Kreesuradej, Worapoj

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Kreesuradej, Worapoj
Alternative Name
Kreesuradej, W.
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Email
worapoj.kr@kmitl.ac.th

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    Item type:Publication,
    Incremental Association Rule Mining with a Fast Incremental Updating Frequent Pattern Growth Algorithm
    (2021-01-01)
    Thurachon, Wannasiri
    ;
    Kreesuradej, Worapoj
    One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental associationrule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incrementalmining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods.
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    Item type:Publication,
    A Study of Using GPT-3 to Generate a Thai Sentiment Analysis of COVID-19 Tweets Dataset
    (2023-01-01)
    Isaranontakul, Patthamanan
    ;
    Kreesuradej, Worapoj
    This study evaluated the effectiveness of using synthetic text datasets generated by GPT-3 for sentiment analysis with deep learning models, namely Bi-GRU and Bi-LSTM. The study compares the performance of these models on both synthetic text and Label Tweet datasets using GPT-3 and reveals that deep learning model performance is dependent on the dataset's nature. The results indicate that using synthetic text generated by GPT-3 significantly enhances the accuracy of both models, with Bi-LSTM achieving an accuracy of 0.84 and Bi-GRU achieving an accuracy of 0.85. The study underscores the importance of meticulous dataset selection and preparation for developing precise and effective deep learning models for various sequential data types. The findings demonstrate that synthetic text datasets generated by GPT-3 can serve as a valuable resource for developing deep learning models as they are labeled and save researchers time and effort in manual labeling of large datasets.