This study enhances the bidirectional long short-term memory (Bi-LSTM) model by incorporating an attention mechanism, which could provide the model with stronger data generalization capabilities. Moreover, it can predict a broader range of data and exhibits enhanced handling and adaptability to anomalies. Through the utilization of the attention mechanism, this research partitions the weights of the feature values, precisely dividing the input LSTM’s feature values based on their weights. This enables the Bi-LSTM to more accurately capture relationships between different feature values in time series and dependencies on various features. Given the diverse air quality conditions in different regions, the introduced attention mechanism in Bi-LSTM manages the weights of different feature values. The Bi-LSTM, enhanced with attention mechanisms, excels at handling relationships in time series data, allowing it to predict PM2.5 values in more complex air quality environments. It demonstrates improved capabilities in handling anomalies. Even in air quality scenarios with various complex conditions, the model maintains satisfactory predictive quality.
Picture division has a special meaning for computer visualization and schema identification. Fast target extraction from deterministic images is an important problem facing real-time picture manipulation. Traditional areal models rely on globally converged messages to achieve fault-minimized segmentation. Its image segmentation is ineffective and takes up a lot of time. To address this problem, this paper proposes a model that Fast Region Image Segmentation of the Least Squares (FRISLS). Specifically, the target as well as the backdrop of the primary picture is approximated by just a pair of constants in order to establish the minimum error function. The weight matrix is used to reduce the influence of the background on image segmentation, and least squares are introduced to achieve fast convergence of the model. Through comparison with other area model-based approaches, it is validated the effectiveness of the study. The results indicate that this method ensures high precision of picture division, and enhances the performance of picture splitting efficiency.
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