The adoption of the e-portfolio management system in the Technical and Vocational Training Corporation (TVTC) in Saudi Arabia

The Electronic Portfolio Management System (EPMS) is one such system, but despite its importance, its extensive adoption among institutions remains low because the end-user rejects its use. EPMS adoption in Technical and Vocational Training Corporation (TVTC). Hence, in the context of TVTC organizations, EPMS adoption needs an effective framework to highlight the factors influencing EPMS adoption and eventually positively affecting the employees’ performance. This study classified the factors into three dimensions (technological, organizational, and environmental) based on the level of interaction. With the help of the Technology Acceptance Model (TAM), De Lone and Mc Lean's IS model, and the Technology, Organization and Environment (TOE) model, this study developed and proposed a robust framework. The study used a quantitative approach in which copies of an online questionnaire were passed and distributed to 375 respondents in TVTC institutions. The analysis of the collected data was done using AMOS-SEM 3 statistical software. The finding revealed that technology, organization, and environment, which are second-order factors, had significant and positive effects on EPMS adoption. The results also supported a substantial relationship between EPMS adoption and the performance of employees (Academicians and Managerial), with the entire first-order factors comprising of technological factors, namely perceived usefulness, perceived ease of use, perceived information quality, perceived system quality, and perceived service quality, organizational factors, namely financial support, top management support and training, and environmental factors, namely cloud computing ability, government role, and big data facility were examined for their role in the adoption of EPMS among Saudi TVTC, and were and were found to be significant and accounted 43% of the variance in the EMPS adoption. At the same time, the EPMS adoption explains 39% of the variance extracted from employees’ performance. This study contributes theoretically by filling a gap in the literature and providing new validation for the TAM, De Lone and Mc Lean's IS model, and TOE. The practical value lies in giving the policy makers and decision makers essential information to adopt EPMS in less time and effort

A New Classification Method for Drone-Based Crops in Smart Farming

During the past decades, smart farming became one of the most important revolutions in the agriculture industry. Smart farming makes use of different communication technologies and modern information sciences for increasing the quality and quantity of the product. On the other hand, drones showed a major potential for enhancing imagery systems and remote sensing usage for many different applications such as crop classification, crop health monitoring, and weed management. In this paper, an intelligent method for classifying crops is proposed to use a transfer learning approach based on a number of drone images. Moreover, the Convolutional Neural Network (CNN) method is used as a classifier to improve efficiency for obtaining more accurate results in the training and testing phases. Various metrics are measured to evaluate the efficiency of the proposed model such as accuracy rate of detection, error rate and confusing matrix. It is found to be proven from the experimental results that the proposed method presents more efficient results with an accuracy detection rate of 92.93%