This article analyzes the problem of filtering spam messages and addressing spam messages, Bayesian theorems based on artificial intelligence, LVQ algorithms (LVQ learning vector quantization) and a filtering scheme for systems based on neural networks. The direct construction of an effective neural network model of spam filtering using database recognition technology is considered. The parameters of access to the neural network how to include predefined statistical and non-statistical attributes of messages are given. The structure of neural network technology for classifying emails is also considered. The procedure for analyzing incoming data using the tool included in the analytical platform Deductor Studio 5.3 is described. as a result, a training kit is obtained that is suitable for use.
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Bekmuratov, Tulkun Fayzievich; Botirov, Fayzullajon Bakhtiyorovich; and Haydarov, Elshod Dilshod ugli
"ELECTRONIC SPAM FILTERING BASED ON NEURAL NETWORKS,"
Chemical Technology, Control and Management: Vol. 2020
, Article 10.
Available at: https://uzjournals.edu.uz/ijctcm/vol2020/iss3/10