Neurocomputing最新文献_第5页

RDNet: Region specific iterative deformation with multi-scale attention for medical image registration 基于多尺度关注的区域特定迭代变形医学图像配准

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131455

Wenming Cao , Naeem Hussain , Zhiyue Yan

{"title":"RDNet: Region specific iterative deformation with multi-scale attention for medical image registration","authors":"Wenming Cao , Naeem Hussain , Zhiyue Yan","doi":"10.1016/j.neucom.2025.131455","DOIUrl":"10.1016/j.neucom.2025.131455","url":null,"abstract":"<div><div>Deformable medical image registration is essential for various clinical applications, including diagnosis, treatment planning, and disease monitoring. Although significant progress has been made with pyramid architecture, they often struggle to effectively capture the complex variations in deformation fields at feature maps with different resolutions. However, conventional skip connection designs inadequately address the asymmetric roles of moving and fixed images in deformation estimation, as they treat both images symmetrically without accounting for their distinct contributions to the alignment process. To address these challenges, we present RDNet, a learning-based dual-stream pyramid-based framework incorporating two key components: the Mapping Block (MB) and the Region Specific Layer (RSL). The MB module is carefully integrated into the fixed image skip connections to improve hierarchical feature alignment between the encoder and decoder. The high-level hierarchical semantic gap is efficiently minimized by MB through spatial and channel-wise attention methods, improving feature correspondence and registration accuracy. Additionally, to address the challenges caused by complex variations in the pyramid architecture, we present the RSL module in a multi-scale framework. This incorporation improves the capture of long-range dependencies specific to a region, resulting in more precise deformation estimation and improved registration accuracy while minimizing deformation loss. We conducted comprehensive experiments on two publicly available Brain MRI datasets, OASIS and LPBA40, and one Lung CT dataset to demonstrate that our proposed framework achieves state-of-the-art registration results.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131455"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-tail learning with rebalanced contrastive loss 具有再平衡对比损失的长尾学习

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131601

Charika De Alvis, Dishanika Denipitiyage, Suranga Seneviratne

{"title":"Long-tail learning with rebalanced contrastive loss","authors":"Charika De Alvis, Dishanika Denipitiyage, Suranga Seneviratne","doi":"10.1016/j.neucom.2025.131601","DOIUrl":"10.1016/j.neucom.2025.131601","url":null,"abstract":"<div><div>Integrating supervised contrastive loss to cross entropy-based classification has recently been proposed as a solution to address the long-tail learning problem. However, when the class imbalance ratio is high, it requires adjusting the supervised contrastive loss to support the tail classes, as the conventional contrastive learning is biased towards head classes by default. To this end, we present Rebalanced Contrastive Learning (RCL), an efficient means to increase the long-tail classification accuracy by addressing three main aspects: 1. Feature space balancedness – Equal division of the feature space among all the classes 2. Intra-Class compactness – Reducing the distance between same-class embeddings 3. Regularization – Enforcing larger margins for tail classes to reduce overfitting. RCL adopts class frequency-based SoftMax loss balancing to supervised contrastive learning loss and exploits scalar multiplied features fed to the contrastive learning loss to enforce compactness. We implement RCL on the Balanced Contrastive Learning (BCL) Framework, which has the SOTA performance. Our experiments on three benchmark datasets CIFAR10-LT,CIFAR100-LT and ImageNet-LT demonstrate the richness of the learnt embeddings and increased top-1 balanced accuracy RCL provides to the BCL framework. We further demonstrate that the performance of RCL as a standalone loss also achieves state-of-the-art level accuracy.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131601"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive reverse perturbation network for audio deepfake detection 音频深度假检测的自适应逆摄动网络

