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Author: search.filters.author.Numnonda, Thanisa

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    Image Dehazing in Disproportionate Haze Distributions
    (2021-01-01)
    Huang, Shih Chia
    ;
    Jaw, Da Wei
    ;
    Li, Wenli
    ;
    Lu, Zhihui
    ;
    Kuo, Sy Yen
    Haze removal techniques employed to increase the visibility level of an image play an important role in many vision-based systems. Several traditional dark channel prior-based methods have been proposed to remove haze formation and thereby enhance the robustness of these systems. However, when the captured images contain disproportionate haze distributions, these methods usually fail to attain effective restoration in the restored image. Specifically, disproportionate haze distribution in an image means that the background region possesses heavy haze density and the foreground region possesses little haze density. This phenomenon usually occurs in a hazy image with a deep depth of field. In response, a novel hybrid transmission map-based haze removal method that specifically targets this situation is proposed in this work to achieve clear visibility restoration and effective information maintenance. Experimental results via both qualitative and quantitative evaluations demonstrate that the proposed method is capable of performing with higher efficacy when compared with other state-of-the-art methods, in respect to both background regions and foreground regions of restored test images captured in real-world environments.
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    A comparative performance of real-time big data analytic architectures
    (2019-07-01)
    Sanla, Apisit
    ;
    Numnonda, Thanisa
    Nowadays, many organizations pay attention to the relevant technologies of Big Data to analyze more accurately, quickly, and efficiently. Real-time Big Data analytics is challenging due to the massive volume of complex data needed to distribute in processing. Therefore, in this research, we investigate two state-of-the-art architectures: Lambda and Kappa. The Kappa architecture is simply the Lambda architecture without the batch layer. To help businesses decide on the right architecture, their processing time, and resource utilization in the same environment need to be found out. Experiments had been carried out with the data size 3 MB, 30 MB, and 300 MB. The results showed that Lambda architecture outperforms Kappa architecture around 9% for the accuracy test when using processing time approximately 2.2 times more than Kappa architecture. Lambda architecture also used more 10-20% of CPU usage and 0.5 GB of RAM usage than Kappa architecture.
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    Concurrent self-identification applying QR code to record class attendance (QRClass)
    (2019-07-01)
    Nalintipwong, Srinual
    ;
    Tasarika, Thanarat
    ;
    Ruksomya, Chayut
    ;
    Vittayakorn, Sirion
    ;
    Numnonda, Thanisa
    The traditional way of recording student attendance is that an instructor calls student names one by one and records their absence or presence status. It usually consumes much time and sometimes causes errors. Therefore, some instructors may choose not to record student attendance, and this could affect academic learning and outcome. Although many self-identification methods have been explored, the entire processing time is similar to the traditional one. Hence, to solve such the problem, this research proposes a methodology to develop a concurrent class attendance system applying QR code, called QRClass. The results show that the processing time of QRClass is reduced by approximately 93.14% for 49 students compared to the traditional way. Therefore, QRClass is not only require less processing time, but also significantly increase the effectiveness of the class attendance recording method.
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    A Comparative Study on Performance and Resource Utilization of Real-time Distributed Messaging Systems for Big Data
    (2019-07-01)
    Intorruk, Somprasong
    ;
    Numnonda, Thanisa
    In the past few years, data continuously increase and in various forms called Big Data. Besides, data analytics play an important role more and more in most organizations. From these reasons, many efficiently distributed messaging systems have been introducing to handle Big Data in real-time. However, choosing the appropriate and efficient methods and tools to transfer Big Data is still challenging. Therefore, this paper purposes of comparing the architecture, performance, and resource utilization between Apache Kafka which is one of the favorite tools for Big Data and Apache Pulsar which is similar to Kafka and become one of the latest tools for big data. After we implemented both systems in the same environment, the results show that Pulsar outperforms in throughput, latency, and average resource utilization especially when the size of messages is small (such as 1 KB and 1MB).
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    Machine learning models comparison for bitcoin price prediction
    (2018-11-13)
    Phaladisailoed, Thearasak
    ;
    Numnonda, Thanisa
    In recent years, Bitcoin is the most valuable in the cryptocurrency market. However, prices of Bitcoin have highly fluctuated which make them very difficult to predict. Hence, this research aims to discover the most efficient and highest accuracy model to predict Bitcoin prices from various machine learning algorithms. By using 1-minute interval trading data on the Bitcoin exchange website named bitstamp from January 1, 2012 to January 8, 2018, some different regression models with scikit-Iearn and Keras libraries had experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and the R-Square (R <sup>2</sup> ) was as high as 99.2%.
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    A real-time recommendation engine using lambda architecture
    (2018-06-01)
    Numnonda, Thanisa
    In a data science theory, the recommended methodology is one of the most popular theories and has been deployed in many real industries. However, one of the most challenging problems these days is how to recommend items with massively streaming data. Therefore, this paper aims to do a real-time recommendation engine using the Lambda architecture. The Apache Hadoop and Apache Spark frameworks were used in this research to process the MovieLens dataset comprised 100 K and 20 M ratings from the GroupLens research. Using alternating least squares (ALS) and k-means algorithms, the top K recommendation movies and the top K trending movies for each user were shown as results. Additionally, the mean squared error (MSE) and within cluster sum of squared error (WCSS) had been computed to evaluate the performance of the ALS and k-means algorithms, sequentially. The results showed that they are acceptable since the MSE and WCSS values are low when comparing to the size of data. However, they can still be improved by tuning some parameters.