This qualitative study is an investigation and assessment of distance learning in Morocco during the COVID-19 pandemic. This research surveyed 3037 students and 231 professors enrolled in different stages of higher education programs. It aims to investigate the limitations of e-learning platforms and how these activities take place at public and private Moroccan universities during the coronavirus confinement. For this purpose, two structured questionnaires were constructed by researchers from different specialties, and the type of data was based on the responses of students and professors from 15 universities. In this paper, we have used three methods: descriptive analysis, regression analysis, and qualitative response analysis. As a data analytics tool, Microsoft Power BI was used to analyze data, visualize it, and draw insights. In this study, both professors and students stated that online learning is not more interesting than ordinary learning and professors need to provide at least 50% of their teaching in face-to-face mode. Recommendations at teaching and technical levels, such as the need for technical support and training in the use of these tools, were provided to enhance and promote distance education in Morocco. The contribution of this paper comes as a result of data analysis obtained from a survey conducted in some famous Moroccan universities.

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Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.

On one hand, the emergence of cutting-edge technologies like AI, Cloud Computing, and IoT holds immense potential in Smart Farming and Precision Agriculture. These technologies enable real-time data collection, including highresolution crop imagery, using Unmanned Aerial Vehicles (UAVs). Leveraging these advancements can revolutionize agriculture by facilitating faster decision-making, cost reduction, and increased yields. Such progress aligns with precision agriculture principles, optimizing practices for the right locations, times, and quantities. On the other hand, integrating UAVs in Smart Farming faces obstacles related to technology selection and deployment, particularly in data acquisition and image processing. The relative novelty of UAV utilization in Precision Agriculture contributes to the lack of standardized workflows. Consequently, the widespread adoption and implementation of UAV technologies in farming practices are hindered. This paper addresses these challenges by conducting a comprehensive review of recent UAV applications in Precision Agriculture. It explores common applications, UAV types, data acquisition techniques, and image processing methods to provide a clear understanding of each technology's advantages and limitations. By gaining insights into the advantages and challenges associated with UAV-based applications in Precision Agriculture, this study aims to contribute to the development of standardized workflows and improve the adoption of UAV technologies.