ABSTRACT
In Saudi Arabia, several mitigating measures were implemented in response to the COVID-19 pandemic, including the creation of COVID-19 mobile applications (apps) for public use. The Saudi government has made the use of these apps mandatory for its citizens and residents. However, it is essential to explore the perception that common users have regarding using these apps in terms of usability and user experience. Therefore, this paper assesses user experience in terms of effectiveness, efficiency, and user satisfaction with the usability of the Saudi COVID-19 apps. The reviews of five mobile apps launched by the Saudi Data and AI Authority (SDAIA) and the Ministry of Health in the Apple Store were extracted using an online tool and analyzed using the content analysis method. The number of collected reviews was 29 for Sehha, 406 for Sehhaty, 442 for Mawid, 107 for Tabaud, and 1338 for Tawakkalna. The results of the study showed that Mawid (82%) and Tabaud (81%) had the highest usability of all the apps studied. Sehha (138%) and Sehhaty (-107%) received the lowest usability scores, followed by Tawakkalna (22%). Based on these results, we identified several usability issues with each app. Some of the main problems reported by users were increased battery drain, lack of privacy, and technical issues.
ABSTRACT
To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.
ABSTRACT
The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.