Expert Systems with Applications最新文献_第8页

Deep low-rank tensor embedded network for hyperspectral image super-resolution 高光谱图像超分辨率的深度低秩张量嵌入网络

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-11 DOI: 10.1016/j.eswa.2025.129864

Qiang Zhang , Xianpeng Zhang , Yi Xiao , Hongjie Xie

{"title":"Deep low-rank tensor embedded network for hyperspectral image super-resolution","authors":"Qiang Zhang , Xianpeng Zhang , Yi Xiao , Hongjie Xie","doi":"10.1016/j.eswa.2025.129864","DOIUrl":"10.1016/j.eswa.2025.129864","url":null,"abstract":"<div><div>Recent efforts have witnessed significant progress in deep-learning-based hyperspectral image super-resolution (HSISR). However, most existing methods focus solely on spatial or spectral exploration, while lacks enough consideration of the intrinsic correlation between these aspects. This oversight limits the potential for collaborative optimization, leading to suboptimal feature representations of HSI. Moreover, they mainly engaged in super-resolve the pixel-wise spatial details, neglecting the vital spectral consistency. To mitigate these issues, this paper proposed LRTENet, a novel deep low-rank tensor embedding network for HSISR, which effectively bridges the optimization gap between spatial and spectral features with well-defined low-rank tensor decomposition. Specially, we introduce a low-rank embedding module (LREM) to extract low-rank dependencies across multiple directions facilitating a holistic mapping by adaptively integrating these tensors. This enables our model to generate discriminative spatial-spectral representations for accurate reconstruction. Furthermore, to better preserve the spectral consistency, we incorporate LREM after upsample operation to progressively refine and correct spectral distortion. Extensive experiments demonstrate that LRTENet achieves superior spatial reconstruction and spectral preservation performance, outperforming state-of-the-art methods on various benchmarks, including Chikusei, CAVE, and Pavia.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"299 ","pages":"Article 129864"},"PeriodicalIF":7.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334173","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

CCF-former: A transformer with cross-channel feature aggregation and frozen backbone for fault prediction ccf变换器：一种具有跨通道特征聚合和冻结主干的变压器，用于故障预测

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129963

Ting Li , Huanlin Huang , Kai Yang , Jing Wen

{"title":"CCF-former: A transformer with cross-channel feature aggregation and frozen backbone for fault prediction","authors":"Ting Li , Huanlin Huang , Kai Yang , Jing Wen","doi":"10.1016/j.eswa.2025.129963","DOIUrl":"10.1016/j.eswa.2025.129963","url":null,"abstract":"<div><div>Unexpected system faults may cause significant economic losses, service disruption, and safety risks in failure-prone interconnected systems, including industrial and distributed computing infrastructures. Therefore, accurate and timely fault prediction is essential for ensuring system reliability and maintaining continuous service availability. In this paper, we propose CCF-Former, a Transformer-based fault prediction framework that combines <strong>C</strong>ross-<strong>C</strong>hannel feature aggregation and a <strong>F</strong>rozen pretrained backbone to predict failures in such interconnected systems. The proposed framework exhibits excellent fault prediction performance, maintaining both high precision and robustness. The framework combines three main components: (1) a <em>Cross-Channel Feature Aggregation Module (CCFAM)</em> that captures long-range dependencies and subtle fault patterns by aggregating and redistributing informative representations across input features; (2) a <em>Frozen Pre-trained Transformer Module (FPTM)</em> that captures temporal patterns using rich pre-trained representations, significantly reducing resource consumption and avoiding repeated fine-tuning; and (3) a <em>Failure Inference Module (FIM)</em> that produces reliable fault judgements through reconstruction-based scoring and adaptive thresholding. Extensive experiments on multiple public benchmarks, including server monitoring and spacecraft telemetry datasets, demonstrate that CCF-Former consistently outperforms state-of-the-art baselines, achieving a top F1-score of 87.94 %. The proposed framework offers a robust and effective solution for fault prediction in complex interconnected systems. Our code is publicly available at <span><span>https://github.com/Yolandalt/CCF-Former</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"299 ","pages":"Article 129963"},"PeriodicalIF":7.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334167","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

SFLES: Shuffled differentially private federated learning with early-stopping strategy 基于早期停止策略的不同洗牌私有联邦学习

