Forest fire risk prediction is essential for building a forest fire defense system. Ensemble learning methods can avoid the problem of difficult model selection for disaster susceptibility prediction and can significantly improve modeling accuracy. This study introduces a stacking ensemble learning model for predicting forest fire risks in Yunnan Province by integrating various data types, such as meteorological, topographic, vegetation, and human activity factors. A total of 70,274 fire points and an equal number of randomly selected nonfire points were used to develop the model, with 70% of the data allocated for training and the remaining 30% for testing. The stacking model combined four diverse machine learning methods: random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). We evaluated the model’s predictive performance using metrics like accuracy, area under the characteristic curve (AUC), and fire density (FD). The results demonstrated that the stacking fusion model exhibited remarkable accuracy with an AUC of 0.970 on the test set, significantly surpassing the performance of individual machine learning models, which had AUC values ranging from 0.935 to 0.953. Furthermore, the stacking fusion model effectively captured the maximum fire density in extremely high susceptibility areas, demonstrating enhanced generalization capabilities.

A WGAN-GP-based ECG signal expansion and an SE-ResNet1D-based ECG classification method are proposed to address the problem of poor modeling results due to the imbalanced sample distribution of ECG data sets. The network architectures of WGAN-GP and SE-ResNet1D are designed according to the characteristics of ECG signals so that they can be better applied to the generation and classification of ECG signals. First, ECG data were generated using WGAN-GP on the MIT-BIH arrhythmia database to balance the dataset. Then, the experiments were performed using the AAMI category and inter-patient data partitioning principles, and classification experiments were performed using SE-ResNet1D on the imbalanced and balanced datasets, respectively, and compared with three networks, VGGNet, DenseNet and CNN+Bi-LSTM. The experimental results show that using WGAN-GP to balance the dataset can improve the accuracy and robustness of the model classification, and the proposed SE-ResNet1D outperforms the comparison model, with a precision of 95.80%, recall of 96.75% and an F1 measure of 96.27% on the balanced dataset. Our methods have the potential to be a useful diagnostic tool to assist cardiologists in the diagnosis of arrhythmias.

Using the data collected by the sensors embedded in smartphones for human activity recognition has become an important research direction. In order to overcome the problem of handcrafted feature extraction in traditional machine learning, we propose an attention-based model which combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) for human activity recognition. This model can extract local features of the original data by CNN, and construct the extracted features into a time series as the input of BiGRU, the BiGRU can capture the temporal dependencies and extract the features from the input, by introducing the attention mechanism, the influence of key input can be strengthened. The features are classified by the Softmax function. The recognition accuracy rate on the Wireless Sensor Data Mining (WISDM) dataset reaches 98.5%, which is improved compared with the state-of-the-art methods.

Log anomaly detection (LAD) is essential to ensure the safe and stable operation of Cyeber-physical systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their limitations in detection efficiency and generalization ability present a formidable challenge when dealing with evolving systems. To construct a real-time and reliable online log anomaly detection model, we propose OMLog, a semi-supervised online meta-learning method, to effectively tackle the distribution shift issue caused by changes in log event types and frequencies. Specifically, we introduce a maximum mean discrepancy-based distribution shift detection method to identify distribution changes in unseen log sequences. Depending on the identified distribution gap, the method can automatically trigger online fine-grained detection or offline fast inference. Furthermore, we design an online learning mechanism based on meta-learning, which can effectively learn the highly repetitive patterns of log sequences in the feature space, thereby enhancing the generalization ability of the model to evolving data. Extensive experiments conducted on two publicly available log datasets, HDFS and BGL, validate the effectiveness of the OMLog approach. When trained using only normal log sequences, the proposed approach achieves the F1-Score of 93.7% and 64.9%, respectively, surpassing the performance of the state-of-the-art (SOTA) LAD methods and demonstrating superior detection efficiency.