Research Article
[Retracted] A Multi-RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency-Based Scientific Influence Modeling
Table 3
RMSE and precision of different models of different fields.
| (a) Precision of different models of CL field | ā | p@10 | p@20 | p@30 | p@40 | p@50 | p@60 | p@70 | p@80 |
| ARIMA | 0.6000 | 0.5000 | 0.5333 | 0.5000 | 0.4600 | 0.4667 | 0.4857 | 0.4750 | GRU | 0.7000 | 0.8000 | 0.8000 | 0.7250 | 0.7200 | 0.6500 | 0.6571 | 0.6750 | LSTM | 0.7000 | 0.8000 | 0.7667 | 0.7750 | 0.7200 | 0.7167 | 0.7000 | 0.7250 | ENDE | 0.6000 | 0.6000 | 0.6000 | 0.5500 | 0.5800 | 0.5667 | 0.5286 | 0.5500 | TARNN | 0.6000 | 0.6000 | 0.6000 | 0.5500 | 0.5600 | 0.5500 | 0.5286 | 0.5375 | DARNN | 0.6000 | 0.6000 | 0.6000 | 0.5500 | 0.5800 | 0.5667 | 0.5286 | 0.5375 | CONI | 0.7000 | 0.8000 | 0.7667 | 0.7250 | 0.6800 | 0.6333 | 0.6286 | 0.6750 | SASC | 0.7000 | 0.9000 | 0.9000 | 0.8500 | 0.8000 | 0.8167 | 0.8143 | 0.8000 |
| (b) Precision of different models of CV field | ARIMA | 0.8000 | 0.6000 | 0.6000 | 0.6250 | 0.5400 | 0.5500 | 0.5714 | 0.6250 | GRU | 0.9000 | 0.6000 | 0.6667 | 0.6250 | 0.5600 | 0.5667 | 0.6000 | 0.6375 | LSTM | 0.9000 | 0.6000 | 0.6667 | 0.6250 | 0.5800 | 0.5833 | 0.6000 | 0.6375 | ENDE | 0.9000 | 0.6000 | 0.6667 | 0.6250 | 0.5800 | 0.5667 | 0.6000 | 0.6375 | TARNN | 0.9000 | 0.6000 | 0.6667 | 0.6250 | 0.5600 | 0.5833 | 0.6000 | 0.6250 | DARNN | 0.9000 | 0.6000 | 0.6667 | 0.6250 | 0.5800 | 0.5667 | 0.5857 | 0.6500 | CONI | 0.9000 | 0.6500 | 0.7000 | 0.6750 | 0.6800 | 0.7000 | 0.6857 | 0.7000 | SASC | 1.0000 | 0.9000 | 0.8333 | 0.8750 | 0.8400 | 0.8333 | 0.8714 | 0.8500 |
| (c) Precision of different models of ML field | ARIMA | 0.6000 | 0.5500 | 0.5333 | 0.6000 | 0.5800 | 0.5500 | 0.5286 | 0.6125 | GRU | 0.7000 | 0.6000 | 0.6000 | 0.7000 | 0.6200 | 0.5500 | 0.6143 | 0.6250 | LSTM | 0.8000 | 0.8500 | 0.7667 | 0.7750 | 0.7400 | 0.7500 | 0.7714 | 0.7875 | ENDE | 0.7000 | 0.6000 | 0.6000 | 0.7000 | 0.6200 | 0.5667 | 0.6143 | 0.6250 | TARNN | 0.7000 | 0.6000 | 0.6000 | 0.7000 | 0.6200 | 0.5833 | 0.6143 | 0.6375 | DARNN | 0.7000 | 0.6000 | 0.6000 | 0.7000 | 0.6200 | 0.5833 | 0.6143 | 0.6250 | CONI | 0.7000 | 0.6000 | 0.6000 | 0.7000 | 0.6200 | 0.5833 | 0.6143 | 0.6250 | SASC | 1.0000 | 0.9000 | 0.9000 | 0.9000 | 0.9000 | 0.9000 | 0.8714 | 0.9250 |