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129970

Yanhui Li , Chen Huang , Yuxin Zhao , Xinjie Du , Junqing Huang , Ye Yuan

{"title":"SFLES: Shuffled differentially private federated learning with early-stopping strategy","authors":"Yanhui Li , Chen Huang , Yuxin Zhao , Xinjie Du , Junqing Huang , Ye Yuan","doi":"10.1016/j.eswa.2025.129970","DOIUrl":"10.1016/j.eswa.2025.129970","url":null,"abstract":"<div><div>Federated Learning (FL) allows multiple clients to collaboratively train a global model without sharing raw data, yet it remains susceptible to privacy attacks. The recently proposed shuffle model of differential privacy (DP) offers a promising solution by leveraging privacy amplification to achieve strong local privacy guarantees while maintaining high utility. However, existing approaches based on this model rely on conventional Gaussian or Laplace mechanisms, which introduce unbounded noise and risk significant data distortion. Furthermore, these methods typically exhibit inefficient privacy budget allocation and suffer from excessive communication overhead and computational costs imposed by fixed training rounds, ultimately degrading performance. To address these limitations, we present SFLES, a novel shuffled differentially private FL framework designed to robustly prevent privacy leakage while optimizing model utility. In particular, SFLES employs Top-<em>k</em> sparsification to compress local model updates and integrates an adaptive, layer-wise bounded noise mechanism based on a symmetric piecewise distribution for fine-grained noise injection. To enhance efficiency, we propose a novel directional similarity-aware aggregation strategy, which prioritizes updates with consistent directional trends, accelerating convergence under DP constraints. Additionally, SFLES incorporates a dynamic early-stopping strategy that tracks update conflict rates and global accuracy trends, dynamically terminating training upon convergence detection and reallocating residual privacy budgets to subsequent rounds for improved utility. Extensive evaluations on MNIST, Fashion-MNIST, and CIFAR-10 demonstrate that SFLES surpasses state-of-the-art alternatives in balancing privacy-utility trade-offs, convergence speed, and communication efficiency.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"299 ","pages":"Article 129970"},"PeriodicalIF":7.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334060","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

Noise-robust and sector-aware representation learning for natural gas demand forecasting 基于噪声鲁棒和行业感知的表征学习的天然气需求预测

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129964

Xinxing Zhou , Jiaqi Ye , Shubao Zhao , Ming Jin , Zhaoxiang Hou , Chengyi Yang , Zengxiang Li , Yanlong Wen , Xiaojie Yuan

{"title":"Noise-robust and sector-aware representation learning for natural gas demand forecasting","authors":"Xinxing Zhou , Jiaqi Ye , Shubao Zhao , Ming Jin , Zhaoxiang Hou , Chengyi Yang , Zengxiang Li , Yanlong Wen , Xiaojie Yuan","doi":"10.1016/j.eswa.2025.129964","DOIUrl":"10.1016/j.eswa.2025.129964","url":null,"abstract":"<div><div>With natural gas becoming a key component of energy systems, precise demand forecasting is crucial for supporting efficient planning and resource management. However, existing methods face two key challenges: substantial noise in industrial datasets and heterogeneous consumption patterns across sectors. Data noise caused by sensor errors, irregular reporting, and logging inconsistencies obscures underlying consumption trends. Simultaneously, sector-specific variations in demand make it challenging to develop a unified forecasting model capable of capturing diverse consumption behaviors. To address these challenges, we propose a novel data forecasting framework that integrates contrastive learning with targeted noise filtering to enhance data representation and prediction robustness. The noise filtering module incorporates a denoising task that enables the model to learn to suppress noise and improve representation reliability. Meanwhile, the contrastive learning mechanism leverages sector-specific information to capture both shared patterns and sectoral usage behaviors. We further introduce a false negative removal strategy to refine sample selection, reducing representation bias and enhancing generalization. Our approach is validated on a large-scale dataset from the ENN Group, covering over 10,000 industrial, commercial, and welfare-related customers across multiple regions. Experimental results demonstrate that our model consistently outperforms a range of state-of-the-art forecasting baselines across both short- and long-term horizons, achieving notably better accuracy and robustness in real-world scenarios. This work demonstrates the potential of noise-robust and sector-aware representation learning for advancing natural gas demand forecasting in real-world applications.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"299 ","pages":"Article 129964"},"PeriodicalIF":7.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334712","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

Adaptive GNSS-5G hybrid positioning based on time offset optimization estimation and multi-rate measurement fusion 基于时差优化估计和多速率测量融合的GNSS-5G自适应混合定位

