Based on the automatic machine learning framework, combined with the characteristics of forest fire meteorological data and the adaptive requirements of forest fire prediction, this paper optimizes the data preprocessing, parameter learning, loss function and other links of auto-sklearn, builds a forest fire risk prediction framework with regional adaptive characteristics. Based on the forest meteorological fire risk data, a forest fire risk prediction model with regional characteristics and self-learning characteristics is constructed to solve the problems of low compatibility of the existing machine learning methods with binary unbalanced forest fire data, improve the accuracy of forest fire prediction and provide decision-making basis for forestry risk management. The comparative analysis results show that the prediction accuracy of the improved framework in different test sets is improved by 13% on average. Compared with the existing machine learning model for forest fire prediction, the prediction accuracy of the framework proposed in this paper is comprehensively better than the existing methods in terms of real forest fire data.

Cerebral palsy is one of the most prevalent neurological disorders and the most frequent cause of disability. Identifying the syndrome by patients’ symptoms is the key to traditional Chinese medicine (TCM) cerebral palsy treatment. Artificial intelligence (AI) is advancing quickly in several sectors, including TCM. AI will considerably enhance the dependability and precision of diagnoses, expanding effective treatment methods’ usage. Thus, for cerebral palsy, it is necessary to build a decision-making model to aid in the syndrome diagnosis process. While the recurrent neural network (RNN) model has the potential to capture the correlation between symptoms and syndromes from electronic medical records (EMRs), it lacks TCM knowledge. To make the model benefit from both TCM knowledge and EMRs, unlike the ordinary training routine, we begin by constructing a knowledge-based RNN (KBRNN) based on the cerebral palsy knowledge graph for domain knowledge. More specifically, we design an evolution algorithm for extracting knowledge in the cerebral palsy knowledge graph. Then, we embed the knowledge into tensors and inject them into the RNN. In addition, the KBRNN can benefit from the labeled EMRs. We use EMRs to fine-tune the KBRNN, which improves prediction accuracy. Our study shows that knowledge injection can effectively improve the model effect. The KBRNN can achieve 79.31% diagnostic accuracy with only knowledge injection. Moreover, the KBRNN can be further trained by the EMRs. The results show that the accuracy of fully trained KBRNN is 83.12%.

Pattern Sequence-based Forecasting (PSF) is an effective method for time series prediction. However, the accuracy of this method depends on the selection of parameters such as the length of the pattern sequence and the number of clusters. In diverse time series data sets, these parameters are often priori unknown. This paper innovatively introduces a pattern mining method before the PSF pattern clustering to guide the clustering process and realize the automation of initial parameter selection. Experimental results show that the method proposed in this paper effectively eliminates the uncertainty of PSF caused by the selection of initial parameters. Compared with the original model, it improves the efficiency while ensuring the advantage of prediction accuracy.