Nowadays, due to easiness and expansion in property of smart mobile devices, it is becoming inevitable for mobile applications to have an important role in higher education systems. The Egyptian public universities are facing the problem of students’ large number enrolled in each year. Thus, we lack proper communication between educators and learners. Mobile learning can solve that problem, and it enables adjustment of the curriculum to meet students' learning time and life situations. It provides different solutions better than traditional educational methods. Students and professors could exchange educational material or information even if they are not in the same class. Furthermore, the cost of universities’ materials reduced, as all course materials can be found online through mobile applications. This paper proposes a mobile learning system named “Easy-Edu.” The proposed system intended to make the learning process easier, focus on students’ needs, and encourage communication and collaboration between students and professors and supports collaborative scenario-based learning for university students. Unlike other traditional systems, the proposed “Easy-Edu” was built using an Agile-based approach that delivers sustainable and high-quality mobile learning system. In addition, it eliminates the chances of absolute system failure and detects and fixes issues faster. Summarily, everything related to the design and implementation of “Easy-Edu” is discussed.
Worldwide, plant diseases adversely influence both the quality and quantity of crop production. Thus, the early detection of such diseases proves efficient in enhancing the crop quality and reducing the production loss. However, the detection of plant diseases either via the farmers' naked eyes or their traditional tools or even within laboratories is still an error prone and time consuming process. The current paper presents a Deep Learning (DL) model with a view to developing an efficient detector of olive diseases. The proposed model is distinguishable from others in a number of novelties. It utilizes an efficient parameterized transfer learning model, a smart data augmentation with balanced number of images in every category, and it functions in more complex environments with enlarged and enhanced dataset. In contrast to the lately developed state-ofart methods, the results show that our proposed method achieves higher measurements in terms of accuracy, precision, recall, and F 1-Measure.
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