# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Performance Assessment of Data Sufficiency  
To evaluate the impact of data sufficiency on prediction accuracy, we maintained a fixed size of the validation set at 3831 entries; varied the size of the training sets at 12,000, 14,000, 16,000, and 18,000, respectively; and utilized the remaining data for the test set. The remarkable performance of MsDFN in comparison to other baseline models, even with limited data, is evident in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(w\)**-\(F_{1}\)** & **R@3** & **R@5** & **R@7** \\ \hline SLFN [26] & 23.56 \(\pm\) 0.12 & 30.37 \(\pm\) 0.11 & 39.29 \(\pm\) 0.09 & 44.89 \(\pm\) 0.11 \\ SLFN\({}_{+}\)1[26] & 23.89 \(\pm\) 0.11 & 31.50 \(\pm\) 0.25 & 40.49 \(\pm\) 0.14 & 45.08 \(\pm\) 0.23 \\ MLP [7] & 23.60 \(\pm\) 0.07 & 30.40 \(\pm\) 0.16 & 39.32 \(\pm\) 0.17 & 44.96 \(\pm\) 0.17 \\ MLP\({}_{+}\)[7] & 23.98 \(\pm\) 0.21 & 31.60 \(\pm\) 0.15 & 40.54 \(\pm\) 0.21 & 45.14 \(\pm\) 0.33 \\ DBN [27] & 23.57 \(\pm\) 0.21 & 30.38 \(\pm\) 0.18 & 39.30 \(\pm\) 0.37 & 44.92 \(\pm\) 0.18 \\ DBN\({}_{+}\)[27] & 23.95 \(\pm\) 0.21 & 30.55 \(\pm\) 0.28 & 40.50 \(\pm\) 0.27 & 44.99 \(\pm\) 0.21 \\ CNN [9] & 29.70 \(\pm\) 0.20 & 36.40 \(\pm\) 0.68 & 42.22 \(\pm\) 0.87 & 46.26 \(\pm\) 0.78 \\ CNN\({}_{+}\)[9] & 32.22 \(\pm\) 0.28 & 35.50 \(\pm\) 0.17 & 44.75 \(\pm\) 0.23 & 48.88 \(\pm\) 0.22 \\ RNN [10] & 31.70 \(\pm\) 0.16 & 38.68 \(\pm\) 0.05 & 45.30 \(\pm\) 0.38 & 50.04 \(\pm\) 0.55 \\ RNN\({}_{+}\)[10] & 32.84 \(\pm\) 0.11 & 39.51 \(\pm\) 0.20 & 45.92 \(\pm\) 0.25 & 50.43 \(\pm\) 0.30 \\ LSTM [11] & 23.62 \(\pm\) 0.10 & 31.40 \(\pm\) 0.16 & 40.32 \(\pm\) 0.18 & 44.60 \(\pm\) 0.16 \\ LSTM, [11] & 24.90 \(\pm\) 0.36 & 32.07 \(\pm\) 0.44 & 41.82 \(\pm\) 0.11 & 45.96 \(\pm\) 0.21 \\ EFSAE-LSTM [28] & 23.68 \(\pm\) 0.12 & 31.43 \(\pm\) 0.26 & 40.36 \(\pm\) 0.28 & 44.63 \(\pm\) 0.15 \\ EFSAE-LSTM\({}_{+}\)[28] & 24.95 \(\pm\) 0.23 & 32.12 \(\pm\) 0.34 & 41.90 \(\pm\) 0.21 & 45.99 \(\pm\) 0.24 \\ Dipole [16] & 23.89 \(\pm\) 0.18 & 31.19 \(\pm\) 0.15 & 40.20 \(\pm\) 0.24 & 44.77 \(\pm\) 0.11 \\ Dipole, [16] & 25.31 \(\pm\) 0.18 & 34.70 \(\pm\) 0.58 & 41.68 \(\pm\) 0.59 & 45.65 \(\pm\) 0.47 \\ RETAIN [17] & 32.57 \(\pm\) 0.08 & 38.11 \(\pm\) 0.73 & 44.34 \(\pm\) 0.71 & 48.87 \(\pm\) 0.78 \\ RETAIN\({}_{+}\)[17] & 33.61 \(\pm\) 0.21 & 39.28 \(\pm\) 0.35 & 46.15 \(\pm\) 0.27 & 50.97 \(\pm\) 0.22 \\ Chet [21] & 30.65 \(\pm\) 0.11 & 39.05 \(\pm\) 0.13 & 45.10 \(\pm\) 0.13 & 49.42 \(\pm\) 0.18 \\ Chet, [21] & 33.25 \(\pm\) 0.18 & 39.10 \(\pm\) 0.16 & 45.99 \(\pm\) 0.24 & 50.80 \(\pm\) 0.27 \\ IKN-ConvLSTM [31] & 33.30 \(\pm\) 0.25 & 39.15 \(\pm\) 0.14 & 46.02 \(\pm\) 0.25 & 50.83 \(\pm\) 0.12 \\ MFDL [30] & 33.35 \(\pm\) 0.28 & 39.20 \(\pm\) 0.36 & 46.09 \(\pm\) 0.26 & 50.89 \(\pm\) 0.22 \\ MIFDELN [29] & 33.51 \(\pm\) 0.28 & 39.24 \(\pm\) 0.26 & 46.12 \(\pm\) 0.23 & 50.92 \(\pm\) 0.26 \\ MsDFN & 34.62 \(\pm\) 0.15 & 40.39 \(\pm\) 0.12 & 47.29 \(\pm\) 0.16 & 52.18 \(\pm\) 0.17 \\ \hline \hline \end{tabular} \({}^{1}\) Here, \({}_{+}\) indicates that historical maintenance projects, mileage, and vehicle base information are fused for prediction.  
\end{table}
Table 4: Maintenance project prediction results using \(w\)-\(F_{1}\)(%) and \(R@k\) (%).