ABSTRACT
The rapid expansion of MOOCs (massive open online courses) allows learners to benefit from these courses by removing the barriers that obstruct the right to an open high-quality education. The courses offered on MOOC platforms are often free which has revolutionized this mode of distance learning, especially with the restrictions imposed by the advent of the COVID-19 pandemic. However, even though the number of registrants to MOOCs is quite considerable, only 10% of the learners complete the MOOC and obtain a certification. This phenomenon leads us to dig deeper to wonder about the means to avoid the high dropout rate of learners in such platforms. For this purpose, we suggest in this paper two complementary systems: a preventive system coupled with a proactive system to personalize the learners' pathways according to their specific needs and prior knowledge. The optimization of the pathways will be handled using a metaheuristic optimization algorithm called: Cuckoo Search Algorithm. © 2022 IEEE.
ABSTRACT
Nowadays, data is becoming incredibly important to manage in a variety of domains, especially healthcare. A large volume of information is collected through a lot of ways such as connected objects, datasets, records, or doctor’s notes. This information can help clinicians in preventing diagnosis errors and reduce treatment complexity. However, data is not always available and reliable due to missing values and outliers which lead to a loss of a significant amount of information. In this paper, we suggest a model to deal with missing values of our system during the diagnosis of COVID-19 pandemic. The system aims to enhance the physical distancing and activities limitations related to the outbreak and then providing to the medical staff the necessary information to make the right decision. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Nowadays social media play an important role in our societies. In fact, they remain one of the most commonly used means to easily obtain information online. However, social media can also be a catalyst for the proliferation of fake news around the world, especially in times of crisis where massive misinformation can have serious consequences. In particular, fake news about the COVID'19, as the virus spreads, leads to an infodemic of misinformation, hence the need for news verification mechanisms in social media. In this paper, we approach the problem of fake news related to COVID'19 as a global pandemic with a significant impact on various sectors. We also provide an aggregation system to detect and analyze fake news related to the COVID'19 pandemic in the Moroccan context based on data sets scrapped from Facebook. © 2020 IEEE.
ABSTRACT
COVID-19 pandemic is the largest worldwide sanitary and economical crisis of modern time. According to the World Health Organization (WHO), the new corona virus is spreading rapidly from person to person through contact transmission. To fight the COVID-19 outbreak, a number of strategies have been implemented at the population level, such as social distancing, testing and contact tracing. In this work, we focus on contact tracing, which is considered an effective public health measure to control the spread of this disease by identifying and tracking potential persons who may have been in contact with a COVID-19 infected person. To this end, we review various approaches and existing contact tracing applications used by various governments around the world to monitor and control the spread of this disease. Then, we discuss the limits and ethical issues of such digital contact tracing solutions. © 2020 IEEE.