| (d) Precision of different models of IR field | ARIMA | 0.7000 | 0.5500 | 0.6333 | 0.7000 | 0.7000 | 0.7000 | 0.7000 | 0.7250 | GRU | 0.8000 | 0.6000 | 0.7000 | 0.8000 | 0.7600 | 0.8167 | 0.8000 | 0.7875 | LSTM | 0.8000 | 0.6000 | 0.6333 | 0.7250 | 0.7600 | 0.7500 | 0.7571 | 0.7625 | ENDE | 0.8000 | 0.5500 | 0.6667 | 0.7000 | 0.7600 | 0.7000 | 0.7429 | 0.7625 | TARNN | 0.8000 | 0.6000 | 0.6667 | 0.7250 | 0.7600 | 0.7167 | 0.7429 | 0.7500 | DARNN | 0.8000 | 0.5500 | 0.6333 | 0.7250 | 0.7600 | 0.7167 | 0.7429 | 0.7500 | CONI | 0.8000 | 0.5500 | 0.6333 | 0.7250 | 0.7600 | 0.7167 | 0.7625 | 0.7556 | SASC | 0.8000 | 0.7500 | 0.8000 | 0.8000 | 0.8000 | 0.8333 | 0.8143 | 0.8000 |
| (e) Precision of different models of AI field | ARIMA | 0.5000 | 0.5500 | 0.5000 | 0.5750 | 0.6000 | 0.6667 | 0.6857 | 0.6375 | GRU | 0.7000 | 0.6500 | 0.5000 | 0.5750 | 0.6200 | 0.6833 | 0.7000 | 0.6750 | LSTM | 0.6000 | 0.7000 | 0.5000 | 0.5750 | 0.6200 | 0.6667 | 0.7000 | 0.6625 | ENDE | 0.6000 | 0.6500 | 0.5333 | 0.5750 | 0.6200 | 0.6500 | 0.7000 | 0.6500 | TARNN | 0.6000 | 0.6500 | 0.5333 | 0.5750 | 0.6200 | 0.6500 | 0.6714 | 0.6750 | DARNN | 0.6000 | 0.6500 | 0.5000 | 0.5750 | 0.6200 | 0.6833 | 0.7000 | 0.6500 | CONI | 0.6000 | 0.6500 | 0.5000 | 0.6000 | 0.6400 | 0.6667 | 0.7000 | 0.6500 | SASC | 0.9000 | 0.8500 | 0.8333 | 0.9000 | 0.9000 | 0.8667 | 0.8429 | 0.8250 | (f) Average precision of different models of five fields | ARIMA | 0.6400 | 0.5500 | 0.5600 | 0.6000 | 0.5760 | 0.5867 | 0.5943 | 0.6150 | GRU | 0.7600 | 0.6500 | 0.6533 | 0.6850 | 0.6560 | 0.6533 | 0.6743 | 0.6800 | LSTM | 0.7600 | 0.7100 | 0.6667 | 0.6950 | 0.6840 | 0.6933 | 0.7057 | 0.7150 | ENDE | 0.7200 | 0.6000 | 0.6133 | 0.6300 | 0.6320 | 0.6100 | 0.6371 | 0.6450 | TARNN | 0.7200 | 0.6100 | 0.6133 | 0.6350 | 0.6240 | 0.6167 | 0.6314 | 0.6450 | DARNN | 0.7200 | 0.6000 | 0.6000 | 0.6350 | 0.6320 | 0.6233 | 0.6343 | 0.6425 | CONI | 0.7400 | 0.6500 | 0.6400 | 0.6850 | 0.6760 | 0.6600 | 0.6714 | 0.6825 | SASC | 0.8800 | 0.8600 | 0.8533 | 0.8650 | 0.8480 | 0.8500 | 0.8429 | 0.8400 |
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