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Author: search.filters.author.Jirakitpuwapat, Wachirapong

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    Convergence property of Nesterov-accelerated adaptive moment estimation with safety helmet detection and classification in smart industry application
    (2024-11-15)
    Jirakitpuwapat, Wachirapong
    ;
    Dubey, Premnath
    ;
    Prasertsuk, Narachata
    ;
    Phanthong, Chaowarit
    ;
    Tritham, Chatchai
    We propose a technique for first-order gradient-based optimization of stochastic objective functions called Nesterov-accelerated adaptive moment assessment, which makes use of dynamic evaluations of lower-order moments. The adaptive moment assessment and the Nesterov acceleration gradient are combined. Consequently, it has perks, and this technique is convenient to use, numerically economical, memory-light, and very well-suited for challenges with massive amounts of information and characteristics. Additionally, we investigate the algorithm's convergence characteristics and propose a conservative constraint on the convergence rate. Finally, we employ this technique for the detection and classification of safety helmets.
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    PM 2.5 Prediction & Air Quality Classification UsinMachine Learning
    (2024-06-01)
    Soontornpipit, Pichitpong
    ;
    Lekawat, Lertsak
    ;
    Tritham, Chatchai
    ;
    Tritham, Chattabhorn
    ;
    Pongpaibool, Pornanong
    Forecasting plays a vital role in air pollution alerts and the management of air quality. Studies and observations conducted in Thailand indicate a concerning rise in pollution levels, particularly in the concentration of PM2.5. concentrations. Bangkok, in particular, has been flagged for its alarmingly high PM2.5 By projecting the future PM2.5 concentrations in these urban areas, we can obtain valuable short-term predictive information regarding air quality. After conducting experiments using four different machine learning algorithms, it was found that the LSTM (Long Short-Term Memory) model provides the most accurate forecasts based on various statistical evaluation indicators. These indicators include a Root Mean Square Error (RMSE) of 2.74, Mean Absolute Error (MAE) of 1.97, R-squared value of 0.94, and Mean Absolute Percentage Error (MAPE) of 10.53. Then the classified air quality based on PM2.5 from the LSTM model gives the best performance indicators including accuracy = 0.9072, precision = 0.8466, negative predict value = 0.9403, sensitivity = 0.8144, specificity = 0.9381, and F1-score = 0.8169. The results show that the machine learning model can predict PM2.5 concentration, which is suitable for early warning of pollution and information provision for air quality management systems in Bangkok.