AI-Based Child Care Parental Control System

Udara Jayasekara, Hansindu Maniyangama, Kalhan Vithana, Tharana Weerasinghe, J. Wijekoon, R. Panchendrarajan
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Abstract

Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.
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基于人工智能的儿童保育家长监控系统
由于新冠肺炎疫情在全球范围内的流行,儿童被迫通过在线平台进行远程学习,手机成为他们的主要设备之一。具有互联网访问的移动设备为教育、娱乐和社交联系提供了一个主要的出口,但这种组合可能导致一些严重的不良后果,如在线利用、有害成瘾和在线社交网络的其他负面影响。为了解决有害的影响,父母的控制变得越来越重要,然而斯里兰卡的父母却没有意识到这一点。因此,本研究提出了一种家长控制系统来监控孩子的活动。Android、Microsoft Azure、Java、Python、OpenCV、MySQL和FastAPI是应用程序开发中使用的最重要的技术。建议的方法主要侧重于斯里兰卡的情况,旨在提高父母的数字素养,同时保护儿童免受网络威胁。结果表明,在对照实验中,提出的手机应用程序对有毒文字、毒品和酒精含量、游戏角色图像和Instagram僧伽罗评论严重程度的识别分别为94%、95%、97%和55%。
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