Expert Systems with Applications最新文献

Hybrid intelligence–driven global path planning for ships in complex maritime environments 复杂海洋环境下船舶混合智能驱动的全局路径规划

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-05 DOI: 10.1016/j.eswa.2026.131473

Jiao Liu , Kaige Zhu , Yuanqiang Zhang , Miao Gao , Pengjun Zheng

Global ship path planning in complex maritime environments is challenged by dynamic disturbances, vessel-specific constraints, and long-range trajectory dependencies. This study develops an integrated hybrid planning framework that combines deep generative modeling with rule-based optimization. Automatic identification system trajectory time series are first transformed into Gramian Angular Field images to enhance spatio-temporal feature extraction. Vessel type and length are encoded as one-hot vectors and introduced as conditional variables, enabling personalized path generation. These inputs are processed by a Multi-Head Attention–based Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (MHA-cWGAN-GP), in which multi-head attention is used to model long-range dependencies, and conditional Generative Adversarial Network (cGAN) training together with a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) objective is adopted to improve conditioning behavior and training robustness. The model generates initial navigation paths, which are further refined using an A* search procedure that incorporates wind and current disturbances, as well as constraints such as static obstacles, water depth, and Traffic Separation Scheme (TSS) regulations. The final path is smoothed to ensure feasibility and compliance. In case studies for the Ningbo–Zhoushan Port and Yangtze River Estuary, the hybrid planner reduces the number of search nodes from 45 to 57 to 29–35 while simultaneously enforcing TSS, water-depth, wind, and current constraints, with only about a 3–4% increase in path length relative to classical A* and Dijkstra algorithms. The results indicate that the proposed framework effectively integrates learning and optimization, offering a practical and intelligent solution for real-world maritime path planning.

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引用次数: 0

DORF-EASNet: physics-driven real-time seafloor classification via entropy‑regularized acoustic features and adaptive model activation DORF-EASNet：物理驱动的实时海底分类，通过熵正则化声学特征和自适应模型激活

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-04 DOI: 10.1016/j.eswa.2026.131461

Xi Zhao, Qiangqiang Yuan, Quanyin Zhang, Jiadan Xu

Real-time seabed sediment classification (SSC) is crucial for underwater navigation, operations, and habitat assessment. Conventional methods relying on post-mission multibeam-echosounder (MBES) data processing impede in situ decision-making. We propose a novel, real-time SSC method deployable on both shipborne and Autonomous Underwater Vehicle (AUV) platforms, integrating three core components. Primarily, an efficient preprocessing pipeline comprising georeferencing, radiometric normalization, noise suppression, and incidence-angle correction enables rapid conversion of raw MBES backscatter into geometry-consistent tiles, supporting real-time operation with sub-second responsiveness. Afterwards, the system extracts multi-modal descriptors by combining entropy-regularised angular-response fitting for acoustic backscatter, object-level texture analysis using adaptive graph segmentation, and curvature-aware terrain metrics derived from quadratic surface fitting under entropy constraints by considering the physical responses and spatial distribution of MBES images and point clouds. Finally, a Dynamic Optimal Random Forest with Entropy-Adaptive Subnetwork Selection (DORF-EASNet) dynamically selects between a global classifier and lightweight domain-specific sub-models to match local acoustic complexity, achieving a balance between inference efficiency and physical interpretability. Field experiments conducted in Jiaozhou Bay and the South China Sea demonstrate the proposed framework’s robustness across platforms and sensing configurations, achieving macro-F1 scores of 0.881 and 0.913, respectively, while maintaining real-time processing capability exceeding that of conventional offline methods.

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引用次数: 0

Robot path planning based on multi-strategy enhanced aquila optimizer algorithm in complex environments 复杂环境下基于多策略增强aquila优化算法的机器人路径规划

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-03 DOI: 10.1016/j.eswa.2026.131489

