# Blockchain and Machine Learning Inspired Secure Smart Home Communication Network
## 4 Results and Discussion
The authentication rate in Figure 5 represents the total number of false detections while applying authentication over the users. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Total Test** & \begin{tabular}{c} **Fused Real-Time Sequential** \\ **Deep Extreme Learning** \\ **Machine System**[3] \\ \end{tabular} & **Data Fusion Technique**[11] & \
\begin{tabular}{c} **Internet of Things for** \\ **Electronic Information** \\ **Engineering and** \\ **Optimization Schemes**[17] \\ \end{tabular} &
\begin{tabular}{c} **Proposed** \\ **Technique** \\ **Technique** \\ \end{tabular} \\ \hline
500 & 0.85991282 & 0.8780829 & 0.84910773 & 0.76528091 \\ \hline
1000 & 2.11021973 & 1.9531654 & 2.04723132 & 1.87965372 \\ \hline
1500 & 1.78525512 & 1.7024945 & 1.82948338 & 1.67876619 \\ \hline
2000 & 2.05459249 & 2.1310947 & 2.19553898 & 2.03360384 \\ \hline
2500 & 3.74510098 & 3.6378217 & 3.52360793 & 3.46864933 \\ \hline
3000 & 3.34175878 & 3.4041558 & 3.48210032 & 3.26647149 \\ \hline
3500 & 4.86868341 & 5.0819671 & 4.60406431 & 4.44362262 \\ \hline
4000 & 4.22346545 & 4.2047017 & 4.47550357 & 4.06799277 \\ \hline
4500 & 4.91378696 & 4.7681754 & 5.12593208 & 4.50671531 \\ \hline
5000 & 6.0492668 & 6.4493703 & 6.17813064 & 5.77015973 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Computation complexity. Figure 5: Number of false authentications against [PERSON] et al. [3], [PERSON] et al. [11], and [PERSON] et al. [21]. The proposed algorithm showed lower numbers of false authentications compared with the other algorithms. This was reflected in the % improvement values, which show the percent reduction in false authentications compared with each of the other algorithms. For example, in a total test sample size of 500, the proposed algorithm had 18 false authentications, while the next-best algorithm had 19 false authentications. This represents a % improvement of 5.26% for the proposed algorithm. Similarly, in a total test sample size of 5000, the proposed algorithm had 173 false authentications, while the other had 185 false authentications. This represents a % improvement of 6.49% for the proposed algorithm. As illustrated in this section, the proposed work was also evaluated for qualitative parameters. Table 6 presents the qualitative analysis. In terms of precision, the proposed method performed consistently well, with an average precision of around 0.97-0.98. The NB and RF methods had similar precision scores for most cases, but the RF method had lower precision for some test sample sizes, particularly for larger test samples. For recall, the proposed method performed well, with an average score of around 0.98-0.99. The NB and RF methods also performed well but with slightly lower scores than the proposed method. In terms of the F-measure, the proposed method again performed consistently well, with an average score of around 0.96-0.99. The NB and RF methods had similar F-measure scores in most cases, but the RF method had a lower F-measure for some test sample sizes, particularly for larger test samples. The performance of the proposed work was further compared for the prediction accuracy of false authentications. Figure 6 shows the comparative analysis of the proposed work performed against the existing works in terms of the prediction of false authentications. It was observed that the proposed work exhibited a high accuracy of 96.54%, which is a little higher than that exhibited by [PERSON] (95.28%) and [PERSON] (92%), thus justifying the effectiveness of the proposed work in smart home networks. The proposed work reduced the computation complexity in terms of overall computation, which will help to transfer more data in the network. The network is capable of running other real-time datasets as well if they are designated to work on a similar aspect.