# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 6 Results and Discussions
### Phase 2:  
To better anticipate mobility in VANET, a second phase presented the outcomes of the suggested and implemented approaches for enhancing machine learning with a hybrid optimization strategy. The project's implementation (H-WDFOA-VANET) was analyzed and compared with modern standards (RI-CS, EHACORP). Measured values for the delay, energy consumption, drop, throughput, and fairness index was computed and compared with both proposed (RI-CS) and existing (EHACORP) approaches. In addition, the suggested technique was tested on both the NS2 and MATLAB platforms. * Results Obtained Through Node Comparisons were made between the node of the proposed approach and existing techniques in terms of latency, energy consumption, drop, throughput, and fairness index (Figures 15-19). In the table below, we can see the actual values that were measured. Node values for both existing and proposed methods are displayed in Table 7. The major purpose of these performance measures was to evaluate the suggested model's capacity for mobility prediction in VANET. Results from a comparison of the proposed method with state-of-the-art approaches to mobility prediction in VANET are shown in Table 7, which shows that the proposed approach is superior. As such, the performance metrics indicated the proposed model's capacity for mobility prediction in VANET. Table 7 shows that, compared with other methods used to predicting mobility in VANET, the suggested technique, which uses a hybrid optimization strategy to improve machine learning, performs better. Better throughput is achieved using the proposed method. The suggested system achieved 2.00000 mJ of energy consumption, 15.61668 s of latency, and 0.15759 drop in node 60. Furthermore, the throughput attained by the new approach was higher than that of existing methods. There was also a reduction in energy use. Figure 14: Ratio of packet delivery. Figure 16: Plots of nodes vs. drop in the suggested approach and existing approaches. Figure 15: Plots of nodes vs. delays in the suggested approach and existing approaches. Figure 17: Plots of nodes vs. energy consumption in the suggested approach and existing approaches. Figure 18: Plots of nodes vs. fairness index in the suggested approach and existing approaches. Figure 19: Plots of nodes vs. throughput in the suggested approach and existing approaches. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Performance Parameters**} & \multirow{2}{*}{**Techniques**} & \multicolumn{4}{c}{**Nodes**} \\ \cline{3-6}  & **20** & **40** & **60** & **80** \\ \hline \multirow{3}{*}{**Delay (s)**} & **H-WDFOA-VANET** & 0.299063 & 10.05305 & 15.61668 & 15.64318 \\ \cline{2-6}  & **RI-CS** & 10.22709 & 29.63915 & 46.23192 & 46.03685 \\ \cline{2-6}  & **EHA-CORP** & 6.942181 & 17.16757 & 20.178139 & 19.568945 \\ \hline \multirow{3}{*}{**Energy consumption (mJ)**} & **H-WDFOA-VANET** & 7 & 3 & 2 & 2 \\ \cline{2-6}  & **RI-CS** & 8 & 5 & 4 & 3 \\ \cline{2-6}  & **EHA-CORP** & 8 & 4 & 3 & 2 \\ \hline \multirow{3}{*}{**Drop**} & **H-WDFOA-VANET** & 0.66674 & 0.17871 & 0.15759 & 0.15504 \\ \cline{2-6}  & **RI-CS** & 0.99904 & 0.6142 & 0.4456 & 0.3898 \\ \cline{2-6}  & **EHA-CORP** & 0.99293 & 0.521614 & 0.340694 & 0.308867 \\ \hline \multirow{3}{*}{**Throughput (Bps)**} & **H-WDFOA-VANET** & 41,661 & 27,995 & 30,560 & 40,095 \\ \cline{2-6}  & **RI-CS** & 13,582 & 18,785 & 202,302 & 22,299 \\ \cline{2-6}  & **EHA-CORP** & 25,022 & 22,043 & 26,556 & 31,937 \\ \hline \multirow{3}{*}{**Fairness Index (FI)**} & **H-WDFOA-VANET** & 66 & 17 & 15 & 15 \\ \cline{2-6}  & **RI-CS** & 99 & 61 & 44 & 38 \\ \cline{2-6}  & **EHA-CORP** & 99 & 52 & 34 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with existing approaches through nodes. Figure 23: Plots of speed vs. throughput in the suggested approach and existing approaches. Figure 22: Plots of speed vs. drop in the suggested approach and existing approaches. Additionally, the throughput achieved by the new approach is higher than that of the existing approaches.