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131466

Xue Ouyang , Chunhui Wang , Bin Zhao , Hao Li

{"title":"Adaptive reverse perturbation network for audio deepfake detection","authors":"Xue Ouyang , Chunhui Wang , Bin Zhao , Hao Li","doi":"10.1016/j.neucom.2025.131466","DOIUrl":"10.1016/j.neucom.2025.131466","url":null,"abstract":"<div><div>The growing prevalence of audio deepfakes underscores the urgent need for advanced detection frameworks capable of identifying subtle synthetic artifacts. In response to this challenge, we propose an Adaptive Reverse Perturbation Network, a novel architecture that leverages partial reversal strategies on speech segments and incorporates hierarchical feature discrepancy analysis to enhance deepfake detection. Specifically, the proposed framework employs learnable reversal modules to capture phase discontinuities and spectral anomalies, and utilizes Prime-window reversal to reveal synthetic artifacts that emerge exclusively in reversed speech. Evaluations conducted on five benchmark datasets demonstrate the superior performance of the proposed method, achieving an equal error rate of 1.98 %, representing a 39.6 % improvement over previous systems, as well as a t-DCF of 0.237. Further analysis reveals an inverse correlation between language-specific weight similarity and detection accuracy. These results validate the effectiveness of the trainable differential convolution and reverse perturbation strategies in combating the evolving threat of audio deepfakes, and provide novel insights into phonological artifact patterns associated with synthetic speech.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131466"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning interpretable dynamics: Influence-based clustering of energy consumption time series 学习可解释的动态：基于影响的能源消耗时间序列聚类

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131578

Binbin Li , Xiufeng Liu , Rongfei Ma , Yuhao Ma

{"title":"Learning interpretable dynamics: Influence-based clustering of energy consumption time series","authors":"Binbin Li , Xiufeng Liu , Rongfei Ma , Yuhao Ma","doi":"10.1016/j.neucom.2025.131578","DOIUrl":"10.1016/j.neucom.2025.131578","url":null,"abstract":"<div><div>Energy consumption is governed by dynamic temporal patterns, context, and user behavior. Traditional clustering methods, often operating on raw data, struggle to capture evolving feature relationships and provide interpretable subgroup definitions. To overcome these limitations, we propose a novel framework, <strong>Dynamic Influence-Based Clustering</strong>, that leverages explainable machine learning (XML) to transform time-series data into an interpretable influence space. Unlike existing approaches that apply XML post-hoc or treat clustering and explanation separately, our framework is the first to jointly optimize influence representation generation and dynamic clustering within a unified mathematical framework. In this space, each data point is represented by a vector of feature contributions to an energy usage prediction, estimated using robust attribution methods such as SHAP or Integrated Gradients applied to predictive models like gradient boosting machines or neural networks. We then introduce a dynamic clustering algorithm that optimizes a composite objective balancing cluster cohesion in the influence space with novel constraints for temporal continuity and contextual alignment—capabilities entirely absent from existing clustering methods. This integrated design enables the robust detection of evolving consumer subgroups and facilitates subgroup transition analysis and anomaly detection. Extensive experiments on two real-world energy datasets demonstrate that our framework produces demonstrably more interpretable, stable, and coherent clusters compared to both standard clustering on raw features and state-of-the-art time-series clustering baselines. The proposed framework provides actionable insights into dynamic energy usage and offers a rigorous foundation for developing interpretable learning systems in time-sensitive domains.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131578"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RoleCF: Role-oriented coarse-to-fine emotion cause recognition for empathetic response generation 角色导向的从粗到细的情感原因识别，用于共情反应的生成

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131603

Wei Zhang , Lifang Wang , Ming Xia , Ronghan Li , Zhongtian Hu , Jiashi Lin

{"title":"RoleCF: Role-oriented coarse-to-fine emotion cause recognition for empathetic response generation","authors":"Wei Zhang , Lifang Wang , Ming Xia , Ronghan Li , Zhongtian Hu , Jiashi Lin","doi":"10.1016/j.neucom.2025.131603","DOIUrl":"10.1016/j.neucom.2025.131603","url":null,"abstract":"<div><div>In empathetic response generation, reasoning about conversational emotions by recognizing the causes of emotions is a key technique for achieving empathy. However, existing approaches encounter two fundamental limitations. First, they predominantly focus on fine-grained analysis of emotion causes at the token level, neglecting the broader, more comprehensive analysis at the utterance level. Second, these methods fail to consider emotion causes from the perspectives of different roles, resulting in biased emotional inference. To tackle the aforementioned challenges, we propose RoleCF, an innovative framework that aims to improve empathetic response generation by identifying role-oriented emotion causes in a coarse-to-fine-grained manner. Our approach models the extraction of emotion causes from different perspectives by constructing two distinct heterogeneous graphs for the user and the agent, respectively. Emotion cause nodes within each graph are utilized to swiftly capture emotion causes at the utterance level, providing a holistic understanding of the dialogue context. In addition, we employ two role-interaction modules to selectively integrate the most relevant information from the counterpart, thereby enhancing the recognition of fine-grained emotion causes. Guided by the agent’s state in the generation process, our model achieves superior performance on two benchmark datasets. This is supported by both automatic and human evaluations, demonstrating its effectiveness in capturing and leveraging the underlying causes of emotions for response generation.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131603"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of memristor performance in neural networks using an AHaH framework 基于ahh框架的神经网络忆阻器性能评估

