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Explainable Context-Aware Fusion Network for Non-Small Cell Lung Cancer Analysis with Application to Smart Healthcare Systems 认领 引用
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作者 Muhammad Waqar Zeshan Aslam Khan +3 位作者 Arthur Chang Zhishan Guo Chun-Liang Lai Chuan-Yu Chang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1208-1235,共28页
Lung cancer(LC)is among the dangerous cancers spreading progressively,and a timely LC diagnosis becomes a dire need of the time.Various imaging-based studies have been conducted for accurate LC examination through com... Lung cancer(LC)is among the dangerous cancers spreading progressively,and a timely LC diagnosis becomes a dire need of the time.Various imaging-based studies have been conducted for accurate LC examination through computed tomography(CT),X-ray,and histopathology.Worldwide,the proportion of LC-affected patients in hospitals is growing,thereby increasing imaging data for fast processing and early examination.To facilitate histopathological imaging-based automated and timely decision making for accurate LC prediction,a Context Aware Fusion Network(CAFNet)for holistic feature learning and spatially localized feature learning is proposed in this study for the efficient extraction and processing of global as well as local features,respectively.CAFNet exploits histopathological tissues to ensure local and global attributes uniformity for extracting contextual information.The conducted research achieves histopathological image enhancement using median filtering(MF)and contrast-limited-adaptive-HistogramEqualization(CLAHE).Moreover,the classifying power of the proposed CAFNet is enhanced through superior attributes extraction strategies,such as Mobile Inverted Bottleneck Convolution(MIBConv)employed with Spatial Attention with Residual Learning(SARL)and Channel Attention with Residual Learning(CARL).An innovative,partially adaptive optimization approach is utilized to fine-tune the degree of adaptivity in the learning process of the network.The descriptive behavior of CAFNet is explored through explainable artificial intelligence(XAI)strategies like Gradient-Weighted Class Activation Mapping(GradCAM)and Local Interpretable Model-Agnostic Explanation(LIME).The proposed network achieved an improved average classification accuracy of 7.36%while reducing models’complexity by 85%to 99%as compared to the existing benchmark models.The study also addresses users’accessibility challenges by providing a web-based interface using Gradio for users’real-time interaction. 展开更多
关键词 Lung cancer histopathology context-aware network explainable AI web-based Interface
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Attention Mechanisms and FFM Feature Fusion Module-Based Modification of the Deep Neural Network for Detection of Structural Cracks 认领 引用 被引量：1
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作者 Tao Jin Zhekun Shou +1 位作者 Hongchao Liu Yuchun Shao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期345-366,共22页
This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge compon... This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management. 展开更多
关键词 Bridge crack diseases structural health monitoring convolutional neural network feature fusion
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Real-time degradation modeling for automotive PEMFC stacks:a multi-scale fusion network validated on an industrial 215-channel system 认领 引用
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作者 Zifei WANG Xiangxian ZHU +5 位作者 Congxin LI Daidai CHEN Zhitao LIU Longhua MA Jili TAO Hongye SU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第5期506-517,I0026-I0040,共12页
Accurately predicting long-term degradation patterns in proton exchange membrane fuel cell(PEMFC)stacks under automotive operating conditions remains challenging.Prediction methods are largely constrained by laborator... Accurately predicting long-term degradation patterns in proton exchange membrane fuel cell(PEMFC)stacks under automotive operating conditions remains challenging.Prediction methods are largely constrained by laboratory-scale experiments and limited stack sizes,resulting in insufficient accuracy and generalization capability.To address these limitations,in this paper we propose a multi-scale bidirectional fusion network(MBFNet)tailored for an industrial 215-channel PEMFC stack,enabling accurate degradation prediction under accelerated real-world dynamic conditions using gas-heat-electricity(GHE)co-simulation data.A channel-joint adaptive noise correlation threshold(NCT)algorithm is introduced to account for variable correlations across sensors and operating conditions without relying on prior physical modeling.A multi-scale decomposition module captures degradation dynamics at different temporal scales,while a bidirectional fusion module integrates global trends and local details into the final prediction.Experimental results show that MBFNet achieves 18.6%lower prediction error and 36.8%fewer parameters than the long short-term memory(LSTM)-attention benchmark under real operating scenarios.In multi-step prediction tasks,MBFNet reduces root mean square error by an average of 24.5%relative to LSTM-attention and 55.2%relative to a one-dimensional convolutional neural network(1D-CNN)across four prediction horizons,better satisfying automotive application requirements.Moreover,MBFNet exhibits strong physical interpretability,making it efficient to implement and promising for practical deployment. 展开更多
