presenters control the presentation tools using mouse and keyboard which keep the presenters always beside the computer machine to be close enough to the keyboard and mouse. This reduces the ability of the lecturer to move close to the audiences and reduces the eye contact with them. Moreover, using such traditional techniques in controlling presentation tools lack the communication naturalness. Several gesture recognition tools are introduced as solutions for these problems. However, these tools require the user to learn specific gestures to control the presentation and/or the mouse. These specific gestures can be considered as a gestures vocabulary for the gesture recognition system. This paper introduces a gesture recognition system, TeachMe, which controls Microsoft PowerPoint presentation tool and the mouse pointer.TeachMe also has a gesture customization feature that allows the user to customize some gestures according to his/her preference. TeachMe uses Kinect device as an interface for capturing gestures. This paper, specifically, discusses in details the techniques and factors taken into consideration for implementing the system and its customization feature.

The adoption of new technologies in Jordanian Universities related to cloud services, shows differences in practices between faculty and staff members. Resistance to adoption may accrue by faculty and staff members who are accustomed and favoring old practices. A questionnaire was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model to identify factors that affect behavioral intentions that lead to the use of mobile cloud computing during the covid-19 pandemic, taking into consideration Work-type as the mediating factor. Five Jordanian Universities participated in this study, with a total response of 153 faculty and staff members. The conceptual proposed model was tested to ensure the fitness of the structural model for providing correct estimations. The collected sample was subjected to confirmatory factor analysis to ensure construct, convergent and discriminant validity. The results came positive in terms of composite reliability as they were above 0.70, for Average Variance Extracted (AVE) it came more than 0.05and Cronbach alpha exceeded 0.70. The results revealed the fitness of the proposed model to measure differences in behavioral intentions towards adopting mobile cloud services between faculty members and employees. Moreover, the results showed that work type had some interesting moderating impact on the tested relationships. Moreover, the results showed that there is a high Behavioral Intention (BI) between faculty and staff to use mobile cloud services and solutions within their workplace. In addition, the results showed some inequalities of the behavioral intention toward the adoption of mobile cloud services in Jordanian Universities between the two groups. These results call the university administration to clarify these factors for user groups to obtain a better judgment on investment and future practices for using new technologies.