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131606

Xiang Xu, Gangquan Si, Minglin Xu, Yukaichen Yang, Chenhao Li

{"title":"Evaluation of memristor performance in neural networks using an AHaH framework","authors":"Xiang Xu, Gangquan Si, Minglin Xu, Yukaichen Yang, Chenhao Li","doi":"10.1016/j.neucom.2025.131606","DOIUrl":"10.1016/j.neucom.2025.131606","url":null,"abstract":"<div><div>Memristor-based neural networks show significant potential for advancing neuromorphic computing by mimicking synaptic behavior. However, their performance can be compromised by various operational conditions, including noise, degradation, and sudden resistance changes.</div><div>In this paper, we propose a refined simulation method and a novel device evaluation framework, leveraging the AHaH Framework, to enhance the performance and reliability of memristor-based neural networks. The improved simulation approach is designed to incorporate realistic features, such as linear and non-linear decay, periodic and aperiodic fluctuations, and customizable behaviors, allowing for a more accurate depiction of memristor dynamics. Through this evaluation, critical impacts on neural network accuracy and efficiency are uncovered, particularly under complex noise patterns and degradation scenarios.</div><div>The device evaluation framework illustrates how devices, despite exhibiting similar classification accuracy, can display distinct dynamic properties through the monitoring of midpoint voltage variations. These findings provide a basis for robust neuromorphic circuit development.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131606"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-modal 3D multi-object tracking with robust association and track drift compensation 基于鲁棒关联和航迹漂移补偿的多模态三维多目标跟踪

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131687

Chen Xie , Ciyun Lin , Xiaoyu Zheng , Bowen Gong , Antonio M. López

{"title":"Multi-modal 3D multi-object tracking with robust association and track drift compensation","authors":"Chen Xie , Ciyun Lin , Xiaoyu Zheng , Bowen Gong , Antonio M. López","doi":"10.1016/j.neucom.2025.131687","DOIUrl":"10.1016/j.neucom.2025.131687","url":null,"abstract":"<div><div>3D multi-object tracking is crucial for enhancing the understanding of the environment in autonomous driving and robotics. Low-quality detections and less robust associations are two challenges in the point-aware tracking-by-detection paradigm. Conventional approaches suffer from inadequate pre-processing of detected outliers, and poor appearance-based associations during occlusion. To address these issues, this paper proposes a real-time and robust 3D multi-object tracking framework based on the fusion of camera and LiDAR data. Firstly, a two-level association strategy is introduced, whereby high-confidence tracks and detections are initially linked through a straightforward 3D IoU cost, followed by the association of remaining entities using discriminative deep appearance features, emphasizing the similarity between the recently updated track appearance and reemerging targets within dynamically constrained search boundaries. Secondly, a track drift compensation method is presented to refine the low-quality detections using their historically matched tracks, facilitating accurate updates accordingly. Experiments show that the proposed method achieved 79.36 % HOTA and 74 % AMOTA in KITTI and nuScenes benchmarks, respectively. This result surpasses many advanced solutions, particularly exhibiting robust performance in occluded environments.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131687"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RAAG:Redundancy-adaptive and attention-guided token pruning for efficient video action detection RAAG：用于高效视频动作检测的冗余自适应和注意力引导令牌修剪

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131615

Jun Chen , Sailong Deng , Wei Yu , Longsheng Wei

{"title":"RAAG:Redundancy-adaptive and attention-guided token pruning for efficient video action detection","authors":"Jun Chen , Sailong Deng , Wei Yu , Longsheng Wei","doi":"10.1016/j.neucom.2025.131615","DOIUrl":"10.1016/j.neucom.2025.131615","url":null,"abstract":"<div><div>Video action detection faces significant computational challenges, especially with high-resolution and long video sequences. Existing fixed-rate pruning methods are often suboptimal, risking crucial information loss or retaining excessive redundancy. This paper introduces Redundancy-Adaptive and Attention-Guided Token Pruning (RAAG), a novel, adaptive framework for efficient end-to-end video action detection. RAAG integrates Information Redundancy-Adaptive Token Pruning (IRTP), which dynamically adjusts token keep rate based on inter-frame information redundancy, and a Hierarchical Attention-Guided (HAG) strategy, which refines pruning by allocating distinct layer-specific rates to preserve essential features in early layers and aggressively prune in actor-focused middle layers. Comprehensive experiments on AVA 2.2, JHMDB, and UCF101-24 demonstrate RAAG’s superior performance. Notably, RAAG (ViT-L) achieves 40.5 mAP on AVA 2.2, and robustly performs on JHMDB (90.7 mAP) and UCF101-24 (86.5 mAP). These results validate RAAG’s ability to intelligently balance computational efficiency with detection accuracy across diverse video contents.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"658 ","pages":"Article 131615"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CCINet: A cascaded consensus interaction network for co-saliency object detection CCINet：一种用于共显著性目标检测的级联共识交互网络

