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  • Open Access

    ARTICLE

    Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

    Mohammed Hadwan1,2,3,*, Mohammed A. Al-Hagery4, Mohammed Al-Sarem5, Faisal Saeed5,6

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4675-4689, 2022, DOI:10.32604/cmc.2022.027311

    Abstract Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users’ reviews is an… More >

  • Open Access

    ARTICLE

    User Role Discovery and Optimization Method Based on K-means++ and Reinforcement Learning in Mobile Applications

    Yuanbang Li*, Wengang Zhou, Chi Xu, Yuchun Shi

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1365-1386, 2022, DOI:10.32604/cmes.2022.019656

    Abstract With the widespread use of mobile phones, users can share their location and activity anytime, anywhere, as a form of check-in data. These data reflect user features. Long-term stability and a set of user-shared features can be abstracted as user roles. This role is closely related to the users’ social background, occupation, and living habits. This study makes four main contributions to the literature. First, user feature models from different views for each user are constructed from the analysis of the check-in data. Second, the K-means algorithm is used to discover user roles from user features. Third, a reinforcement learning… More >

  • Open Access

    ARTICLE

    Managing Traumatic Stress Using a Mental Health Care Mobile App: A Pilot Study

    Yun-Jung Choi1,#, Eun-jung Ko2,#, Eun-Joo Choi2,*, Youn-Joo Um2,*

    International Journal of Mental Health Promotion, Vol.23, No.3, pp. 385-393, 2021, DOI:10.32604/IJMHP.2021.015018

    Abstract This study aims to contribute to improving mental health services and establishing a direction for disaster survivors by verifying the effectiveness of the TLS (Training for Life Skills) app, a mental health management mobile application. Altogether, 22 disaster survivors received access to the app (with guidance) for eight weeks; we analyzed its effectiveness by examining each participant’s electroencephalography data, which were collected while they were utilizing the app. The results of this study show that the use of the TLS app had a significant positive effect on emotional quotient, basic rhythm quotient (left brain, right brain), alpha blocking rate (left… More >

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