Yu Zhou , Xing Liu , Jianqiao Long , Yitian Lu , Jiaoyang Cheng , Jichun Li

Path planning is a core challenge in autonomous navigation and continuously attracts significant attention in mobile robotics. While optimization algorithms are widely employed for solving robot path planning problems, the Aquila Optimizer (AO) suffers from slow convergence and a tendency to become trapped in local optima. To address these limitations, we propose a robot path planning method based on a Multi-strategy Enhanced Aquila Optimizer (MEAO). In MEAO, the initial population is enhanced using opposition-based learning, and an adaptive parameter mechanism balances exploration and exploitation. During the narrowed exploration phase, a phasor operator enables non-parametric optimization to improve global search capability, while a differential evolution mutation strategy is embedded to strengthen local exploitation. The algorithm’s performance is validated on the CEC2022 benchmark functions with ablation studies confirming the effectiveness and synergy of the various strategies. MEAO is further applied to robot path planning, with simulations performed on various complex two-dimensional grid maps, and comparisons made against several intelligent optimization-based algorithms. In addition, to address the limitations of the traditional Dynamic Window Approach (DWA) in terms of dynamic obstacle avoidance robustness and susceptibility to local minima, we introduce a dynamic threat response mechanism and an adaptive heading trap detection strategy. A collaborative framework combining MEAO-based global planning with the improved DWA for local obstacle avoidance is then established. Experimental results demonstrate that MEAO achieves shorter path lengths and faster convergence, while the improved DWA significantly enhances obstacle avoidance robustness in complex environments. The proposed collaborative framework thus ensures globally optimal paths and reliable real-time local obstacle avoidance, demonstrating the practicality and efficiency of the MEAO algorithm and improved DWA for mobile robot navigation.

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引用次数: 0

3D-MolGL: A multimodal framework for integrating 3D molecular graphs into language models 3D- molgl：用于将3D分子图集成到语言模型中的多模态框架

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-03 DOI: 10.1016/j.eswa.2026.131437

Huizhi Li , Dagang Li , Jinglin Zhang , Yuhui Zheng , Cong Bai

Large Language Models (LLMs) have exhibited remarkable capabilities in natural language generation and have been extensively applied to diverse tasks such as text generation and medical literature analysis, demonstrating robust proficiency in structured data processing and knowledge extraction. However, these models generally overlook the crucial three-dimensional (3D) molecular conformations, which are vital for understanding key chemical properties. This oversight significantly limits the potential of LLMs in the biomolecular field, particularly in complex tasks like drug structure discovery. To address this, we propose a Multimodal Framework for Integrating 3D Molecular Graphs into Language Models (3D-MolGL). It employs a Physics-Informed Equivariant Graph Neural Network (PI-EGNN) incorporating physically meaningful edge-level priors and physics-based regularization, aligning learned representations with empirical data and physical laws. Our approach incorporates an Iterative Cross-Modal Fusion module to reinforce structural and linguistic information, enabling the model to capture complex dependencies and improve the alignment between molecular data and natural language. Moreover, the Region-Phrase Semantic Grounding module enables fine-grained alignment between molecular substructures and linguistic tokens, thereby reinforcing the connection between molecular semantics and their textual representation. Additionally, the Best-of-N sampling strategy enhances output reliability. Notably, 3D-MolGL achieves competitive or state-of-the-art performance in molecule captioning and 3D-aware question answering tasks, while utilizing approximately 75% fewer parameters than existing large-scale multimodal architectures. This demonstrates that robust molecular reasoning capabilities can be achieved with more compact models, providing a promising new perspective for interpretable AI in chemistry.

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引用次数: 0

Local sharpness aware minimization in decentralized federated learning with privacy protection 具有隐私保护的分散联邦学习中的局部锐度感知最小化

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-02 DOI: 10.1016/j.eswa.2026.131510

Jifei Hu , Yanli Li , Huayong Xie , Lijun Xu , Hang Zhang , Xinqiang Zhou

Federated learning (FL) enables distributed model training while preserving data privacy, but it still faces challenges from data heterogeneity and privacy constraints. Existing optimization methods aiming to flatten loss landscapes often fail to resolve inconsistencies between local and global flatness due to hyperparameter dependencies and centralized aggregation. Moreover, privacy-preserving techniques such as differential privacy (DP) can distort gradients, leading to sharper loss landscapes and hindered convergence. To tackle these issues, we propose DFedLSAM (Decentralized Federated Local Sharpness-Aware Minimization), a novel framework that eliminates the central server and uses the Sharpness-Aware Minimization (SAM) optimizer on the client side to maintain locally flattened loss landscapes. DFedLSAM adopts a dual-model architecture, where each client trains a sharing model for cross-client knowledge exchange and a private model updated via knowledge distillation (KD) from the sharing model’s soft logits, thereby reducing data heterogeneity and mitigating DP-induced sharpness. Building on this design, we introduce a perturbation-based SAM variant, integrated into the framework as DFedLSAM-Pert, which allocates perturbations according to layer-wise sensitivity and improves generalization without sacrificing privacy. Extensive experiments on benchmark image datasets and real-world medical datasets show that DFedLSAM and its perturbation-based extension DFedLSAM-Pert outperform existing baselines, especially in non-IID settings and under strict privacy budgets. These results indicate that DFedLSAM and DFedLSAM-Pert provide practical solutions for privacy-preserving FL in healthcare and other sensitive application domains.

