The application of transformer networks and feature fusion models in medical image segmentation has aroused considerable attention within the academic circle.Nevertheless,two main obstacles persist:(1)the restrictions...The application of transformer networks and feature fusion models in medical image segmentation has aroused considerable attention within the academic circle.Nevertheless,two main obstacles persist:(1)the restrictions of the Transformer network in dealing with locally detailed features,and(2)the considerable loss of feature information in current feature fusion modules.To solve these issues,this study initially presents a refined feature extraction approach,employing a double-branch feature extraction network to capture complex multi-scale local and global information from images.Subsequently,we proposed a low-loss feature fusion method-Multi-branch Feature Fusion Enhancement Module(MFFEM),which realizes effective feature fusion with minimal loss.Simultaneously,the cross-layer cross-attention fusion module(CLCA)is adopted to further achieve adequate feature fusion by enhancing the interaction between encoders and decoders of various scales.Finally,the feasibility of our method was verified using the Synapse and ACDC datasets,demonstrating its competitiveness.The average DSC(%)was 83.62 and 91.99 respectively,and the average HD95(mm)was reduced to 19.55 and 1.15 respectively.展开更多
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio...Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.展开更多
The effectiveness of recommendation systems heavily relies on accurately predicting user ratings for items based on user preferences and item attributes derived from ratings and reviews.However,the increasing presence...The effectiveness of recommendation systems heavily relies on accurately predicting user ratings for items based on user preferences and item attributes derived from ratings and reviews.However,the increasing presence of fake user data in these ratings and reviews poses significant challenges,hindering feature extraction,diminishing rating prediction accuracy,and eroding user trust in the system.To tackle this issue,we propose a robust rating prediction model for recommendation systems that integrates fake user detection and multi-layer feature fusion.Our model utilizes a GraphSAGE-based submodel to filter out fake user data from rating data and review texts.To strengthen fake user detection,we enhance GraphSAGE by selecting aggregation neighbors based on the collusion fraud degree among users,and employ an attention mechanism to weigh the contribution of each neighbor during representation aggregation.Furthermore,we introduce a multi-layer feature fusion submodel to integrate diverse features extracted from the filtered ratings and reviews.For deep feature extraction from review texts,we implement a temporal attention mechanism to analyze the relevance of reviews over time.For shallow feature extraction from rating data,we incorporate trust evaluation mechanism and cloud model to assess the influence of trusted neighbors’ratings.In our evaluation,we compare our model against six baseline models for fake user detection and four rating prediction models across five datasets.The results demonstrate that our model exhibits significant performance advantages in both fake user detection and rating prediction.展开更多
In this paper,a feature interactive bi-temporal change detection network(FIBTNet)is designed to solve the problem of pseudo change in remote sensing image building change detection.The network improves the accuracy of...In this paper,a feature interactive bi-temporal change detection network(FIBTNet)is designed to solve the problem of pseudo change in remote sensing image building change detection.The network improves the accuracy of change detection through bi-temporal feature interaction.FIBTNet designs a bi-temporal feature exchange architecture(EXA)and a bi-temporal difference extraction architecture(DFA).EXA improves the feature exchange ability of the model encoding process through multiple space,channel or hybrid feature exchange methods,while DFA uses the change residual(CR)module to improve the ability of the model decoding process to extract different features at multiple scales.Additionally,at the junction of encoder and decoder,channel exchange is combined with the CR module to achieve an adaptive channel exchange,which further improves the decision-making performance of model feature fusion.Experimental results on the LEVIR-CD and S2Looking datasets demonstrate that iCDNet achieves superior F1 scores,Intersection over Union(IoU),and Recall compared to mainstream building change detectionmodels,confirming its effectiveness and superiority in the field of remote sensing image change detection.展开更多
基金funded by the Henan Science and Technology research project(222103810042)Support by the open project of scientific research platform of grain information processing center of Henan University of Technology(KFJJ-2021-108)+1 种基金Support by the innovative funds plan of Henan University of Technology(2021ZKCJ14)Henan University of Technology Youth Backbone Teacher Program.
