Association Rules and Deep Learning Paradigms for Big Data Processing
DOI:
https://doi.org/10.61704/jpr.v24i4.pp36-45Keywords:
RNN, TCN, LSTM, MSE, MAEAbstract
This in-depth study looks at data on fuel use in two main ways to find trends and make better predictions. One way is to learn how to use machine learning and association rule mining to try to guess what will happen. It uses association rules to show how things in a set are linked and how they do a lot of different things together. We can learn more about the parts that work together to change how much fuel is used. The RNN, TCN, and LSTM machine learning models can all guess how much fuel will be used, but they can do so in different ways. The TCN plan works out the best. The results show how important it is to choose a model design that makes the dataset's features better by putting together numbers and people's ideas about what might be important. We might be able to fully understand how fuel use changes over time if we put together what machine learning and association rule mining tell us. The numbers make it clear that the collection should be used for more research. There are different sets of methods that should be used for machine learning and more general statistical methods. People who give money, make rules, and try to guess how people will use fuel in the future should think about these ideas. It was found that people will be able to make better predictions in the future if they learn more about complicated machine learning design and link rules. The study is a good way to find out how much fuel people use when they switch sources of energy. When people use different types of fuel, we can also see how much they use. We can make the most of what we have this way.
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