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引用次数: 0

Discovering anomalous sequences in attributed graphs: A parameter-light approach 发现属性图中的异常序列：一种轻参数的方法

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-02 DOI: 10.1016/j.eswa.2026.131467

Cheng He , Xinyang Chen , Amaël Broustet , Guoting Chen

Graphs have been widely used across scientific disciplines, from sociology to biology, particularly when modeling temporal evolution. Although many algorithms have been developed to discover patterns in graphs, they face three main limitations. First, most algorithms assume that each node or edge is associated with a single attribute, whereas real-world applications often involve multiple attributes to capture events more comprehensively. Second, existing methods typically require tuning several hyperparameters, which can vary significantly across different datasets. Third, most approaches focus on identifying frequent patterns, often overlooking rare but meaningful ones. To address these limitations, this paper proposes a framework for discovering anomalous sequences in attributed graphs. Instead of relying on frequency-based measures, the framework adopts an entropy-based method for pattern mining, thereby requiring at most one hyperparameter. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach in detecting anomalous sequences. Moreover, we extend the framework to applications in optics, where it is used to evaluate phase differences.

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引用次数: 0

Chrysanthemum image quality assessment via multi-scale feature fusion and meta-learning 基于多尺度特征融合和元学习的菊花图像质量评估

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-02 DOI: 10.1016/j.eswa.2026.131378

Shun Zhu , Xichen Yang , Tianshu Wang , Zhongyuan Mao , Yifan Chen , Jian Jiang , Hui Yan

The origin tracing of chrysanthemum is significant in ensuring the quality of chrysanthemum. With the development of computer vision, it is feasible to utilize vision technology to the origin tracing. This enables intelligent origin tracing, thereby improving efficiency and accuracy. However, image distortions are inevitable while collecting chrysanthemum images. These distortions, such as incomplete chrysanthemum tissue and poor angle, tend to reduce the accuracy of the origin tracing. Thus, it is important to measure the image quality accurately, and then further improve the accuracy of the origin tracing. Considering it, we proposed a chrysanthemum image quality assessment method. First, a two-step screening (TSS) module is designed to screen existing classically distorted images that are suitable for distorted chrysanthemum images. Second, a deep feature extraction module is utilized to extract features at different receptive field levels. Third, the semantic analysis module is used to analyze and fuse the semantic information of different features. Finally, the meta-learning framework is designed to improve the accuracy and robustness of the model. The prior knowledge acquired through meta-learning is utilized to fine-tune the model with few-shot samples. The experimental results demonstrate that the proposed method can accurately judge incomplete and angle distortions, and thus effectively promote the accuracy of origin tracing. Our codes and models are available at https://github.com/dart-into/a-chrysanthemum-Screening-Method.

{"title":"Chrysanthemum image quality assessment via multi-scale feature fusion and meta-learning","authors":"Shun Zhu , Xichen Yang , Tianshu Wang , Zhongyuan Mao , Yifan Chen , Jian Jiang , Hui Yan","doi":"10.1016/j.eswa.2026.131378","DOIUrl":"10.1016/j.eswa.2026.131378","url":null,"abstract":"<div><div>The origin tracing of chrysanthemum is significant in ensuring the quality of chrysanthemum. With the development of computer vision, it is feasible to utilize vision technology to the origin tracing. This enables intelligent origin tracing, thereby improving efficiency and accuracy. However, image distortions are inevitable while collecting chrysanthemum images. These distortions, such as incomplete chrysanthemum tissue and poor angle, tend to reduce the accuracy of the origin tracing. Thus, it is important to measure the image quality accurately, and then further improve the accuracy of the origin tracing. Considering it, we proposed a chrysanthemum image quality assessment method. First, a two-step screening (TSS) module is designed to screen existing classically distorted images that are suitable for distorted chrysanthemum images. Second, a deep feature extraction module is utilized to extract features at different receptive field levels. Third, the semantic analysis module is used to analyze and fuse the semantic information of different features. Finally, the meta-learning framework is designed to improve the accuracy and robustness of the model. The prior knowledge acquired through meta-learning is utilized to fine-tune the model with few-shot samples. The experimental results demonstrate that the proposed method can accurately judge incomplete and angle distortions, and thus effectively promote the accuracy of origin tracing. Our codes and models are available at <span><span>https://github.com/dart-into/a-chrysanthemum-Screening-Method</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"312 ","pages":"Article 131378"},"PeriodicalIF":7.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122752","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

Local attention alignment fusion network for domain adaptive water body segmentation 区域自适应水体分割的局部注意力对齐融合网络

