## Article  
_Article_ oneM2M-Enabled Prediction of High Particulate Matter Data Based on Multi-Dense Layer BiLSTM Model  
[PERSON]  
Department of Electronics Engineering, Kookmin University, Seoul 02707, Korea; [EMAIL_ADDRESS] (A.T.P); [EMAIL_ADDRESS] (I.B.K.Y.U).  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel: +82-02-910-5068  
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such as scratches, fractures, overlay flaws, and stress [5]. As a result, in industrial hygiene, monitoring, determining, and predicting the powder by-products and airborne PM in the cleanroom semiconductor factory are essential to avoid economic losses. This study aims to predict the concentrations of airborne PM in the semiconductor manufacturing facilities based on ten-day historical data gathered using oneM2M technology.  
Over the years, several approaches have been developed to predict and manage PM. [PERSON] et al. [6] utilized RNN incorporated with CMAQ (Community Multiscale Air Quality) to forecast PM2.5. [PERSON] et al. [7] employed the RNN model to predict PM2.5 concentrations, but the result of errors such as RMSE and MAE are high. [PERSON] et al. [8] used the long short-term memory (LSTM) and artificial neural network (ANN) models to forecast PM, which had a higher F-1 score than the individual scores of LSTM, ANN, and random forest (RF) models. [PERSON] et al. [9] forecasted PM2.5 in a smart city environment using a deep neural network (APNet) based on CNN-LSTM. [PERSON] et al. [10] combined CNN and LSTM (named CNN-LSTM) to predict PM2.5 concentrations. For improving forecasting accuracy, the CNN-LSTM used a convolutional neural network for feature extraction and a recurrent neural network for time series data processing.  
[PERSON] et al. [11] predicted 186 stations of PM2.5 concentrations using 2 layers of convolutional long short-term memory two-dimensional (CONVLSTM2D) and batch normalization. [PERSON] et al. [12] had forecasted the air quality index (AQI) containing O\({}_{3}\), CO, SO\({}_{2}\), NO\({}_{2}\), and PM2.5 based on support vector regression, but the accuracy of PM2.5 still had to be enhanced. [PERSON] et al. [13] had constructed a model to forecast PM2.5 using a combination of auto-encoder and BiLSTM neural networks. However, the results lacked metric comparison by only mentioning the RMSE and correlation coefficient.  
In our work, PM2.5 prediction was still used as a case study to demonstrate the Multi-Dense Layer BiLSTM model in order to prove and compare among the AI methods to the same object, as the advancement of the previous author's research [3], can successfully predict the time series data and even outperform several existing predicting strategies.  
The following are our specific contributions:  
* We used the hardware architecture, based on oneM2M technology, to achieve IoT system compatibility in the semiconductor factory cleanroom;
* We showed that our Multi-Dense Layer BiLSTM model can accurately forecast PM2.5 from multi-size PM concentration datasets (PM0.3, PM0.5, PM1, PM2.5, PM5, and PM10);
* We created a system with a small number of parameters, making it computationally efficient, potent, and stable;
* Our findings revealed that the Multi-Dense Layer BiLSTM approach yields the lowest error when compared to the RNN, LSTM, CNN-LSTM, and Single-Dense Layer BiLSTM methods.  
The following is a breakdown of the paper's structure. The overview of the system is provided in Section 2, while the experimental setup for PM prediction is highlighted in Section 3. The results of the experiment are evaluated and elaborated in Section 4. Finally, in Section 5, the paper is concluded, and ideas for further research are discussed.