关键词 Large-scale proton exchange membrane fuel cell(PEMFC)stacks Multi-scale bidirectional fusion network(MBFNet) Degradation prediction Automotive scenarios
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A multiscale residual dense fusion network for nuclear medical image fusion 认领 引用
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作者 Jun Fu Gengyu Ge +5 位作者 Yihui Tan Miaoqiang Yang Qian Wang Jie Yang Ya Wang Aijia Ouyang 《Intelligent Oncology》 CAS 2026年第2期53-65,共13页
Introduction:Multimodal medical image fusion technology generates new images containing more accurate disease information by fusing different modal images.It not only improves the accuracy and efficiency of diagnosis ... Introduction:Multimodal medical image fusion technology generates new images containing more accurate disease information by fusing different modal images.It not only improves the accuracy and efficiency of diagnosis but also provides strong support for the formulation of treatment plans.Meanwhile,it also shows great potential value in disease monitoring,personalized medicine,and clinical research.Although different multimodal medical image fusion methods have been presented,most of them are hindered by information loss,blurred edges,and low fusion efficiency.Methods:To solve these problems,this paper proposes a multiscale residual dense fusion network(MRDFN)for multimodal medical image fusion.MRDFN integrates the strengths of both the multiscale residual network and dense network to achieve feature extraction and fusion.Results:Experiments show that the fusion images of the proposed method are superior to the reference methods in terms of edge intensity,detail definition,and objective metrics.The comparative analysis of these fusion metrics proves that the fusion image quality of MRDFN is better than that of the reference methods.The suggested method achieves higher values in average gradient,standard deviation,spatial frequency,and visual information fidelity for fusion,with average gradient reaching 2.0 times the average of the comparison algorithms,standard deviation 1.2 times,spatial frequency 2.3 times,and visual information fidelity for fusion 1.3 times.Conclusions:The findings in this study demonstrate that MRDFN outperforms other approaches discussed in the analysis,particularly in objective metrics and detailed information,and the average fusion time of MRDFN is lower than that of most reference methods,demonstrating effective multimodal medical image fusion. 展开更多
关键词 Multimodal medical image Multiscale residual dense Fusion network
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Retraction: A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique 认领 引用
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作者 无 《Computers, Materials & Continua》 SCIE EI 2026年第7期1981-1981,共1页
The published article titled“A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique”has been retracted from Computers,Materials&C... The published article titled“A Lightweight Multimodal Deep Fusion Network for Face Antis Poofing with Cross-Axial Attention and Deep Reinforcement Learning Technique”has been retracted from Computers,Materials&Continua,Vol.85,No.3,2025,pp.5671-5702. 展开更多
关键词 face antis poofing deep reinforcement learning technique deep reinforcement learning face antispoofing lightweight multimodal deep fusion network cross axial attention
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Dynamic Memristor-Inspired Zeroing Neural Network with Hybrid Activation for Finite-Time Convergence and Remote Sensing Image Fusion 认领 引用
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作者 Jiaqi He Mu Li 《Journal of Electronic Research and Application》 2026年第1期139-146,共8页
Solving time-varying nonlinear equations in real time is a significant challenge in modern computing.Dynamic Memristor-Inspired Zeroing Neural Networks(DMZNN)have shown strong performance in this field,but their conve... Solving time-varying nonlinear equations in real time is a significant challenge in modern computing.Dynamic Memristor-Inspired Zeroing Neural Networks(DMZNN)have shown strong performance in this field,but their convergence speed and robustness heavily rely on the design of the activation function.This paper proposes a novel hybrid activation function inspired by the nonlinear characteristics of memristors.By integrating cubic and sublinear terms,the proposed function facilitates multi-stage error decay,effectively addressing the slow convergence and poor noise resistance of traditional activation functions.Theoretical analysis shows that the DMZNN model,built upon this activation function,can converge in finite time.Robustness under parameter perturbations and additive noise is rigorously proven using Lyapunov theory.Simulation results demonstrate that the convergence speed of the DMZNN model is obviously faster than that of traditional ZNN models when solving second-order,third-order,and fourth-order time-varying nonlinear equations.Additionally,in the application of remote sensing image fusion,DMZNN outperforms traditional gradient-based methods in both fusion quality and processing speed,demonstrating its practical effectiveness and superiority in real-world applications. 展开更多
关键词 Zeroing neural network Finite-time convergence Memristor Time-varying nonlinear equations Remote sensing image fusion
