In this study, an innovative prediction framework for intelligent management and allocation decisions of tourism resources is constructed by integrating the iterative local search strategy and the sparrow search algorithm to co-optimize the random forest model. The experimental results show that the tourism resource allocation level and the number of tourists show significant correlation (Pearson coefficient 0.73), and this indicator as a core driver directly affects the resource allocation decision; the resource utilization rate, as a secondary correlation variable (correlation coefficient 0.54), and the scenic spot operational efficiency form a two-way feedback mechanism, which together constitute the key influencing factors of the dynamic allocation of tourism resources. Through the quantitative analysis of the confusion matrix, the optimized model achieves 100% perfect fit in the training set, and maintains 95% prediction accuracy (190/200 samples are correctly classified) in the independent test set, and the performance degradation of only 5% fully proves the success of the algorithmic improvement strategy in avoiding overfitting, and its excellent generalization characteristic breaks through the limitations of the traditional prediction model in cross-scene applications.

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In the era of digital transformation, big data has emerged as a pivotal resource driving innovation across various sectors. The effective utilization of big data, however,is fundamentally contingent upon robust governance frameworks. This paper conducts a comprehensive exploration of the multifaceted domain of big data governance, with a focus on three critical dimensions: key technological implementations, policy-system architecture, and application prospects. By employing a combination of literature review, case analysis, and comparative research methods, it addresses several key research questions, such as how to improve the efficiency and quality of data management in big data governance, how to construct a scientific and reasonable policy system, and how to better apply big data governance in different scenarios. The research findings demonstrate that advanced techniques, particularly data wrangling, play an indispensable role in data preprocessing and quality enhancement. Furthermore, the study reveals that a sophisticated, multi-tiered policy system involving multiple stakeholders is progressively evolving in the realm of policy-system construction. In terms of application, big data governance has achieved remarkable results in areas such as government decision-making and business operations, but challenges remain. Overall, this research provides valuable insights for promoting the development and application of big data governance.

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This paper addresses the issues of weak model transferability and poor environmental adaptability in cross-domain gesture recognition within wireless sensing technology. A time-series data classification model integrating Long Short-Term Memory (LSTM) and attention mechanism (W-LSTM+A) is proposed. By introducing a feature selection weight matrix to reconstruct the LSTM gating mechanism and combining a dynamic attention allocation strategy, the model’s ability to capture key spatiotemporal features in channel state information is significantly enhanced. Experiments based on a WiFi signal dataset collected in a real office environment compared the performance of CNN, LSTM, and LSTM+A models. The results show that the LSTM+A model achieved a test accuracy of 87.3% after 200 training epochs, significantly outperforming CNN’s 81.9%. Although the LSTM model had a higher final accuracy, its convergence speed was significantly slower than that of the LSTM+A model. Further analysis indicates that the attention mechanism, by strengthening key time-step features, enables the model to quickly capture effective patterns in the early stages of training. However, due to limited sample size, its potential has not been fully realized. This study provides new solutions for the cross-scene adaptability of wireless sensing technology and has application value in smart homes, health monitoring, and other fields.

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Deep learning and neural networks have become widely applied in signal modulation recognition, offering numerous advantages in performance. Traditional approaches to modulation recognition using neural networks typically focus on improving model accuracy, which often results in increased model size and computational complexity. This makes deployment on mobile devices challenging. Therefore, this study aims to reduce the model size while ensuring recognition accuracy and proposes a lightweight neural network architecture based on MobileNetV4. The network incorporates an inverted bottleneck structure, which helps reduce the model’s running time through depthwise separable convolution and residual concatenation. The performance of the model was evaluated on the public dataset RadioML 2018.01A dataset across multiple signal-to-noise ratios and compared with convolutional neural networks and residual networks. The results show that the proposed network reduces the running time to approximately 1/2 to 1/3 of the original models while maintaining comparable or even slightly improved recognition accuracy.

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