This study addresses challenges in sentiment classification accuracy and time series forecasting precision by analyzing visitor reviews and flow data for the Dunhuang Mogao Grottoes. Using a Convolutional Neural Netwo...
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Tourist flow prediction faces challenges in achieving high accuracy due to the complex and dynamic nature of tourismdata. This study addresses these challenges by proposing a multi-feature combined prediction model, ...
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This study addresses challenges in sentiment classification accuracy and time series forecasting precision by analyzing visitor reviews and flow data for the Dunhuang Mogao Grottoes. Using a Convolutional Neural Netwo...
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ISBN:
(数字)9798350380347
ISBN:
(纸本)9798350380354
This study addresses challenges in sentiment classification accuracy and time series forecasting precision by analyzing visitor reviews and flow data for the Dunhuang Mogao Grottoes. Using a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model for sentiment analysis, the study demonstrated superior performance in classifying positive reviews compared to traditional machine learning and Text Convolutional Neural Network (TextCNN) models, despite slightly lower recall and accuracy for negative reviews. Time series forecasting with the Seasonal-Trend Decomposition using Loess-Long Short-Term Memory (STLLSTM) model exhibited higher accuracy for short periods (e.g., 2 days), with an average MAE of 35.71, RMSE of 59.02, and $R^{2}$ of 0.93, though accuracy decreased over longer periods (e.g., 30 days). This research provides insights into visitor sentiments and patterns, offering management and strategy recommendations to enhance visitor experiences. Future studies should refine these models to improve accuracy and efficiency.
Tourist flow prediction faces challenges in achieving high accuracy due to the complex and dynamic nature of tourismdata. This study addresses these challenges by proposing a multi-feature combined prediction model, ...
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ISBN:
(数字)9798350380347
ISBN:
(纸本)9798350380354
Tourist flow prediction faces challenges in achieving high accuracy due to the complex and dynamic nature of tourismdata. This study addresses these challenges by proposing a multi-feature combined prediction model, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Incremental Entropy-K-means clustering-Time Convolutional Network-Coati Optimization Algorithm (CEEMDAN-IEK-TT-COA). The model integrates signal decomposition, clustering reconstruction, component prediction, and optimization algorithms. key features are selected through feature engineering, followed by data decomposition and reconstruction using CEEMDAN, Incremental Entropy, and K-means clustering. A model pool of Time Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory network (LSTM), and Transformer filters optimal sub-models for combined prediction. The Coati Optimization Algorithm (COA) refines the final prediction. Experimental results on Jiuzhaigou and Siguniangshan datasets demonstrate significant RMSE improvements of $21.81 \%$ and $17.84 \%$, validating the model’s effectiveness.
Grassland monitoring is essential for the sustainable development of grassland resources. Traditional Internet of Things (IoT) devices generate critical ecological data, making data loss unacceptable, but the harsh en...
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