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Self-FAGCFN:Graph-Convolution Fusion Network Based on Feature Fusion and Self-Supervised Feature Alignment for Pneumonia and Tuberculosis Diagnosis 认领 引用 被引量：1
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作者 Junding Sun Wenhao Tang +5 位作者 Lei Zhao Chaosheng Tang Xiaosheng Wu Zhaozhao Xu Bin Pu Yudong Zhang 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期2012-2029,共18页
Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely us... Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely used in pulmonary disease diagnosis,such as pneumonia and tuberculosis.However,traditional feature fusion methods often suffer from feature disparity,information loss,redundancy,and increased complexity,hindering the further extension of DL algorithms.To solve this problem,we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment(Self-FAGCFN)to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis.The network integrates Convolutional Neural Networks(CNNs)for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks(GCNs)within a Graph Neural Network branch to capture features based on graph structure,focusing on significant node representations.Additionally,an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs.To ensure effective feature alignment between pre-and post-fusion stages,we introduce a feature alignment loss that minimizes disparities.Moreover,to address the limitations of proposed methods,such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset,we develop a Feature-Centroid Fusion(FCF)strategy and a Multi-Level Feature-Centroid Update(MLFCU)algorithm,respectively.Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis,highlighting its potential for practical medical applications. 展开更多
关键词 Feature fusion Self-supervised feature alignment Convolutional neural networks Graph convolutional networks Class imbalance Feature-centroid fusion
Adaptive Fusion Neural Networks for Sparse-Angle X-Ray 3D Reconstruction 认领 引用 被引量：1
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作者 Shaoyong Hong Bo Yang +4 位作者 Yan Chen Hao Quan Shan Liu Minyi Tang Jiawei Tian 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第7期1091-1112,共22页
3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safe... 3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safer and faster,poses challenges for accurate volumetric reconstruction due to limited spatial information.This study proposes a 3D reconstruction neural network based on adaptive weight fusion(AdapFusionNet)to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images.To address the issue of spatial inconsistency in multi-angle image reconstruction,an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion,thereby improving the final reconstruction quality.The reconstruction network is built on an autoencoder(AE)framework and uses orthogonal-angle X-ray images(frontal and lateral projections)as inputs.The encoder extracts 2D features,which the decoder maps into 3D space.This study utilizes a lung CT dataset to obtain complete three-dimensional volumetric data,from which digitally reconstructed radiographs(DRR)are generated at various angles to simulate X-ray images.Since real-world clinical X-ray images rarely come with perfectly corresponding 3D“ground truth,”using CT scans as the three-dimensional reference effectively supports the training and evaluation of deep networks for sparse-angle X-ray 3D reconstruction.Experiments conducted on the LIDC-IDRI dataset with simulated X-ray images(DRR images)as training data demonstrate the superior performance of AdapFusionNet compared to other fusion methods.Quantitative results show that AdapFusionNet achieves SSIM,PSNR,and MAE values of 0.332,13.404,and 0.163,respectively,outperforming other methods(SingleViewNet:0.289,12.363,0.182;AvgFusionNet:0.306,13.384,0.159).Qualitative analysis further confirms that AdapFusionNet significantly enhances the reconstruction of lung and chest contours while effectively reducing noise during the reconstruction process.The findings demonstrate that AdapFusionNet offers significant advantages in 3D reconstruction of sparse-angle X-ray images. 展开更多
关键词 3D reconstruction adaptive fusion X-ray imaging medical imaging deep learning neural networks sparse angles autoencoder
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Cross-feature fusion speech emotion recognition based on attention mask residual network and Wav2vec 2.0 认领 引用 被引量：1
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作者 Xiaoke Li Zufan Zhang 《Digital Communications and Networks》 SCIE EI CSCD 2025年第5期1567-1577,共11页
Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the dev... Speech Emotion Recognition(SER)has received widespread attention as a crucial way for understanding human emotional states.However,the impact of irrelevant information on speech signals and data sparsity limit the development of SER system.To address these issues,this paper proposes a framework that incorporates the Attentive Mask Residual Network(AM-ResNet)and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively,together with a cross-attention module to interact and fuse these two features.The AM-ResNet branch mainly consists of maximum amplitude difference detection,mask residual block,and an attention mechanism.Among them,the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network,respectively,to reduce the impact of silent frames,and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech.In the Wav2vec 2.0 branch,this model is introduced as a feature extractor to obtain general speech features(Wav2vec 2.0 features)through pre-training with a large amount of unlabeled speech data,which can assist the SER task and cope with data sparsity problems.In the cross-attention module,AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features,which are used to predict the final emotion.Furthermore,multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations.Finally,experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches. 展开更多
