# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Parametric Sensitivity Analysis  
To explore the influence of variable dimensionality on the performance of MsDFN, we scrutinized the impact of \(E\) and \(U\) dimensions on the experimental results. Figure 5 shows the performance of the proposed MsDFN model across various hyperparameter combinations. Notably, we ascertained that an optimal balance between efficiency and performance is achieved by setting the \(E\) and \(U\) dimensions at approximately 75. Furthermore, we sought to evaluate the significance of the co-occurrence matrix threshold \(\delta\) in Equation (1) on the experimental results, examining thresholds including 0.04, 0.05, 0.06, 0.07, 0.08, and 0.09. Remarkably, as illustrated in Figure 6, the overall performance remains relatively stable, with the most favorable results attained at a threshold of 0.07.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & \(\mathbf{w}\)-\(\mathbf{F_{1}}\) & \(\mathbf{R@3}\) & \(\mathbf{R@5}\) & \(\mathbf{R@7}\) \\ \hline SLFN [26] & \(6.89\pm 0.128\) & \(8.38\pm 0.26\) & \(13.78\pm 0.66\) & \(18.70\pm 0.34\) \\ SLFN\({}_{+}\)1 [26] & \(11.32\pm 0.13\) & \(11.33\pm 0.34\) & \(17.74\pm 0.57\) & \(22.68\pm 0.45\) \\ MLP [7] & \(6.99\pm 0.18\) & \(8.42\pm 0.16\) & \(13.82\pm 0.96\) & \(18.79\pm 0.44\) \\ MLP\({}_{+}\)[7] & \(11.36\pm 0.10\) & \(11.38\pm 0.44\) & \(17.80\pm 0.67\) & \(22.71\pm 0.75\) \\ DBN [27] & \(6.94\pm 0.31\) & \(8.40\pm 0.26\) & \(13.80\pm 0.36\) & \(18.75\pm 0.34\) \\ DBN \({}_{+}\)[27] & \(11.33\pm 0.21\) & \(11.36\pm 0.23\) & \(17.78\pm 0.56\) & \(22.69\pm 0.45\) \\ CNN [9] & \(11.00\pm 0.23\) & \(11.19\pm 0.70\) & \(17.58\pm 0.49\) & \(22.25\pm 0.30\) \\ CNN\({}_{+}\)[9] & \(11.80\pm 0.42\) & \(11.76\pm 0.56\) & \(18.04\pm 0.64\) & \(23.26\pm 0.63\) \\ RNN [10] & \(7.24\pm 0.11\) & \(8.82\pm 0.55\) & \(14.32\pm 0.39\) & \(18.35\pm 0.53\) \\ RNN\({}_{+}\)[10] & \(11.23\pm 0.21\) & \(11.41\pm 0.21\) & \(17.83\pm 0.21\) & \(22.76\pm 0.21\) \\ LSTM [11] & \(7.31\pm 0.21\) & \(8.83\pm 0.56\) & \(14.08\pm 0.85\) & \(18.64\pm 0.70\) \\ LSTM\({}_{+}\)[11] & \(10.99\pm 0.23\) & \(11.21\pm 0.23\) & \(17.48\pm 0.23\) & \(22.26\pm 0.23\) \\ EFMSAE-LSTM [28] & \(7.42\pm 0.32\) & \(8.89\pm 0.36\) & \(14.04\pm 0.65\) & \(18.62\pm 0.57\) \\ EFMSAE-LSTM\({}_{+}\)[28] & \(10.96\pm 0.43\) & \(11.18\pm 0.21\) & \(17.44\pm 0.21\) & \(22.23\pm 0.13\) \\ Dipole [16] & \(7.75\pm 0.13\) & \(10.76\pm 0.17\) & \(15.42\pm 0.40\) & \(19.16\pm 0.58\) \\ Dipole, [16] & \(13.07\pm 0.29\) & \(13.97\pm 0.29\) & \(19.55\pm 0.29\) & \(23.90\pm 0.29\) \\ RETAIN [17] & \(12.50\pm 0.47\) & \(12.25\pm 0.15\) & \(17.22\pm 0.11\) & \(21.11\pm 0.17\) \\ RETAIN\({}_{+}\)[17] & \(14.72\pm 0.33\) & \(15.64\pm 0.33\) & \(22.08\pm 0.33\) & \(27.04\pm 0.33\) \\ Chet [21] & \(11.19\pm 0.11\) & \(14.02\pm 0.13\) & \(19.27\pm 0.13\) & \(23.93\pm 0.18\) \\ Chet\({}_{+}\)[21] & \(12.73\pm 0.18\) & \(14.57\pm 0.12\) & \(20.86\pm 0.20\) & \(24.88\pm 0.37\) \\ IKN-ConvLSTM [31] & \(12.83\pm 0.21\) & \(14.60\pm 0.16\) & \(20.92\pm 0.24\) & \(24.98\pm 0.39\) \\ MFDL [30] & \(12.85\pm 0.31\) & \(14.61\pm 0.13\) & \(20.95\pm 0.23\) & \(25.02\pm 0.31\) \\ MIFDELN [29] & \(12.88\pm 0.11\) & \(14.66\pm 0.15\) & \(20.99\pm 0.22\) & \(25.08\pm 0.32\) \\ MsDFN & \(15.65\pm 0.17\) & \(16.79\pm 0.35\) & \(23.27\pm 0.33\) & \(28.18\pm 0.27\) \\ \hline \end{tabular} \({}^{1}\) Here, \({}_{+}\) indicates that historical maintenance projects, mileage, and vehicle base information are fused for prediction.  
\end{table}
Table 6: New maintenance project prediction results using \(w\)-\(F_{1}\)(%) and \(\mathbf{R@k}\) (%).  
Figure 5: The performance under different dimensions of \(E\) and \(U\) combinations.