Browsing by Author "Boonrawd, Pudsadee"

Now showing 1 - 3 of 3
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    A hybrid forecasting model of cassava price based on artificial neural network with support vector machine technique
    Polyiam, Korawat; Boonrawd, Pudsadee (2017)
    Thailand is the world's largest exporter of cassava. The cassava prices fluctuate because of many factors such as the production cost; economic condition; and price intervention. Therefore; this research aims to propose a forecasting model of cassava price based on the 11-year data (from 2005 to 2015) obtained from the Thai Tapioca Starch Association and Office of Agricultural Economics. Various techniques were applied for the forecast such as Artificial Neural Network; Support Vector Machine; k-Nearest Neighbor and Hybrid Technique. The statistics used to determine the effectiveness of this model were Mean Absolute Percentage Error (MAPE); Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). The results of this research showed that Hybrid Technique demonstrated the lowest value of error followed by Artificial Neural Network; k-Nearest Neighbor and Support Vector Machine; respectively. Therefore; it could be concluded that using the Hybrid Technique to forecast the price of cassava was better than other techniques and generated the predicted price closest to the actual price.
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    Augmented Reality with Mask R-CNN (ARR-CNN) inspection for Intelligent Manufacturing
    Perdpunya, Tawatchai; Nuchitprasitchai, Siranee; Boonrawd, Pudsadee (Association for Computing Machinery, 2021)
    A machine is an essential factor for industrial production. Industry 4.0 is the revolution that causes improvement of machines to have higher efficiency. Accordingly; inspection and maintenance are becoming more important. However; most of factories are not changed the operating process; there is no data logging for evaluation and analysis for preventive maintenance. This research aims to develop a model for machine inspection using augmented reality with object detection and marker techniques on real world machines and mask R-CNN algorithm allowing inspector to perform inspections. This study; we demonstrate the process of development of the proposed model by showing steps of data acquisition from a machine in a factory. The dataset is images of machines in different perspectives; and they were used for training and testing the model. The testing is done on a mobile device of an inspector. With computer vision technique and the proposed model; the instant precision tracking and detection are provided. Then the trained model is transferred to the mobile devices for testing without any modification by an expert. Some images of machines are randomly selected to verify the accuracy of the model. The result shows that the efficiency of the model is acceptable in real usage.
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    Opinion Mining using TRC Techniques:
    Romyen, Nirach; Nualnim, Sureeporn; Maliyaem, Maleerat; Boonrawd, Pudsadee; Viriyapant, Kanchana; Heeptaisong, Tongpool (SCITEPRESS - Science and Technology Publications, 2021)
    Sentiment Analysis; Opinion Mining; Text Representing Centroids; Co-occurrence Graph.