关键词 Speech emotion recognition Residual network Mask Attention Wav2vec 2.0 Cross-feature fusion
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Multimodal Trajectory Generation for Robotic Motion Planning Using Transformer-Based Fusion and Adversarial Learning 认领 引用 被引量：1
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作者 Shtwai Alsubai Ahmad Almadhor +3 位作者 Abdullah Al Hejaili Najib Ben Aoun Tahani Alsubait Vincent Karovic 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期848-869,共22页
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel... In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics. 展开更多
关键词 Multimodal trajectory generation robotic motion planning transformer networks sensor fusion reinforcement learning generative adversarial networks
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Adaptive weight strategy for frequency-decomposed seismic attribute fusion in predicting of complex sand body distributions 认领 引用 被引量：1
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作者 Hong-Li Wu Sheng-He Wu +7 位作者 Zhen-Hua Xu Ming-Cheng Liu Bo Yang De-GangWu Zi-Shi Xie Yu Tang Xiao-Long Wan Xin-Ping Zhou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2367-2389,共23页
High-precision sand prediction is fundamental to improving the efficiency of oil and gas exploration and development.To address the limitations of traditional fixed-weight fusion strategies,particularly under conditio... High-precision sand prediction is fundamental to improving the efficiency of oil and gas exploration and development.To address the limitations of traditional fixed-weight fusion strategies,particularly under conditions of significant lateral variation in sand body distribution,this study proposesa dynamic weightingdeep neural network(DW-DNN)for adaptive frequency-decomposed attribute fusion.The approach integrates physical constraints with deep learning and introduces two innovations:(i)a priori weight matrices derived from the amplitudefrequency and tuning thickness relationship(amplitude variation with frequency,AvF)are embeddedintothe attention mechanism to adaptivelyallocate multiband seismic attributes,emphasizing high-frequency features for thin sands and low-frequency features for thick sands;and(ii)a deep neural network with a composite loss function combining mean squared error(MSE)and AVF-based constraints is designed to jointly optimize weight allocation and prediction accuracy.The method was applied to the Xi 233 area of theQingcheng Oilfield in the Ordos Basin and compared with conventional approaches.DW-DNN achieved high accuracy and generalizability,with an R2 of 0.92 in the 30%blind-well test,24.3%higher than conventional methods.In addition,91%of well-point errors were within 03 m,while prediction accuracies for thin(≤3 m)and thick(>3 m)sands reached 88%and 91%,respectively.The model also maintained stable performance under low well-control conditions(training-test ratio 5:5).Predicted sand distributions exhibited improved continuity and geologically plausible geometries,clearly delineating channels,lobes,and estuary bars.The results demonstrate that DW-DNN enhances frequency-decomposed attribute fusion through adaptive weight allocation,providing a robust tool for predicting sand body distributions in complex reservoirs. 展开更多
关键词 Reservoir heterogeneity Sand thickness prediction Frequency-decomposed attribute intelligent fusion Adaptive weighting Deep neural network
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DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model 认领 引用
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作者 Xiangbin Wang Qingjun Yuan +3 位作者 Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu 《Computers, Materials & Continua》 SCIE EI 2025年第5期2267-2285,共19页
With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based approaches.Among these,multimodal learning-based classification methods... With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based approaches.Among these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification accuracy.However,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the data.To address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature extraction.Specifically,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective learning.This continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network conditions.Experimental results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods. 展开更多
关键词 Deep fusion intrusion detection multimodal learning network traffic classification
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Predictions of complete fusion cross‑sections of 6,7Li,9Be,and 10B using a Bayesian neural network method 认领 引用
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作者 Kai‑Xuan Cheng Rong‑Xing He +1 位作者 Chun‑Yuan Qiao Chun‑Wang Ma 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第10期169-175,共7页
