IEEE transactions on neural networks and learning systems最新文献_第2页

IEEE Transactions on Neural Networks and Learning Systems Publication Information IEEE神经网络与学习系统汇刊

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-07 DOI: 10.1109/tnnls.2025.3611090

引用次数: 0

IEEE Computational Intelligence Society Information IEEE计算智能学会信息

IF 8.9 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-07 DOI: 10.1109/TNNLS.2025.3611088

引用次数: 0

MDSF-YOLO: Advancing Object Detection With a Multiscale Dilated Sequence Fusion Network. MDSF-YOLO：基于多尺度扩展序列融合网络的超前目标检测。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-07 DOI: 10.1109/tnnls.2025.3617122

Yu Sun,Chong Zhang,Xian Li,Xuyang Jing,Hui Kong,Qing-Guo Wang

{"title":"MDSF-YOLO: Advancing Object Detection With a Multiscale Dilated Sequence Fusion Network.","authors":"Yu Sun,Chong Zhang,Xian Li,Xuyang Jing,Hui Kong,Qing-Guo Wang","doi":"10.1109/tnnls.2025.3617122","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3617122","url":null,"abstract":"Accurate and fast detection of traffic signs is critical for autonomous driving, particularly in complex environments with diverse sign scales and varying detection distances. Existing approaches, incorporating attention modules or modifying detection heads, frequently encounter high rates of false positives and omissions due to the increased sampling depth. To address these limitations, we propose MDSF-you only look once (YOLO), a novel detection framework that integrates multiscale sequence fusion (MSF) for synergistic feature integration across granularities, enhancing the precision of both localization and semantic information fusion. Additionally, our dilated-wise residual (DWR) module leverages dilated convolutions and channel-wise reparameterization to improve fine-grained feature extraction. The architecture further introduces a $P_{2}$ detection head for shallow features and fully decouples all detection heads, optimizing target localization and category identification. Extensive experiments on the TT100K and CCTSDB2021 datasets demonstrate the superiority of MDSF-YOLO over benchmark models, including YOLOv11s, with significant improvements in mAP by 8.8% and 2.4% on respective datasets while substantially reducing false positives and leakage rate. Besides, the marked improvement of MDSF-YOLO on the VisDrone2019 dataset verifies its enhanced capability to address drone-based object detection. These advances underscore the efficiency and robustness of the proposed model, providing a promising solution for autonomous driving and similar object detection scenarios.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"20 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

A Novel Pattern Learning Framework With Enhanced Scalability for Continuous Optimization. 一种增强可扩展性的模式学习框架。

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-07 DOI: 10.1109/tnnls.2025.3610993

Jian Qin,Yuanqiu Mo,Hongzhe Liu,Zhi-Hui Zhan,Wenwu Yu

{"title":"A Novel Pattern Learning Framework With Enhanced Scalability for Continuous Optimization.","authors":"Jian Qin,Yuanqiu Mo,Hongzhe Liu,Zhi-Hui Zhan,Wenwu Yu","doi":"10.1109/tnnls.2025.3610993","DOIUrl":"https://doi.org/10.1109/tnnls.2025.3610993","url":null,"abstract":"Multiobjective optimization problems (MOPs) arise in numerous real-world scenarios, yet finding their solutions with optimal trade-offs can be a formidable challenge. This article studies the continuous optimization problem involving large-scale variables, many objectives, and intricate constraints, which is rarely comprehensively discussed in existing works, due to the coexisting difficulties posed by the curse of dimensionality, selection pressure, and feasibility restrictions. To address these problems, this work pioneers a novel optimization framework, optimization pattern learning, embedded with machine learning (ML) techniques. Within this framework, the concept of measurable order and its corresponding learning mechanism are proposed to extract valuable knowledge from solutions. This measurable order is a general form of those orders used explicitly or implicitly in the existing studies, providing a more flexible means to evaluate solutions for efficient optimization adaptively. By substituting original solutions with their measurable orders, this framework effectively avoids the selection pressure from many objectives and the feasibility restrictions from intricate constraints. Furthermore, two novel ML models based on measurable orders are developed to progressively learn effective optimization patterns from iterative data in high-dimensional search spaces. Leveraging these learned patterns, this framework successfully addresses the curse of dimensionality from large-scale variables and thus achieves efficient optimization. Owing to the strong adaptability and search capabilities of this framework, it also demonstrates excellent scalability as the number of variables, objectives, and constraints increases. Extensive simulations validate the effectiveness of the framework and underscore its competitiveness relative to state-of-the-art algorithms in this field.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"58 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

IEEE Transactions on Neural Networks and Learning Systems Information for Authors IEEE神经网络与学习系统信息汇刊

IF 8.9 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-07 DOI: 10.1109/TNNLS.2025.3611086

引用次数: 0

PID: A Parameter-Efficient Isolation Domain-Incremental Learning Framework for Signal Modulation Classification PID：一种参数有效的信号调制分类隔离域-增量学习框架

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-06 DOI: 10.1109/tnnls.2025.3615307

Guanchun Wang, Ziyi Liu, Xiangrong Zhang, Yifan Chen, Yifang Zhang, Jin Zhu, Licheng Jiao

引用次数: 0

Three-Dimensional Multiobject Tracking Based on Voxel Masking Encoder and Deep Hashing Paradigm 基于体素掩蔽编码器和深度哈希范式的三维多目标跟踪

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-06 DOI: 10.1109/tnnls.2025.3613354

Saiyu Li, Zhong Chen, Hui Li, Ye Tao, Ying Gao, Jun Yan

引用次数: 0

DMutDE: Dual-View Mutual Distillation Framework for Knowledge Graph Embeddings 知识图嵌入的双视图互蒸馏框架

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-06 DOI: 10.1109/tnnls.2025.3608503

Ruizhou Liu, Zhe Wu, Yiling Wu, Zongsheng Cao, Qianqian Xu, Qingming Huang

引用次数: 0

Syntax-Oriented Shortcut: A Syntax Level Perturbing Algorithm for Preventing Text Data From Being Learned 面向语法的快捷方式：一种防止文本数据被学习的语法级扰动算法

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-06 DOI: 10.1109/tnnls.2025.3609842

Bo Li, Kun Zhang, Xi Chen, Richang Hong

引用次数: 0

Unbiased Semantic Decoding With Vision Foundation Models for Few-Shot Segmentation 基于视觉基础模型的无偏语义解码少镜头分割

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-10-06 DOI: 10.1109/tnnls.2025.3611497

Jin Wang, Bingfeng Zhang, Jian Pang, Weifeng Liu, Baodi Liu, Honglong Chen

引用次数: 0