Background General population knowledge, satisfaction, and barriers to using Seha app have not been evaluated from a large-scale perspective. Therefore, this study aimed to explore current knowledge, satisfaction, and barriers of using Seha app and identify the most common mobile health application used among the general population in Saudi Arabia. Methods A cross-sectional online survey, consisting of 25 questions, was distributed among the general population of Saudi Arabia. Descriptive statistics were used to describe the respondents’ characteristics. Categorical variables were reported as frequencies and percentages. A chi-square (χ2) test was conducted to assess the statistical difference between respondents’ demographic characteristics and their knowledge and use of the app. Results Overall, 5008 respondents, both Saudi (3723: 74%) and non-Saudi (1285: 26%) as well as male 2142 (43%) and female 2866 (57%), across the Kingdom of Saudi Arabia completed the online survey. A total of 2921 (58%) had heard of the Seha app, although only 1286 (25%) had used the app. Higher percentages of users were from the western region, females and those within the age group of ≥51 years old, 388 users (29%: P <0.001), 804 (28%; P <0.001) and 67 (35%; P =0.013), respectively. Consulting a doctor was the most frequently utilized service, 576 users (58%). Respondents strongly agreed 402 (41%) that Seha was easy to use, and 538 (54%) strongly agreed that they would recommend Seha to others. The most common barrier of using Seha was a lack of knowledge about the app and its benefits, at 1556 (35%). Overall, the Tawakkalna app was the most utilized mobile health application provided by MOH used 2170 (48%). Conclusion Utilization of the Seha app is quite low due to a lack of knowledge about the app and its benefits. Thus, the MOH should promote public awareness about the app and its benefits.

A human gesture is a non-verbal form of communication and is critical in human-robot interactions. Vision-based gesture recognition methods play a key role to detect hand motion and support such interactions. Hand gesture recognition allows a convenient and usable interface between devices and users. Hand gestures can be used for various fields which makes it be able to be implemented for communication and further. Hand gesture recognition is not only useful for people who are hearingimpaired or disabled but also for the people who experienced a stroke, as they need to communicate with other people using different common essential gestures such as the sign of eating, drink, family and, more. In this paper, an approach for recognizing hand gesture based on Convolutional Neural Network (CNN) is proposed. The developed method is evaluated and compared between training and testing modes based on several metrics such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood and root mean square. Results show that testing accuracy is 99% using CNN and is an effective technique in extracting distinct features and classifying data.

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BACKGROUND Background: The Saudi Ministry of Health (MOH) decided to enhance telemedicine provision by introducing the Seha mobile app; however, the perception of the Seha app has not been extensively explored from the perspective of the general population OBJECTIVE This study aims to explore the current knowledge, satisfaction, and barriers to the use of the Seha app among the general population in Saudi Arabia. METHODS A cross-sectional online survey was distributed among the general population of Saudi Arabia. Descriptive statistics were used to describe the respondents’ characteristics. Categorical variables were reported as frequencies and percentages. A chi-square (χ2) test was conducted to assess the statistical difference between respondents’ demographic characteristics and their knowledge and use of the Seha app. RESULTS 2,921 (58%) of the respondents had heard of the app, although only 1,286 (25%) had used Seha. Higher users were noticed among those above 51 years of age and females, 67 (35%) and 804 (28%), respectively. Highest Seha users were from the western region, 388 (29%: P <0.05). Consulting a doctor was the most frequently used service, 576 (58%). Respondents strongly agreed, 402 (41%), or agreed, 470 (48%), that Seha was easy to use. Moreover, 538 (54%) strongly agreed and 343 (35%) agreed that they would recommend Seha to others. The most common barrier to using the Seha app was a lack of knowledge about the app and its benefits, 1,556 (35%). CONCLUSIONS Conclusion: Utilization of the Seha app in Saudi Arabia is very low due to a lack of knowledge about the app and its benefits. Demographic factors and awareness were predictors for higher utilization. Seha was easy to use, and the majority would recommend it to others. Future studies are needed to explore the factors associated with the low rate of use.

Social media has great importance in the community for discussing many events and sharing them with others. The primary goal of this research is to study the quality of the sentiment analysis (SA) of impressions about Saudi cruises, as a first event, by creating datasets from three selected social media platforms (Instagram, Snapchat, and Twitter). The outcome of this study will help in understanding opinions of passengers and viewers about their first Saudi cruise experiences by analyzing their feelings from social media posts. After cleaning, this experiment contains 1200 samples. The data was classified into positive or negative classes using the choice of machine learning algorithms, such as multilayer perceptron (MLP), naıve bayes (NB), random forest (RF), support vector machine (SVM), and voting. The results show the highest classification accuracy for the RF algorithm, as it achieved 100% accuracy with over-sampled data from Snapchat using both test options. The algorithms were compared among the three different datasets. All algorithms achieved a high level of accuracy. Hence, the results show that 80% of the sentiments were positive while 20% were negative.