The evolution of technology acceptance theories and models have started since the beginning of the 20th century and it is still evolving. This evolution is happened in different theoretical perspectives, such as: cognitive, affective, motivational, and behavioral intentions and reactions for individuals. Nowadays, understanding the reason of accepting or rejecting any new technology by users has become one of the most important areas in the IT field. The social media applications are benefited and enhanced

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data.

Electronic Learning (E-Learning) evolves consistently. An effective eLearning has unrelenting readiness in integrating new paradigms while the students become the core of the process. In this regard, the current tools are replaced by others, with customization under consideration. Somehow, changes are costly and should not be merely focusing on replacing all past initiatives but also should attempt to combine the new initiatives with the successful ones, in order that the great robustness and effectiveness of learning environments could be assured. Accordingly, the presently available integration initiatives will be reviewed in this paper. A new initiative will be proposed as well.

Learning disabilities could impair one's access to and involvement in education. For students with learning disabilities or students with Special Education Needs (SEN), school may be a struggle, and concentrating in lessons or trying to understand what is taught may be highly challenging. Some students failed in their studies, while some become dropouts as school to them is obsolete and boring, and fails to cater to their learning needs. This study therefore proposed a well-known but unpopular method for assisting the struggling students to return to school. Specifically, this study proposes the use of more than one styles of records and expression presentation to attract students who require learning strategies that are broader and more dynamic.

The Interval Manager (INTMAN-Mobile Workforce) is a mobile-assisted Utility that replaces the traditional paper-and-pencil strategies to gather fixed c language time-sampled observational information. An initial application that compares INTMAN-Mobile Workforce with the traditional method of paper-and-pencil was presented in this article. Smart Devices were used to run INTMAN-Mobile Workforce data collection application while a Microsoft Windows run using desktop was used to analyse the data. Changed frequencies, durations percent, conditional probabilities, and kappa settlement matrices and values were the analyses carried out. the contrast among INTMAN-Mobile Workforce and the traditional paper-and-pencil technique was primarily based on 5 dimensions: setup time, statistics entry duration, length of inter-observer agreement calculations, accuracy, and price. the outcomes show the efficiency and accurateness of the computer-assisted data collection system over the conventional technique for Time-sampled records.