The spread of the COVID-19 pandemic affected all areas of social life, especially education. Globally, many states have closed schools temporarily or imposed local curfews. According to UNESCO estimations, approximately 1.5 billion students have been affected by the closure of schools and the mandatory implementation of distance learning. Although rigorous policies are in place to ban harmful and dangerous content aimed at children, there are many cases where minors, mainly students, have been exposed relatively or unfairly to inappropriate, especially sexual content, during distance learning. Ensuring minors’ emotional and mental health is a priority for any education system. This paper presents a severe attention neural architecture to tackle explicit material from online education video conference applications to deal with similar incidents. This is an advanced technique that, for the first time in the literature, proposes an intelligent mechanism that, although it uses attention mechanisms, does not have a square complexity of memory and time in terms of the size of the input. Specifically, we propose the implementation of a Generative Adversarial Network (GAN) with the help of a local, sparse attention mechanism, which can accurately detect obscene and mainly sexual content in streaming online video conferencing software for education.
In order to fully understand and analyze the rules and cognitive characteristics of users’ learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation system including user portraits is proposed. System raking of data layers, data analysis responses, and recommendations for sum beds are seamless and collaboratively combined. The data layer consists of user data and a design library containing scholarship materials, study materials, and price sets. The data analysis framework is captured by rest and energy data represented by basic information, learning behavior, etc. We can provide perceptual and visual learning audio feedback. And thus witness computing should convey users’ learning behavior rules through similarity analysis and mob algorithm. We further use TF-IDF to sequentially mine users’ resource priorities and always bind personalized learning suggestions. The system has been applied to an online education platform supported by artificial intelligence technique, which can provide instructors and students with personalized portraits. We also proposed to learn audio feedback and data consulting services, typically during the hard work phase of the assistant semester.
Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals’ personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users’ privacy.
Strengthening construction for teaching staff is an eternal theme of development and construction for colleges, and it is also the focus of personnel management in colleges. China is swiftly transitioning from the industrial age to the era of intelligence as a result of the rapid growth of information technology and artificial intelligence. Colleges and universities have reached a new stage in their evolution, one marked by intelligent use of technology, as represented by the fourth generation of information technology: cloud computing, big data, and artificial intelligence. Higher standards for college professors’ teaching abilities have been imposed by this new policy. It is therefore beneficial to evaluate teachers’ teaching abilities from an artificial intelligence perspective to improve the overall quality of college education. First, this work researches and improves the teaching ability training strategies of college teachers driven by artificial intelligence from different levels. Second, this work proposes a neural network (IPSO-BP) for evaluating the teaching ability of college teachers via artificial intelligence technology. Aiming at the issues in BP network, this work constructs IPSO by improving the weight decay strategy and learning factor of PSO algorithm. Then, it uses IPSO to optimize the BP to construct IPSO-BP. Third, the results of the experiments in this work suggest that the strategy proposed here is both feasible and preferable.
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