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-27 DOI: 10.1016/j.neucom.2025.131613

Longsheng Wei , Xu Pei , Jiu Huang , Fan Xu

{"title":"CCINet: A cascaded consensus interaction network for co-saliency object detection","authors":"Longsheng Wei , Xu Pei , Jiu Huang , Fan Xu","doi":"10.1016/j.neucom.2025.131613","DOIUrl":"10.1016/j.neucom.2025.131613","url":null,"abstract":"<div><div>Co-saliency object detection imitates human attention behavior, with the aim of identifying common salient objects in a set of related images. Previous approaches generally suffer from a lack of interaction among the extracted co-saliency information. As a result, the detection maps often turn out to be incomplete or redundant. In this paper, we propose a Cascaded Consensus Interaction Network (CCINet) for co-saliency object detection. This network improves the fusion and interaction among features, thus making full use of the co-saliency information. In the encoding stage, we introduce an Edge Semantic Consensus (ESC) module. It effectively integrates low-level and high-level encoding information. In this way, it is able to capture both fine edge details and rich semantics. Meanwhile, the ESC module refines the co-saliency features, which enhances the detection of co-saliency regions. During the up-sampling stage, the Cascaded Contextual Aggregation (CCA) module employs attention mechanisms, adaptive pooling, and separated-dilated convolution for comprehensive feature extraction. This approach effectively reduces background noise and controls the number of parameters. Extensive experiments indicate that our model outperforms many excellent CoSOD methods in recent years on the three most popular benchmark datasets. Source code is available at: <span><span>https://github.com/JoeLAL24/CCINet.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131613"},"PeriodicalIF":6.5,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Traffic prediction based on spatio-temporal feature embedding fusion and gate operation optimization 基于时空特征嵌入融合与闸门运行优化的交通预测

IF 6.5 2区计算机科学

Neurocomputing Pub Date : 2025-09-26 DOI: 10.1016/j.neucom.2025.131658

Xiaotong Geng , Fan Zhang , Mingli Zhang , Hua Wang

{"title":"Traffic prediction based on spatio-temporal feature embedding fusion and gate operation optimization","authors":"Xiaotong Geng , Fan Zhang , Mingli Zhang , Hua Wang","doi":"10.1016/j.neucom.2025.131658","DOIUrl":"10.1016/j.neucom.2025.131658","url":null,"abstract":"<div><div>In real-world traffic prediction problems, there are often complex spatio-temporal features and patterns. To enhance the accuracy and performance of traffic prediction and address these complexities, it is essential to employ effective models and methods to capture spatio-temporal features and patterns of change. For this purpose, we propose a network model that integrates spatio-temporal feature embeddings with gate operation optimization(TSGO). In our model, we design a novel module: the spatio-temporal feature embedding fusion module, which combines input data to strengthen the model’s ability to extract spatio-temporal correlation features, particularly in enhancing temporal features. To further bolster the capture of spatial features, we design an adaptive graph structure learning method based on a node repository, dynamically capturing non-Euclidean spatial correlations within the traffic network. Additionally, to better capture long-term dependence and short-term variations in sequential data, we adopt a new strategy in the Gated Recurrent Unit (GRU): treating the even and odd positions in the input sequence as two separate input streams to generate corresponding update gates and reset gates. This approach enables the model to utilize data more evenly, achieving complementarity between the two sets of features and allowing it to adapt to information at different time scales within the sequential data. In short-term, medium-term, and long-term predictions across three real-world traffic datasets, the TSGO model achieved average MAE reductions of 8.76 %, 10.12 %, and 11.86 %, respectively, compared to the baseline. This demonstrates its capability to generalize across different time scales and significantly improve prediction performance.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"657 ","pages":"Article 131658"},"PeriodicalIF":6.5,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0