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129875

Huiyu Chen , Yu Lu , Yao Xing , Xu Zhang , Jiongqi Wang

{"title":"Adaptive GNSS-5G hybrid positioning based on time offset optimization estimation and multi-rate measurement fusion","authors":"Huiyu Chen , Yu Lu , Yao Xing , Xu Zhang , Jiongqi Wang","doi":"10.1016/j.eswa.2025.129875","DOIUrl":"10.1016/j.eswa.2025.129875","url":null,"abstract":"<div><div>The fusion positioning of the Global Navigation Satellite System and Fifth-Generation Mobile Communication Network is a key direction for breaking through the performance bottleneck of a single system. However, it faces two core challenges: inconsistent spatiotemporal benchmarks and multi-rate measurement fusion. To address these issues, this paper proposes a joint time offset estimation and phased fusion strategy: An adaptive time-varying offset model is established, and an adaptive relative time offset estimation algorithm based on pseudo-measurements is designed. The high-precision time benchmark provided by GNSS is used to realize the indirect estimation of the 5G absolute time offset, solving the problem of offset accumulation in dynamic scenarios. A two-stage filtering framework is proposed, which processes the coordinate conversion error of 5G polar coordinate measurements through a modified unbiased converted measurement Kalman filter, combines with a Kalman filter to estimate the target state, and constructs a time offset pseudo-measurement based on velocity estimation for efficient solutions. A phased multi-rate fusion strategy is designed: At GNSS sampling moments, adaptive weighted fusion is used to correct the accumulated errors of 5G high-frequency data; at non-GNSS moments, 5G high-frequency measurements and motion state equation predictions are used to maintain tracking accuracy for high-dynamic targets. Simulation results show that the proposed algorithm significantly outperforms eight mainstream algorithms such as SPP, EKF, and UKF in positioning accuracy, with a total average error of 1.66 m and a total root mean square error of 2.02 m. Moreover, the error distribution is more concentrated and stability is stronger, which can effectively adapt to the needs of high-dynamic scenarios and provide reliable solutions for GNSS-5G hybrid positioning.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"298 ","pages":"Article 129875"},"PeriodicalIF":7.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267977","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

PHGAT: Persistent homology-enhanced graph attention network for IIoT anomaly detection 用于工业物联网异常检测的持久同构增强图关注网络

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129923

Lulu Wang , Yuhua Sun , Xuchong Liu , Chengqing Li

{"title":"PHGAT: Persistent homology-enhanced graph attention network for IIoT anomaly detection","authors":"Lulu Wang , Yuhua Sun , Xuchong Liu , Chengqing Li","doi":"10.1016/j.eswa.2025.129923","DOIUrl":"10.1016/j.eswa.2025.129923","url":null,"abstract":"<div><div>The operational safety and efficiency of modern Industrial Internet of Things (IIoT) systems, which generate massive volumes of high-dimensional multivariate time series data, hinge on the early detection of anomalies. However, existing graph-based methods often struggle with the structural instability of dynamically learned graphs and are blind to higher-order, multi-component system dependencies. This paper introduces the Persistent Homology-enhanced Graph Attention Network (PHGAT). This novel framework addresses these critical limitations by pioneering a co-learning paradigm that structurally regularizes dynamic graph learning through topological invariants. Unlike prior works that apply persistent homology to static graphs or as a simple feature augmentation step, PHGAT introduces a principled framework where pH-derived topological features provide global structural constraints, forcing the model to learn meaningful and robust sensor relationships from noisy time-series data. The framework integrates three key innovations: (1) an adaptive graph construction mechanism that dynamically learns sensor relationships by fusing spatio-temporal correlations to model evolving system dynamics; (2) a hierarchical graph attention architecture with cross-scale mechanisms to capture multi-resolution temporal dependencies; and (3) a learnable topological vectorization component that leverages persistent homology to extract robust structural invariants, enhancing model resilience. Extensive experiments on four public IIoT benchmarks–SWaT, SMD, WADI, and SMAP–demonstrate that PHGAT consistently outperforms state-of-the-art methods by a significant margin. Notably, PHGAT achieves an F1-score of 0.976 on SWaT, improving upon the best-performing baseline by 2.24 %, which validates the efficacy of topological regularization in dynamic graph learning for IIoT anomaly detection.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"299 ","pages":"Article 129923"},"PeriodicalIF":7.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334176","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

Spiking Depth: Depth estimation from sparse events with spiking neural networks 尖峰深度：用尖峰神经网络从稀疏事件中估计深度

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129977

Dongze Liu, Yimeng Fan, Wenrui Lu, Changsong Liu, Wei Zhang

{"title":"Spiking Depth: Depth estimation from sparse events with spiking neural networks","authors":"Dongze Liu, Yimeng Fan, Wenrui Lu, Changsong Liu, Wei Zhang","doi":"10.1016/j.eswa.2025.129977","DOIUrl":"10.1016/j.eswa.2025.129977","url":null,"abstract":"<div><div>Event cameras provide remarkable temporal resolution, wide dynamic range, and low power consumption, making them ideal for depth estimation in high-contrast and dynamic environments. While spiking neural networks (SNNs) are naturally suited to process event data, their performance in depth estimation tasks has not consistently surpassed those of traditional artificial neural networks (ANNs) because of the former’s lack of effective mechanisms for handling the sparse nature of event data. Herein, we propose Spiking Depth, a novel end-to-end SNN framework designed to overcome the limitations of current ANN models and achieve superior depth estimation from sparse event data. In particular, Spiking Depth introduces three key innovations: an event encoding module based on a spiking-driven fusion block (SDFB), enhanced skip connections incorporating both SDFB and an adaptive spiking convolutional block attention module, and the event depth loss that optimizes depth estimation by addressing the sparse and dynamic nature of event data. Spiking Depth outperforms current state-of-the-art SNN and ANN models on two event-based datasets: the Multi Vehicle Stereo Event Camera (MVSEC) dataset, which is a real-world dataset, and a synthetic dataset. On the MVSEC dataset, our model achieves mean depth error values of 11.8 cm, 18.0 cm, and 12.5 cm for Splits 1, 2, and 3, respectively, setting a new benchmark for event-based depth estimation with significantly lower power consumption.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"299 ","pages":"Article 129977"},"PeriodicalIF":7.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334708","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

