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
Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a...Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a bottleneck to the widespread adoption of UCAN in 6G.In this paper,we propose Overlap Shard,a blockchain framework based on a novel reputation voting(RV)scheme,to dynamically manage the APs in UCAN.AP nodes in UCAN are distributed across multiple shards based on the RV scheme.That is,nodes with good reputation(virtuous behavior)are likely to be selected in the overlap shard.The RV mechanism ensures the security of UCAN because most APs adopt virtuous behaviors.Furthermore,to improve the efficiency of the Overlap Shard,we reduce cross-shard transactions by introducing core nodes.Specifically,a few nodes are overlapped in different shards,which can directly process the transactions in two shards instead of crossshard transactions.This greatly increases the speed of transactions between shards and thus the throughput of the overlap shard.The experiments show that the throughput of the overlap shard is about 2.5 times that of the non-sharded blockchain.展开更多
Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptibl...Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.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
Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the ident...Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.展开更多
Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine lea...Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.展开更多
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
In this study,we conducted an experiment to construct multi-model ensemble(MME)predictions for the El Niño-Southern Oscillation(ENSO)using a neural network,based on hindcast data released from five coupled oceana...In this study,we conducted an experiment to construct multi-model ensemble(MME)predictions for the El Niño-Southern Oscillation(ENSO)using a neural network,based on hindcast data released from five coupled oceanatmosphere models,which exhibit varying levels of complexity.This nonlinear approach demonstrated extraordinary superiority and effectiveness in constructing ENSO MME.Subsequently,we employed the leave-one-out crossvalidation and the moving base methods to further validate the robustness of the neural network model in the formulation of ENSO MME.In conclusion,the neural network algorithm outperforms the conventional approach of assigning a uniform weight to all models.This is evidenced by an enhancement in correlation coefficients and reduction in prediction errors,which have the potential to provide a more accurate ENSO forecast.展开更多
This paper presents 3RVAV(Three-Round Voting with Advanced Validation),a novel Byzantine Fault Tolerant consensus protocol combining Proof-of-Stake with a multi-phase voting mechanism.The protocol introduces three lay...This paper presents 3RVAV(Three-Round Voting with Advanced Validation),a novel Byzantine Fault Tolerant consensus protocol combining Proof-of-Stake with a multi-phase voting mechanism.The protocol introduces three layers of randomized committee voting with distinct participant roles(Validators,Delegators,and Users),achieving(4/5)-threshold approval per round through a verifiable random function(VRF)-based selection process.Our security analysis demonstrates 3RVAV provides 1−(1−s/n)^(3k) resistance to Sybil attacks with n participants and stake s,while maintaining O(kn log n)communication complexity.Experimental simulations show 3247 TPS throughput with 4-s finality,representing a 5.8×improvement over Algorand’s committee-based approach.The proposed protocol achieves approximately 4.2-s finality,demonstrating low latency while maintaining strong consistency and resilience.The protocol introduces a novel punishment matrix incorporating both stake slashing and probabilistic blacklisting,proving a Nash equilibrium for honest participation under rational actor assumptions.展开更多
According to the Charter of the United Nations,the United Nations Security Council adopts a“collective security system”authorized voting system,which has prominent drawbacks such as difficulty in fully reflecting th...According to the Charter of the United Nations,the United Nations Security Council adopts a“collective security system”authorized voting system,which has prominent drawbacks such as difficulty in fully reflecting the will of all Member States.Combining interdisciplinary,qualitative and quantitative research methods,in response to the dilemma of Security Council voting reform,this article suggests retaining the Security Council voting system and recommending a simplified model of“basic and weighted half”for voting allocation.This model not only inherits the authorized voting system of the collective security system,but also follows the allocation system of sovereignty equality in the Charter.It can also achieve the“draw on the advantages and avoid disadvantages”of Member States towards international development,promote the transformation of“absolute equality”of overall consistency into“real fairness”relative to individual contributions,and further promote the development of international law in the United Nations voting system.展开更多
In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatia...In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism(SRVM) is designed for standard Particle Swarm Optimization(PSO) to overcome the defects of local optimal and slow convergence.For each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival(ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.展开更多
To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to ...To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.展开更多
The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in ni...The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.展开更多
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supported by the National Natural Science Foundation of China(NSFC)under Grant 61931005.
