In this investigation,the Gradient Boosting(GB),Linear Regression(LR),Decision Tree(DT),and Voting algo-rithms were applied to predict the distribution pattern of Au geochemical data.Trace and indicator elements,inclu...In this investigation,the Gradient Boosting(GB),Linear Regression(LR),Decision Tree(DT),and Voting algo-rithms were applied to predict the distribution pattern of Au geochemical data.Trace and indicator elements,including Mo,Cu,Pb,Zn,Ag,Ni,Co,Mn,Fe,and As,were used with these machine learning algorithms(MLAs)to predict Au concentration values in the Doostbigloo porphyry Cu-Au-Mo mineralization area.The performance of the models was evaluated using the Mean Absolute Percentage Error(MAPE)and Root Mean Square Error(RMSE)metrics.The proposed ensemble Voting algorithm outperformed the other models,yielding more ac-curate predictions according to both metrics.The predicted data from the GB,LR,DT,and Voting MLAs were modeled using the Concentration-Area fractal method,and Au geochemical anomalies were mapped.To compare and validate the results,factors such as the location of the mineral deposits,their surface extent,and mineralization trend were considered.The results indicate that integrating hybrid MLAs with fractal modeling signifi-cantly improves geochemical prospectivity mapping.Among the four models,three(DT,GB,Voting)accurately identified both mineral deposits.The LR model,however,only identified Deposit I(central),and its mineralization trend diverged from the field data.The GB and Voting models produced similar results,with their final maps derived from fractal modeling showing the same anomalous areas.The anomaly boundaries identified by these two models are consistent with the two known reserves in the region.The results and plots related to prediction indicators and error rates for these two models also show high similarity,with lower error rates than the other models.Notably,the Voting model demonstrated superior performance in accurately delineating mineral deposit locations and identifying realistic mineralization trends while minimizing false anomalies.展开更多
目的以中药药性作为特征变量,构建基于Voting集成算法的中药抗炎作用预测模型,并通过可视化技术分析不同药性特征对于中药抗炎作用的影响。方法以《中药学》与SymMap数据库中1247味中药为研究对象,经过初筛和复筛后建立包含性味归经等...目的以中药药性作为特征变量,构建基于Voting集成算法的中药抗炎作用预测模型,并通过可视化技术分析不同药性特征对于中药抗炎作用的影响。方法以《中药学》与SymMap数据库中1247味中药为研究对象,经过初筛和复筛后建立包含性味归经等特征的规范化数据库。基于决策树、支持向量机、轻量级梯度提升机等6种基础模型构建Voting集成模型,并以七折交叉验证和基于树结构的贝叶斯优化算法超参数优化提升模型性能。利用SHAP(SHapley Additive ex Planations)解释器可视化关键药性特征。结果经筛选后,共纳入522味抗炎中药构建数据库。Voting集成模型综合性能最优,F1分数为0.797,AUC值为0.77,较单一模型平均提升7.4%。SHAP分析表明使中药发挥抗炎作用的重要特征分别是“脾经”“甘味”“补益”等,使中药不具有抗炎作用的重要特征为“性温或平”和“毒性”。结论首次通过集成算法构建具有良好性能的中药抗炎作用预测模型,为中医药与机器学习结合的研究模式提供了新思路。展开更多
3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width ...3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width dimensions simultaneously,leading to limited feature representation capabilities when handling complex visual tasks.To address this challenge,we propose a novel 3D model classification network named ViT-GE(Vision Transformer with Global and Efficient Attention),which integrates Global Grouped Coordinate Attention(GGCA)and Efficient Channel Attention(ECA)mechanisms.Specifically,the Vision Transformer(ViT)is employed to extract comprehensive global features from multi-view inputs using its self-attention mechanism,effectively capturing 3D shape characteristics.To further enhance spatial feature modeling,the GGCA module introduces a grouping strategy and global context interactions.Concurrently,the ECA module strengthens inter-channel information flow,enabling the network to adaptively emphasize key features and improve feature fusion.Finally,a voting mechanism is adopted to enhance classification accuracy,robustness,and stability.Experimental results on the ModelNet10 dataset demonstrate that our method achieves a classification accuracy of 93.50%,validating its effectiveness and superior performance.展开更多
