Artificial Intelligence as a Service for Immoral Content Detection and Eradication
Issue date
2022Author
Shah, Fadia
Anwar, Aamir
Haq, Ijaz Ul
AlSalman, Hussain
Hussain, Saddam
Al-Hadhrami, Suheer
Suggested citation
Shah, Fadia;
Anwar, Aamir;
Haq, Ijaz Ul;
AlSalman, Hussain;
Hussain, Saddam;
Al-Hadhrami, Suheer;
.
(2022)
.
Artificial Intelligence as a Service for Immoral Content Detection and Eradication.
Scientific Programming, 2022, vol. 2022, Article ID 6825228.
https://doi.org/10.1155/2022/6825228.
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Social media is referred to as active global media because of its seamless binding thanks to COVID-19. Connecting software such
as Facebook, Twitter, WhatsApp, WeChat, and others come with a variety of capabilities. +ey are well-known for their low-cost,
quick, and effective communication. Because of the seclusion and travel constraints caused by COVID-19, concerns, such as low
physical involvement in many possible activities, have arisen. Depending on their information, knowledge, nature, experience,
and way of behavior, various types of human beings have diverse responses to any scenario. As the number of net subscribers
grows, inappropriate material has become a major concern. +e world’s most prestigious and trustworthy organizations are keenly
interested in conducting practical research in this field. +e research contributes to using Artificial Intelligence (AI) as a service
(AIaaS) for preventing the spread of immoral content. As software as a service (SaaS) and infrastructure as a service (IaaS), AIaaS
for immoral content detection and eradication can use effective cloud computing models to leverage this service. It is highly
adaptable and dynamic. AIaaS-based immoral content detection is mostly effective for optimizing the outcomes based on big data
training data samples. Immoral content is identified for semantic and sentiment evaluation, and content is divided into immoral,
cyberbullying, and dislike components. +e suggested paper’s main issue is the polarity of immoral content that can be processed
using an AI-based optimization approach to control content proliferation. To finish the class and statistical analysis, support
vector machine (SVM), selection tree, and Naive Bayes classifiers are employed.
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