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Though many researchers have studied plant leaf disease, the timely diagnosis of diseases in olive leaves still presents an indisputable challenge. Infected leaves may display different symptoms from one plant to another, or even within the same plant. For this reason, many researchers studied the effects of those diseases on, at most, two plants. Since olive crops are affected by many pathogens, including bacteria welt, olive knot, Aculus olearius, and olive peacock spot, the development of an efficient algorithm to detect the diseases was challenging because the diseases could be defined in many different ways. For this purpose, we introduce an optimal deep learning model for diagnosing olive leaf diseases. This approach is based on an adaptive genetic algorithm for selecting optimal parameters in deep learning model to provide rapid diagnosis. To evaluate our approach, we applied it in three famous deep learning models. For the comparative evaluation, we also tested other well-known machine learning methods. The experimental results presented in this paper show that our model outperformed the other algorithms, achieving an accuracy of approximately 96% for multiclass classification and 98% for binary classification.

This research study is inscribed in cycle improvement of training and of academic program for graduates at Aljouf University. So, in this paper, we show how to evaluate academic programs using the goals modeling to assess the course learning outcomes in this university. The academic program evaluation is a complex and multidisciplinary process. It relies on the technical process (requirement engineering that we use in computer and software engineering) and on the other human science activities. Here, we are only interested by the technical aspect of this process. In this study, we use the goal model like it is used by the requirement engineering methods, such as goals-based requirement analysis method (GBRAM), I* and collaborative requirement engineering with scenarios (CREWS) to identify the expected outcomes on the courses. As result, the model that we present here feeds well the process of Course Learning Outcomes (CLOs). For experimenting of our approach and model, we present the application of this evaluation process that is conducted at Al-Jouf University, in the department of computer sciences.

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The concept of quality of service (QoS) is a new computer technology. Previously, there was a slow internet connection to access the sites and it was slow to send information. But now, it requires speeding up the traffic and increasing the efficiency for audio and video. In this study, we discuss the concepts of QoS provided over the network to achieve these goals. This study aims to compare six algorithms to control the QoS, then, the best algorithm will be selected to improve the traffic. These algorithms are named first in first out (FIFO), priority queuing (PQ), custom queuing (CQ), CQ with low latency queuing (LLQ), weighted fair queuing (WFQ), WFQ with low latency queuing (LLQ), so the behavior of these algorithms can be measured. The results obtained by comparing between them using OPNETsimulation show that the best algorithm is the priority queuing algorithm, followed by CQ, then CQ with LLQ, then WFQ, then WFQ with LLQ and finally FIFO. All these results are plotted in the form of graphs to show the paths of these algorithms for the single state with an operation time of 5 minutes for each algorithm.