文摘In this investigation,the Gradient Boosting(GB),Linear Regression(LR),Decision Tree(DT),and Voting algo-rithms were applied to predict the distribution pattern of Au geochemical data.Trace and indicator elements,including Mo,Cu,Pb,Zn,Ag,Ni,Co,Mn,Fe,and As,were used with these machine learning algorithms(MLAs)to predict Au concentration values in the Doostbigloo porphyry Cu-Au-Mo mineralization area.The performance of the models was evaluated using the Mean Absolute Percentage Error(MAPE)and Root Mean Square Error(RMSE)metrics.The proposed ensemble Voting algorithm outperformed the other models,yielding more ac-curate predictions according to both metrics.The predicted data from the GB,LR,DT,and Voting MLAs were modeled using the Concentration-Area fractal method,and Au geochemical anomalies were mapped.To compare and validate the results,factors such as the location of the mineral deposits,their surface extent,and mineralization trend were considered.The results indicate that integrating hybrid MLAs with fractal modeling signifi-cantly improves geochemical prospectivity mapping.Among the four models,three(DT,GB,Voting)accurately identified both mineral deposits.The LR model,however,only identified Deposit I(central),and its mineralization trend diverged from the field data.The GB and Voting models produced similar results,with their final maps derived from fractal modeling showing the same anomalous areas.The anomaly boundaries identified by these two models are consistent with the two known reserves in the region.The results and plots related to prediction indicators and error rates for these two models also show high similarity,with lower error rates than the other models.Notably,the Voting model demonstrated superior performance in accurately delineating mineral deposit locations and identifying realistic mineralization trends while minimizing false anomalies.
文摘目的以中药药性作为特征变量,构建基于Voting集成算法的中药抗炎作用预测模型,并通过可视化技术分析不同药性特征对于中药抗炎作用的影响。方法以《中药学》与SymMap数据库中1247味中药为研究对象,经过初筛和复筛后建立包含性味归经等特征的规范化数据库。基于决策树、支持向量机、轻量级梯度提升机等6种基础模型构建Voting集成模型,并以七折交叉验证和基于树结构的贝叶斯优化算法超参数优化提升模型性能。利用SHAP(SHapley Additive ex Planations)解释器可视化关键药性特征。结果经筛选后,共纳入522味抗炎中药构建数据库。Voting集成模型综合性能最优,F1分数为0.797,AUC值为0.77,较单一模型平均提升7.4%。SHAP分析表明使中药发挥抗炎作用的重要特征分别是“脾经”“甘味”“补益”等,使中药不具有抗炎作用的重要特征为“性温或平”和“毒性”。结论首次通过集成算法构建具有良好性能的中药抗炎作用预测模型,为中医药与机器学习结合的研究模式提供了新思路。
基金funded by the project supported by the Heilongjiang Provincial Natural Science Foundation of China(Grant Number LH2022F030).
文摘3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width dimensions simultaneously,leading to limited feature representation capabilities when handling complex visual tasks.To address this challenge,we propose a novel 3D model classification network named ViT-GE(Vision Transformer with Global and Efficient Attention),which integrates Global Grouped Coordinate Attention(GGCA)and Efficient Channel Attention(ECA)mechanisms.Specifically,the Vision Transformer(ViT)is employed to extract comprehensive global features from multi-view inputs using its self-attention mechanism,effectively capturing 3D shape characteristics.To further enhance spatial feature modeling,the GGCA module introduces a grouping strategy and global context interactions.Concurrently,the ECA module strengthens inter-channel information flow,enabling the network to adaptively emphasize key features and improve feature fusion.Finally,a voting mechanism is adopted to enhance classification accuracy,robustness,and stability.Experimental results on the ModelNet10 dataset demonstrate that our method achieves a classification accuracy of 93.50%,validating its effectiveness and superior performance.