Adaptive personalized large-scale group decision-making model based on psychobehavior in comprehensive trust network 基于心理行为的综合信任网络自适应个性化大规模群体决策模型

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129863

Wei Yang, Yizhuo Wang

引用次数: 0

Physics-encoded neural network via multi-scale tree-structured graph representation for assessing cardiovascular hemodynamics 基于多尺度树结构图表示的物理编码神经网络评估心血管血流动力学

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129975

Anbang Wang , Xiaofei Xue , Zhifan Gao , Zhihui Zhang , Dan Deng , Xiujian Liu

{"title":"Physics-encoded neural network via multi-scale tree-structured graph representation for assessing cardiovascular hemodynamics","authors":"Anbang Wang , Xiaofei Xue , Zhifan Gao , Zhihui Zhang , Dan Deng , Xiujian Liu","doi":"10.1016/j.eswa.2025.129975","DOIUrl":"10.1016/j.eswa.2025.129975","url":null,"abstract":"<div><div>Hemodynamic assessment is crucial for understanding cardiovascular disease mechanisms and accurate diagnosis. Deep learning with prior physical knowledge holds promise for hemodynamic modelling. However, existing approaches struggle to assess hemodynamics across various cardiovascular systems due to geometric heterogeneity and imbalance learning from competing physical constraints. We propose a multi-scale tree-structured physics-encoded graph neural network for hemodynamic assessment across various cardiovascular systems. We introduce a multi-scale tree-structured graph representation (MTGR) that hierarchically decomposes vascular systems. This enables adaptive geometric modelling while maintaining physiological consistency. Building on MTGR, we propose a physics-encoded computational decoupling paradigm: (1) intra-segment hemodynamic computation within continuous vessel regions and (2) inter-segment hemodynamic coupling at bifurcation nodes. This decoupling paradigm efficiently combines knowledge of morphology and physics. With physics-encoded framework, we achieve the network training with label-free data. Experimental results on coronary and pulmonary arteries validate our framework’s superior generalization across diverse vascular topologies while preserving clinically interpretable physics. The excellent accuracy in predicting functionally significant stenosis demonstrates that this novel methodology has the potential to contribute to the development of innovative diagnostic and treatment strategies in the field of cardiovascular medicine.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"299 ","pages":"Article 129975"},"PeriodicalIF":7.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334709","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

Optimizing job offer packages in a two-sided matching with bounded rationality: a two-stage stochastic approach 有限理性双边匹配条件下的就业机会优化：两阶段随机方法

IF 7.5 1区计算机科学

Expert Systems with Applications Pub Date : 2025-10-10 DOI: 10.1016/j.eswa.2025.129971

Saeed Najafi-Zangeneh, Naser Shams-Gharneh

{"title":"Optimizing job offer packages in a two-sided matching with bounded rationality: a two-stage stochastic approach","authors":"Saeed Najafi-Zangeneh, Naser Shams-Gharneh","doi":"10.1016/j.eswa.2025.129971","DOIUrl":"10.1016/j.eswa.2025.129971","url":null,"abstract":"<div><div>Personnel selection is a two-sided market where companies compete for qualified candidates by designing job-offer packages. However, there is a gap in understanding how to optimize these packages considering candidate preferences and associated costs, while decision-makers exhibit bounded rationality due to limited information or cognitive constraints. This study addresses this gap by proposing a matching framework that accounts for bounded rationality based on the Quantal Response Equilibrium (QRE), in which both sides are not perfect optimizers and face uncertainty in the other side’s actions. Maximum Likelihood Estimation (MLE) and analysis of real hiring data confirm that decision-makers exhibit bounded rationality and tend to behave more rationally as the selection process progresses. Finally, a two-stage stochastic optimization approach using Particle Swarm Optimization (PSO) to determine the optimal job offer package for the organization, taking into account its human resource policies and candidate competencies, is presented. The evaluation of the results and a sensitivity analysis are conducted under rational and bounded rational modes. This approach offers valuable insights for organizations to optimize their hiring processes and attract top talent.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"299 ","pages":"Article 129971"},"PeriodicalIF":7.5,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334705","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