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Author: search.filters.author.Nootyaskool, SupakitSubject: search.filters.subject.recurrent neural network

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    Categorize Level of Crystal Sugar Making with Recurrent Neural Network
    (2022-01-01)
    Ounsrimuang, Pimolrat
    ;
    Nootyaskool, Supakit
    This research presents the study of recurrent neural networks to predict industrial crystal sugar making. The recurrent neural network trains on six parameters consisting of liquid in the pan, Brix levels, vacuum in the pan, liquor temperatures, water steam supplier, and current for mix-motor agitator. The input variables were the trained model to predict by categorizing data in three levels high, middle, and low which the data came from human control the sugar boiler machine. The trained model for the future can be extended to make an experience meter to indicate the ability of workers to control the machine.
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    Gold Investment Model on RNN and Finding Best Investment Strategy on PSO
    (2022-01-01)
    Kanchanakantikul, Pakamas
    ;
    Nootyaskool, Supakit
    Nowadays, Algorithm trading in community and stock is interesting research, while gold is also an investment option. This research presents two steps. Three inputs sequence consists of the gold price(sell), gold spot and crude oil. Output has an order sequence indicating buy, sell, and wait for the signal. Firstly, finding the best strategy from historical data by particle swarm optimization (PSO) compared with random search (RS). That will get buying, selling, or waiting signals in the gold trading market Secondly, creating gold investment by recurrent neural network (RNN) model. The experiment result showed RNN trading model based on PSO is better than RS, which has a profit of 79.667 percent.