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-02 DOI: 10.1016/j.eswa.2026.131382

Hao Liu , Xiaobin Zhu , Xu Qizhi , Chun Yang , Hongyang Zhou , Yongjie Xia , Xucheng Yin

Water body segmentation is crucial for various tasks, e.g., disaster early warning and ecological management. Existing deep learning-based methods mainly focus on specific scenarios, often encountering significant performance drops on multi-source satellite data with large domain differences. In this paper, we propose a novel Local Attention Alignment Fusion Network for domain adaptive water body segmentation (dubbed LAAFNet). Our LAAFNet explores spatial relationships between pseudo-RGB and pseudo-NIR images, and then extracts invariant features via local attention to improve the representative capability of cross-domain features. Moreover, we design a novel Difficult Sample Point Loss (DSPLoss) to address the presence of potential positive samples within negative regions across domains through a pixel-level contrastive learning strategy. DSPLoss leverages a Cauchy-Schwarz-based constraint to regulate the upper bound of feature similarity in the pixel-level inner product space. This constraint enhances the separation between water bodies and background in hard samples, allowing the model to learn a clearer decision boundary and thereby improving its generalization capability. Notably, we construct a large-scale Water Generation Testing Dataset (WGTDataset) to evaluate water body segmentation in real-world applications. Experimental results demonstrate that the LAAFNet outperforms the state-of-the-art (SOTA) methods. The codes and dataset are available on: https://github.com/LH325/LAAFNet.

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引用次数: 0

Visible-guided multigranularity prompt learning for visible-infrared person re-identification 可见制导的多粒度提示学习，用于可见红外人再识别

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-02 DOI: 10.1016/j.eswa.2026.131464

Yangyan Luo, Ying Chen

Visible-infrared person re-identification (VI-ReID) remains challenging due to substantial cross-modal discrepancies and the absence of explicit semantic correspondence. This paper presents a novel Visible-Guided Multigranularity Prompt Learning (VG-MPL) framework that integrates semantic reasoning into cross-modal alignment through language-guided prompt learning. A fine-grained adaptive prompt is constructed by decomposing textual templates into learnable semantic slots, whose activations are dynamically modulated by a Prompt Slot Router (PSR) guided by visible features. This design enables sample-specific semantic modeling and enhances interpretability. To establish coherent cross-modal representations, a multi-granularity consistency constraint is imposed across the hierarchical layers of the CLIP text encoder, ensuring that global identity and local attribute semantics remain aligned. Furthermore, an Alternative Cross-Modal Alignment (ACMA) strategy and its theoretical analysis promotes bidirectional learning between visible and infrared modalities, improving optimization stability and preventing one-sided collapse. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate that VG-MPL achieves state-of-the-art performance and superior cross-modal generalization, validating the effectiveness of adaptive semantic prompting and hierarchical alignment in bridging the modality gap.

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引用次数: 0

A heterogeneous Hopfield neural network with discrete memristor: modeling, dynamics, and application in medical image encryption 具有离散忆阻器的异构Hopfield神经网络：建模、动态和在医学图像加密中的应用

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications

Pub Date : 2026-02-01 DOI: 10.1016/j.eswa.2026.131457

Huiqun Zou , Yang Lu , Wenjiao Li , Wenhui Li , Xiuli Chai

Memristors and activation functions critically shape the nonlinear dynamics of Hopfield neural networks. While previous studies explored memristor modeling and heterogeneous activation separately, their combination to form heterogeneous memristive networks remains insufficiently explored. This paper bridges this gap by proposing a novel Heterogeneous Hopfield Neural Network with Discrete Memristor (HHNN-DM), coupling a discrete memristor with heterogeneous activations to mimic neural diversity. By analyzing dissipation, equilibrium stability, bifurcation diagrams, and Lyapunov exponents, we demonstrate that heterogeneous activation mechanisms significantly enhance network complexity and unpredictability under memristive interactions. This synergy gives rise to rich chaotic behaviors, such as periodic orbits, bifurcations, transient chaos, and chaotic bursting. As these biologically inspired chaotic dynamics, the resulting high-quality chaotic sequences are well suited for cryptographic applications. Furthermore, a Heterogeneous Hopfield Neural Network–based Medical Image Encryption Algorithm (HHNN-MIEA) is developed to enhance security in remote medical image transmission, integrating an X-fractal curve sorting matrix for permutation with multi-logical diffusion driven by chaotic sequences. Experimental results verify that the HHNN-MIEA achieves high security in aspects such as key sensitivity, information entropy without compromising efficiency, highlighting its effectiveness, robustness and reliable solution for secure medical image transmission.

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引用次数: 0