## 5 Conclusions  
This study proposed an enhanced IAQMS which could recognize the pollutants by the utilized supervised machine learning algorithm that is widely used in data mining. With regards to pollutant recognition, it can be concluded that this IAQMS has successfully recognized five sources of indoor air pollution with a classification rate of 100 percent. These five sources of indoor air pollution--ambient air, combustion activity, presence of chemicals, presence of fragrances and presence of food and beverages--are successfully classified by MLP and KNN using the VAN before PCA feature. To prove that all the five sources of indoor air pollution are mutually exclusive, a 3-D graph has been attached, as shown in Section 4. Three classifiers have been tested to choose for the best classifier: MLP, KNN and LDA. In the end, the MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into the IAQMS because it is more suitable to be embedded in the system. A model structure with fewer hidden variables has a less complicated formula and is therefore easy to embed. KNN requires a large storage space in the system because it saves every data that it receives. MLP, on the other hand, does not require a large storage system. Due to these reasons, the MLP classifier is chosen for this project. The system has also been tested with multiple sources of IAP presented together. The result shows that the system is able to classify when single and two mixed sources are presented together; however, when more than two sources of IAP are presented at the same period, the system will classify the sources as 'unknown' because the system cannot recognize the input of the new pattern. Hence, the classification accuracy falls dramatically. Future research should consider expanding the sources of indoor air pollution to include more sources of indoor air pollution. In addition, the proxy for each condition could also be added to include more activity and to test the efficiency of IAQ that can detect more sources of IAP such as furniture (wood, plastic), building materials (plaster, insulation),  
Figure 11: Sensor’s response when additional sources of IAP are present. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Data Samples**} & \multicolumn{6}{c}{**Sources of IAQ Pollutant**} \\ \cline{3-8}  & & **Ambient** & **Fragrance** & **Combustion Activity** & **Food \& **Beverages** & **Chemical** & **Unknown** \\ \hline Single source & 45 & 45 & 0 & 0 & 0 & 0 & 0 \\ Mixed sources A & 75 & 7 & 68 & 0 & 0 & 0 & 0 \\ Mixed sources B & 10 & 0 & 1 & 7 & 0 & 0 & 2 \\ Mixed sources C & 20 & 0 & 0 & 1 & 2 & 7 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Result of the classifier based on mixed sources. and coatings (carpeting, painting). In order to sense mixed sources, more sensitive sensors might provide more insight; more sensitive sensors with the ability to distinguish the different sources need to used. Furthermore, the system can also be trained with two or more mixed sources, for example, mixing between air freshener with smoking activity and the presence of food and beverages. For this study, a neural network (NN) used to classify the sources of indoor air pollution is embedded into the IAQMS only. Therefore, this study would like to suggest that the NN should be embedded onto the microcontroller for future research. If the classifier is embedded onto the microcontroller, the IAQMS could operate as a stand-alone device, which would enable the IAQMS to send an alert to users (via short massage system (SMS)) if the air quality level in the observed room changed to a \"bad\" or \"hazardous\" level. The equipment used in this project was pre-developed by Universit Malaysia Perlis (UniMAP) for previous studies. The authors would like to offer a special thanks to the members of the Centre of Excellence for Advanced Sensor Technology (CEATech), UniMAP, Malaysia for their critical advice and warm cooperation.