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Application of Computer Simulation Technology in Detection Model of Mycoplasma Pathogen Subtype
Summary
This paper developed a detection model for mycoplasma pathogen invasion in biological systems using deep learning techniques. The research is focused on pathogen detection and has limited direct relevance to microplastic pollution or human health.
The traditional Microplastic-based microbial invasion detection system uses a low-level binary detector. An algorithm is proposed to judge whether the random pattern has vulnerabilities based on the in-depth study of the detection technology of mycoplasma pathogen microorganism invasion. A new model of mycoplasma pathogen invasion detection system was designed based on the above research,. The affinity maturation process, memory detector variation and incomplete matching rules were introduced in the model. In this paper, the hybrid intrusion detection system of mycoplasma pathogenic microorganisms based on immunological principle was used to conduct intrusion detection experiments, and the average detection rate of all mycoplasma pathogenic microorganisms' attack types was 94.94%.
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