A machine learning approach based on Bayesian neural networks was developed to predict the complete fusion cross-sections of weakly bound nuclei.This method was trained and validated using 475 experimental data points... A machine learning approach based on Bayesian neural networks was developed to predict the complete fusion cross-sections of weakly bound nuclei.This method was trained and validated using 475 experimental data points from 39 reaction systems induced by 6,7Li,9Be,and 10B.The constructed Bayesian neural network demonstrated a high degree of accuracy in evaluating complete fusion cross-sections.By comparing the predicted cross-sections with those obtained from a single-barrier penetration model,the suppression effect of 6,7Li and 9Be with a stable nucleus was systematically analyzed.In the cases of 6Li and 7Li,less suppression was predicted for relatively light-mass targets than for heavy-mass targets,and a notably distinct dependence relationship was identified,suggesting that the predominant breakup mechanisms might change in different mass target regions.In addition,minimum suppression factors were predicted to occur near target nuclei with neutron-closed shell. 展开更多
关键词 Fusion reaction Weakly bound nuclei Machine learning Bayesian neural network
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Stochastic state of health estimation for lithium-ion batteries with automated feature fusion using quantum convolutional neural network 认领 引用
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作者 Chen Liang Shengyu Tao +3 位作者 Xinghao Huang Yezhen Wang Bizhong Xia Xuan Zhang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第7期205-219,共15页
The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data... The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data segments while designing SOH estimation algorithms that efficiently handle the large-scale computational demands of cloud-based battery management systems presents a substantial challenge.In this work,we propose a quantum convolutional neural network(QCNN)model designed for accurate,robust,and generalizable SOH estimation with minimal data and parameter requirements and is compatible with quantum computing cloud platforms in the Noisy Intermediate-Scale Quantum.First,we utilize data from 4 datasets comprising 272 cells,covering 5 chemical compositions,4 rated parameters,and 73operating conditions.We design 5 voltage windows as small as 0.3 V for each cell from incremental capacity peaks for stochastic SOH estimation scenarios generation.We extract 3 effective health indicators(HIs)sequences and develop an automated feature fusion method using quantum rotation gate encoding,achieving an R2of 96%.Subsequently,we design a QCNN whose convolutional layer,constructed with variational quantum circuits,comprises merely 39 parameters.Additionally,we explore the impact of training set size,using strategies,and battery materials on the model’s accuracy.Finally,the QCNN with quantum convolutional layers reduces root mean squared error by 28% and achieves an R2exceeding 96% compared to other three commonly used algorithms.This work demonstrates the effectiveness of quantum encoding for automated feature fusion of HIs extracted from limited discharge data.It highlights the potential of QCNN in improving the accuracy,robustness,and generalization of SOH estimation while dealing with stochastic and noisy data with few parameters and simple structure.It also suggests a new paradigm for leveraging quantum computational power in SOH estimation. 展开更多
关键词 Lithium-ion battery State of health Feature fusion Quantum convolutional neural network Quantum machine learning
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A Prediction Method for Concrete Mixing Temperature Based on the Fusion of Physical Models and Neural Networks 认领 引用
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作者 Lei Zheng Hong Pan +6 位作者 Yuelei Ruan Guoxin Zhang Lei Zhang Jianda Xin Zhenyang Zhu Jianyao Zhang Wei Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期3217-3241,共25页
As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid pr... As a critical material in construction engineering,concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency.This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron(MLP)neural network,dynamically fused via a weighted strategy to achieve high-precision temperature estimation.Experimental results on an independent test set demonstrated the superior performance of the fused model,with a root mean square error(RMSE)of 1.59℃ and a mean absolute error(MAE)of 1.23℃,representing a 25.3%RMSE reduction compared to conventional physical models.Ambient temperature and coarse aggregate temperature were identified as the most influential variables.Furthermore,the model-based temperature control strategy reduced costs by 0.81 CNY/m3,showing significant potential for improving resource efficiency and supporting sustainable construction practices. 展开更多
关键词 Concrete outlet temperature prediction physical model neural network dynamic weight fusion temperature control
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Trust-Based Context-Aware Mobile Social Network Service Recommendation 认领 引用 被引量：4
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作者 XU Jun ZHONG Yuansheng +1 位作者 ZHU Wenqiang SUN Feifei 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2017年第2期149-156,共8页