文摘Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a bottleneck to the widespread adoption of UCAN in 6G.In this paper,we propose Overlap Shard,a blockchain framework based on a novel reputation voting(RV)scheme,to dynamically manage the APs in UCAN.AP nodes in UCAN are distributed across multiple shards based on the RV scheme.That is,nodes with good reputation(virtuous behavior)are likely to be selected in the overlap shard.The RV mechanism ensures the security of UCAN because most APs adopt virtuous behaviors.Furthermore,to improve the efficiency of the Overlap Shard,we reduce cross-shard transactions by introducing core nodes.Specifically,a few nodes are overlapped in different shards,which can directly process the transactions in two shards instead of crossshard transactions.This greatly increases the speed of transactions between shards and thus the throughput of the overlap shard.The experiments show that the throughput of the overlap shard is about 2.5 times that of the non-sharded blockchain.
文摘Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
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.
文摘Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.
文摘Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.
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.
基金The fund from Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.SML2021SP310the National Natural Science Foundation of China under contract Nos 42227901 and 42475061the Key R&D Program of Zhejiang Province under contract No.2024C03257.
文摘In this study,we conducted an experiment to construct multi-model ensemble(MME)predictions for the El Niño-Southern Oscillation(ENSO)using a neural network,based on hindcast data released from five coupled oceanatmosphere models,which exhibit varying levels of complexity.This nonlinear approach demonstrated extraordinary superiority and effectiveness in constructing ENSO MME.Subsequently,we employed the leave-one-out crossvalidation and the moving base methods to further validate the robustness of the neural network model in the formulation of ENSO MME.In conclusion,the neural network algorithm outperforms the conventional approach of assigning a uniform weight to all models.This is evidenced by an enhancement in correlation coefficients and reduction in prediction errors,which have the potential to provide a more accurate ENSO forecast.
文摘This paper presents 3RVAV(Three-Round Voting with Advanced Validation),a novel Byzantine Fault Tolerant consensus protocol combining Proof-of-Stake with a multi-phase voting mechanism.The protocol introduces three layers of randomized committee voting with distinct participant roles(Validators,Delegators,and Users),achieving(4/5)-threshold approval per round through a verifiable random function(VRF)-based selection process.Our security analysis demonstrates 3RVAV provides 1−(1−s/n)^(3k) resistance to Sybil attacks with n participants and stake s,while maintaining O(kn log n)communication complexity.Experimental simulations show 3247 TPS throughput with 4-s finality,representing a 5.8×improvement over Algorand’s committee-based approach.The proposed protocol achieves approximately 4.2-s finality,demonstrating low latency while maintaining strong consistency and resilience.The protocol introduces a novel punishment matrix incorporating both stake slashing and probabilistic blacklisting,proving a Nash equilibrium for honest participation under rational actor assumptions.
文摘According to the Charter of the United Nations,the United Nations Security Council adopts a“collective security system”authorized voting system,which has prominent drawbacks such as difficulty in fully reflecting the will of all Member States.Combining interdisciplinary,qualitative and quantitative research methods,in response to the dilemma of Security Council voting reform,this article suggests retaining the Security Council voting system and recommending a simplified model of“basic and weighted half”for voting allocation.This model not only inherits the authorized voting system of the collective security system,but also follows the allocation system of sovereignty equality in the Charter.It can also achieve the“draw on the advantages and avoid disadvantages”of Member States towards international development,promote the transformation of“absolute equality”of overall consistency into“real fairness”relative to individual contributions,and further promote the development of international law in the United Nations voting system.
基金co-supported by China Scholarship Council (No. 201604000003)the National Natural Science Foundation of China (Nos. U1433203, U1533119 and L142200032)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 61221061)
文摘In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism(SRVM) is designed for standard Particle Swarm Optimization(PSO) to overcome the defects of local optimal and slow convergence.For each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival(ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.
基金This project was supported by the National Basic Research Programof China (2001CB309403)
文摘To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.
基金The National Nat-ural Science Foundation of China (NSFC), Grant Nos.90711003, 40375014the program of GYHY200706005, and the APCC Visiting Scientist Program jointly supportedthis work.
文摘The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.
