CENTRALIZED ARTIFICIAL INTELLIGENCE-BASED TOXICOLOGY TRAINING MODEL FOR ENVIRONMENTAL PUBLIC HEALTH ASSESSMENT

摘要

#$%^&*AU2020101666A420200910.pdf#####CENTRALIZED ARTIFICIAL INTELLIGENCE-BASED TOXICOLOGY TRAINING MODEL FOR ENVIRONMENTAL PUBLIC HEALTH ASSESSMENT ABSTRACT Learning which contaminants in the atmosphere constitute a public health risk, including with and without safety data, allows us to make further accurate usage of the information that is for pursuing insightful strategies to developing relevant information. Whereas the conventional methods of gathering vital environmental information are cumbersome and of uncertain precision and living thing participation, this past knowledge can also be required to training designs for implementing and assessing the hazard of dangerous compounds, ensuring that the details are organized in an algorithmically suitable form and, preferably, combined with certain other forms of facts offering deterministic knowledge. It entails massive initiative, both in gathering and processing data and parallelizing effectively. This proposal promotes the data on genomics, the microbiome, socioeconomic factors, health behaviors, and environmental exposure data like soil, water, air, food, and toxins are obtained from various repositories such as Dataverse, UniProt, Open PHACTS and FAIRDOM which are based on FAIR guidelines. The obtained data can be further processed by using a data analysis tool and stored in the cloud. The Artificial intelligencebased training model called the reinforcement learning model is proposed for high-quality training data, and that makes a sequence of decisions. The proposal idea analyses the processed data and provides decisions about the impacts of environmental public health with high accuracy. 1 P a g eCENTRALIZED ARTIFICIAL INTELLIGENCE-BASED TOXICOLOGY TRAINING MODEL FOR ENVIRONMENTAL PUBLIC HEALTH ASSESSMENT Diagram Environment Hazard Data 5gnetwork g ateway Big Data Comn _g network reinforcement learning with deep Q-networks gateway data integrationtool environmental publichealth assessment Figure 1: Artificial based toxicology training model 1 P a g e