The service recommendation mechanism as a key enabling technology that provides users with more proactive and personalized service is one of the important research topics in mobile social network （MSN）. Meanwhile, M... The service recommendation mechanism as a key enabling technology that provides users with more proactive and personalized service is one of the important research topics in mobile social network （MSN）. Meanwhile, MSN is susceptible to various types of anonymous information or hacker actions. Trust can reduce the risk of interaction with unknown entities and prevent malicious attacks. In our paper, we present a trust-based service recommendation algorithm in MSN that considers users＇ similarity and friends＇ familiarity when computing trustworthy neighbors of target users. Firstly, we use the context information and the number of co-rated items to define users＇ similarity. Then, motivated by the theory of six degrees of space, the friend familiarity is derived by graph-based method. Thus the proposed methods are further enhanced by considering users＇ context in the recommendation phase. Finally, a set of simulations are conducted to evaluate the accuracy of the algorithm. The results show that the friend familiarity and user similarity can effectively improve the recommendation performance, and the friend familiarity contributes more than the user similarity. 展开更多
关键词 trust context-aware mobile social network recommendation
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Rolling Bearing Fault Detection Based on Self-Adaptive Wasserstein Dual Generative Adversarial Networks and Feature Fusion under Small Sample Conditions 认领 引用
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作者 Qiang Ma Zhuopei Wei +2 位作者 Kai Yang Long Tian Zepeng Li 《Structural Durability & Health Monitoring》 EI 2025年第4期1011-1035,共25页
An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extra... An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance. 展开更多
关键词 Deep learning Wasserstein deep convolutional generative adversarial network small sample learning feature fusion multidimensional data enhancement small sample fault diagnosis
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SYMPHONIA-Enhanced Multimodal Emotion Recognition with Dual-Branch Dynamic Attention and Hierarchical Adaptive Fusion 认领 引用
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作者 Akmalbek Abdusalomov Mukhriddin Mukhiddinov +4 位作者 Kamola Abdurashidova Alpamis Kutlimuratov Avazjon Marakhimov Kuanishbay Seytnazarov Young-Im Cho 《Computers, Materials & Continua》 SCIE EI 2026年第7期1786-1807,共22页
Human emotions are intricate and difficult to decipher through various modalities.Current methodologies frequently employ inflexible fusion strategies that do not consider the dynamic and context-sensitive characteris... Human emotions are intricate and difficult to decipher through various modalities.Current methodologies frequently employ inflexible fusion strategies that do not consider the dynamic and context-sensitive characteristics of emotional expressions in both visual and textual mediums.This paper presents SYMPHONIA(Synchronizing Facial and Textual Modalities for Emotion Understanding),an innovative architecture engineered to capture and amalgamate emotional signals from facial expressions and language,attuned to contextual and modality interactions.There are two parts to SYMPHONIA:a Facial Emotion Branch that uses Vision Transformers and facial landmarks,and a Textual Emotion Branch that uses RoBERTa embeddings and graph-based reasoning.A Dual-Branch Dynamic Attention Mechanism and a Hierarchical Adaptive Fusion Module are used to connect these branches.SYMPHONIA beat the best models on four datasets:IEMOCAP,MELD,CMU-MOSI,and CMU-MOSEI.It got 80.9%accuracy and 80.1%F1-score on IEMOCAP,which was better than Dualgats(74.8%)and EmoCLIP(75.3%).SYMPHONIA got 74.2%accuracy and 73.5%F1-score for MELD.It beat its competitors by getting a 0.86 Pearson correlation on MOSI and a 0.83 on MOSEI for predicting sentiment.Cross-dataset tests showed that SYMPHONIA could generalize,with 66.9%accuracy when trained on IEMOCAP and tested on MELD.This was better than all the baselines.These results show that SYMPHONIA is good at recognizing emotions and analyzing sentiment in different situations,which shows that it can adapt and do well in different settings. 展开更多
关键词 Multimodal emotion recognition RoBERTa cross-modal attention graph neural networks contrastive learning adaptive fusion temporal modeling affective computing context-aware representation
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Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment 认领 引用 被引量：3
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作者 Shumin Li Qifang Luo Yongquan Zhou 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期1955-1994,共40页
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ... Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained. 展开更多
关键词 Stochastic data fusion wireless sensor networks network deployment spatiotemporal coverage dwarf mongoose optimization algorithm multi-objective optimization
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xCViT:Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI 认领 引用 被引量：3
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作者 Armughan Ali Hooria Shahbaz Robertas Damaševicius 《Computers, Materials & Continua》 SCIE EI 2025年第4期1367-1398,共32页
Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead t... Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model. 展开更多
关键词 Skin lesions vision transformer CNN Xception deep learning network fusion explainable AI Grad-CAM skin cancer detection
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