文摘The application of transformer networks and feature fusion models in medical image segmentation has aroused considerable attention within the academic circle.Nevertheless,two main obstacles persist:(1)the restrictions of the Transformer network in dealing with locally detailed features,and(2)the considerable loss of feature information in current feature fusion modules.To solve these issues,this study initially presents a refined feature extraction approach,employing a double-branch feature extraction network to capture complex multi-scale local and global information from images.Subsequently,we proposed a low-loss feature fusion method-Multi-branch Feature Fusion Enhancement Module(MFFEM),which realizes effective feature fusion with minimal loss.Simultaneously,the cross-layer cross-attention fusion module(CLCA)is adopted to further achieve adequate feature fusion by enhancing the interaction between encoders and decoders of various scales.Finally,the feasibility of our method was verified using the Synapse and ACDC datasets,demonstrating its competitiveness.The average DSC(%)was 83.62 and 91.99 respectively,and the average HD95(mm)was reduced to 19.55 and 1.15 respectively.
文摘Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
基金funded by the National Natural Science Foundation of China(No.72072091)the Natural Science Foundation of Colleges and Universities of Jiangsu Province(Nos.21KJA520002 and 22KJA520005)+1 种基金the Ministry of Education Supply and Demand Matching Employment and Education Project(No.2023121801795)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Nos.KYCX23_2345 and SJCX24_1164).
文摘The effectiveness of recommendation systems heavily relies on accurately predicting user ratings for items based on user preferences and item attributes derived from ratings and reviews.However,the increasing presence of fake user data in these ratings and reviews poses significant challenges,hindering feature extraction,diminishing rating prediction accuracy,and eroding user trust in the system.To tackle this issue,we propose a robust rating prediction model for recommendation systems that integrates fake user detection and multi-layer feature fusion.Our model utilizes a GraphSAGE-based submodel to filter out fake user data from rating data and review texts.To strengthen fake user detection,we enhance GraphSAGE by selecting aggregation neighbors based on the collusion fraud degree among users,and employ an attention mechanism to weigh the contribution of each neighbor during representation aggregation.Furthermore,we introduce a multi-layer feature fusion submodel to integrate diverse features extracted from the filtered ratings and reviews.For deep feature extraction from review texts,we implement a temporal attention mechanism to analyze the relevance of reviews over time.For shallow feature extraction from rating data,we incorporate trust evaluation mechanism and cloud model to assess the influence of trusted neighbors’ratings.In our evaluation,we compare our model against six baseline models for fake user detection and four rating prediction models across five datasets.The results demonstrate that our model exhibits significant performance advantages in both fake user detection and rating prediction.
基金supported in part by the Fund of National Sensor Network Engineering Technology Research Center(No.NSNC202103)the Natural Science Research Project in Colleges and Universities of Anhui Province(No.2022AH040155)the Undergraduate Teaching Quality and Teaching Reform Engineering Project of Chuzhou University(No.2022ldtd03).
文摘In this paper,a feature interactive bi-temporal change detection network(FIBTNet)is designed to solve the problem of pseudo change in remote sensing image building change detection.The network improves the accuracy of change detection through bi-temporal feature interaction.FIBTNet designs a bi-temporal feature exchange architecture(EXA)and a bi-temporal difference extraction architecture(DFA).EXA improves the feature exchange ability of the model encoding process through multiple space,channel or hybrid feature exchange methods,while DFA uses the change residual(CR)module to improve the ability of the model decoding process to extract different features at multiple scales.Additionally,at the junction of encoder and decoder,channel exchange is combined with the CR module to achieve an adaptive channel exchange,which further improves the decision-making performance of model feature fusion.Experimental results on the LEVIR-CD and S2Looking datasets demonstrate that iCDNet achieves superior F1 scores,Intersection over Union(IoU),and Recall compared to mainstream building change detectionmodels,confirming its effectiveness and superiority in the field of remote sensing image change detection.
