Search

The rockburst hazard induced by the extreme release of the stress concentrated in rock mass in deep underground mines poses a significant threat to the safety and economy of the mining projects. Therefore, properly managing this hazard is critical for ensuring rock engineering projects' sustainability. This study proposes comprehensible and practical classifiers for rockburst risk level appraisal by hybridizing K-means clustering with gene expression programming, GEP, logistic regression, LR, and classification and regression tree, CART (i.e., K-mean-GEP-LR and K-means-CART classifiers). A database containing 246 rockburst events with four risk levels of none, light, moderate, and severe was compiled from previous practices. Preliminary statistical analyses were conducted to detect the extreme outliers and determine the critical rockburst indicators. The K-means clustering analysis was performed to identify the main clusters within the database and relabel the rockburst events. The GEP algorithm was then utilized to develop binary models for predicting the occurrence of each class. Then, the likelihood of each class occurrence was determined using LR. Furthermore, the K-means clustering was combined with the CART algorithm to provide another visual tree structure model. The classifiers' performance evaluation showed 96% and 95% accuracy values in the training and testing stages, respectively, for the K-means-GEP-LR model, while the accuracy values of 98.8% and 93.0% were obtained for the foregoing stages for the K-means-CART classifier. The results showed the robustness and high classification capability of both models. MatLab codes were also provided for the K-means-GEP-LR model, which assists other researchers/engineers in implementing the model in practice. [ABSTRACT FROM AUTHOR]

Based on authorized patents of China's artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm's innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers. [ABSTRACT FROM AUTHOR]

ABSTRACT This study examines the landfalling tropical cyclones ( TCs) over China using state-of-the-art data mining methods (i.e. Finite Mixture Model ( FMM) based cluster algorithm and the Classification and Regression Tree ( CART)). Using the 1951-2012 TC best track dataset released by the Shanghai Typhoon Institute of the Chinese Meteorological Administration, the tracks of TCs landfalling over the Chinese coast were classified into three clusters through an FMM. Several climate indices were analysed using the CART algorithm for the three clusters. The prediction model built by CART for summer track frequency was based on a random sampling of the data for 46 years (about 75% of the total years) as the training set with a training accuracy of 100% (Cluster-1), 89.96% (Cluster-2) and 100% (Cluster-3). Data for the remaining 16 years (about 25%) were used for testing with a prediction accuracy of 87.5% (Cluster-1), 62.5% (Cluster-2) and 68.75% (Cluster-3). This study focuses on Cluster-1 of summer TCs landfalling over China for its high frequency, strong intensity, severe impacts and long lifespan. Furthermore, it suggests that the FMM algorithm is effective for track classification of TCs landing over China. In addition, the CART algorithm, which was used to build the prediction model of Cluster-1 for the classification of track frequency, showed high accuracy and its results can be explained and understood easily. It provides a novel framework for forecasting the frequency of TCs landfalling over China. [ABSTRACT FROM AUTHOR]