Expert Systems with Applications最新文献

{"title":"A novel biologically plausible spiking convolutional capsule network with optimized batch normalization for EEG-based emotion recognition","authors":"Kun Chen ,&nbsp;Zihao Yang ,&nbsp;Mincheng Cai ,&nbsp;Quan Liu ,&nbsp;Qingsong Ai ,&nbsp;Li Ma","doi":"10.1016/j.eswa.2025.128183","DOIUrl":"10.1016/j.eswa.2025.128183","url":null,"abstract":"<div><div>Emotion recognition based on Electroencephalogram (EEG) is currently a hot topic in human-computer interaction, as EEG can more accurately reflect the characteristics of emotion. In recent years, EEG emotion recognition based on deep learning has achieved significant progress, and particularly methods combined with a capsule network (CapsNet) have outstanding performance. However, as the complexity of models continues to increase, resource consumption has also escalated. In this context, spiking neural networks (SNNs), known for their energy efficiency and greater biological plausibility, have attracted attentions of numerous researchers. Nevertheless, convolutional neural networks (CNNs) -based methods are not effective in SNN, and there remains a considerable gap compared with artificial neural networks (ANNs). This paper proposes a novel method combining the high-performing capsule networks with SNNs, named the spiking convolutional capsule network (SCCapsNet) for EEG-based emotion recognition tasks. To our knowledge, this is the first attempt to introduce capsule networks into SNNs for EEG emotion recognition. Furthermore, the spike-timing-dependent plasticity (STDP) routing algorithm is improved to sensitively capture temporal sequence information of EEG signals to enhance biological plausibility of SCCapsNet. In addition, a novel batch normalization (BN) layer incorporating the membrane potential decay time constant (tau-BN) is suggested to address the issue of neuron death caused by reduction in spike firing rate due to the <span><math><msub><mi>L</mi><mn>2</mn></msub></math></span> norm. We provide a theoretical explanation of the role of the BN layer in regulating spike firing rates. Finally, the performance of SCCapsNet is validated on two public datasets. As for DEAP dataset, SCCapsNet achieved recognition accuracies of 97.01 %, 96.84 %, and 96.73 % for valence, arousal, and dominance dimensions, respectively. The accuracies are 89.82 %, 93.69 %, and 93.90 % on the same dimensions for DREAMER dataset. A recognition accuracy of 90.32 % was achieved on the five-category dataset SEED-V. Experimental results outperform all other comparable SNN methods. In addition, we validated the enhancing effect of the proposed tau-BN on spike firing rates. The results showed that the enhancement effect was obvious, successfully addressing the issue of neuron death caused by excessively low spike firing rates due to the <span><math><msub><mi>L</mi><mn>2</mn></msub></math></span> norm. Our code is available at <span><span>https://github.com/Zihao0/SCCapsNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"288 ","pages":"Article 128183"},"PeriodicalIF":7.5,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124949","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}

{"title":"FMFI: Transformer based four branches multi-granularity feature integration for person Re-ID","authors":"Xiaohan Zheng,&nbsp;Jian Lu,&nbsp;Jiahui Xing,&nbsp;Kaibing Zhang","doi":"10.1016/j.eswa.2025.128150","DOIUrl":"10.1016/j.eswa.2025.128150","url":null,"abstract":"<div><div>Extracting multi-granularity features is a critical challenge in person re-identification (Re-ID). While convolutional neural networks (CNNs) are effectively capture local salient features through convolutional kernels, they struggle to construct globally discriminative representations. In contrast, Transformer networks can model long-range dependencies and establish global contextual relationships among features, making them a powerful tool for multi-granularity feature learning in Re-ID. To comprehensively extract multi-granularity features from various aspects such as clothing attributes, walking postures, and social interactions, we propose FMFI, a four-branch multi-granularity feature extraction and integration method based on Transformer. FMFI employs a four-branch architecture to capture diverse feature representations, enhancing the model’s expressiveness and robustness. Specifically, we introduce the Quadra-Net (QN) structure, which extends from the final layer of a replicated Transformer. By appropriately scaling the branch-wise feature weights and aggregating global tokens from all four branches, FMFI constructs enhanced global feature representations. Furthermore, we design the Refined Global Feature (RGF) module, which refines the initial global features and establishes connections with the newly integrated features, leading to more distinctive and discriminative global representations. Extensive experiments on the Market1501, CUHK03, and MSMT17 Re-ID datasets demonstrate that the proposed FMFI method outperforms most existing Re-ID approaches. Our model significantly enriches feature representations and improves the extraction of multi-granularity features, thereby enhancing person re-identification performance.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"288 ","pages":"Article 128150"},"PeriodicalIF":7.5,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131007","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}

{"title":"Deep Shallow network with LSTM for detecting attacks in IoT networks and preserving privacy based on Adaptive hybrid encryption algorithm","authors":"Deepak Dilip Mahajan ,&nbsp;A. Jeyasekar","doi":"10.1016/j.eswa.2025.128050","DOIUrl":"10.1016/j.eswa.2025.128050","url":null,"abstract":"<div><div>In this article, a new attack detection and privacy preservation framework is implemented to identify the attacks present in IoT networks and preserve the information from various attacks during transmission. Initially, the optimal features are selected from the garnered data by using the Adaptive Random Index-based Sea Lion Optimization Algorithm (ARI-SLOA). Consequently, the resultant features are given to a Deep Shallow Network with Long Short-Term Memory (DSN-LSTM) for attack identification. The attacks present in the IoT are mitigated during data transmission, and thus the data is highly secured. The designed encryption scheme is implemented for preserving the privacy of the information, where Adaptive Hybrid Attribute-based Encryption with an Advanced Encryption System (AHABE-AES) is utilized for privacy preservation. Here, the parameters of the AHABE-AES are optimized by the ARI-SLOA. The developed framework’s performance is examined with other existing approaches. From the results, the suggested framework obtained 95% accuracy, 8% FDR and 4% FNR rates. The outcomes obtained from the developed system ensure that this designed strategy is more robust and effective than other related models.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"288 ","pages":"Article 128050"},"PeriodicalIF":7.5,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125277","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}

{"title":"KFF: K-feature fusion token merging for vision transformer","authors":"Yu Yang ,&nbsp;Yue Zhou ,&nbsp;Xiaofang Hu ,&nbsp;Shukai Duan","doi":"10.1016/j.eswa.2025.128206","DOIUrl":"10.1016/j.eswa.2025.128206","url":null,"abstract":"<div><div>Recently, Vision Transformer (ViT) has achieved better performance than Convolutional Neural Networks (CNNs) in various vision applications. However, they are usually more computationally expensive than CNNs and face challenges in training and inference efficiency. Token merging is an effective and training-free way to reduce model complexity. However, since few tokens are exactly the same, prevalent similarity-based merging methods are challenging to avoid feature information loss and accuracy degradation. To address this issue, we propose a novel K-feature fusion token merging algorithm that significantly reduces the similarity metric error and token merging error with almost no accuracy loss. Specifically, we first reveal that similarity measurement errors and merging strategies have a significant impact on the performance of token merging algorithms, and the currently popular K-based similarity method will cause obvious feature shifts during the merging process. Based on this observation, we present a new feature-enhanced K-feature fusion token similarity calculation method. By combining the keys (K), which summarize the information contained in each token, and the more detailed intermediate features, the error of similarity measurement is greatly reduced. Then, we design a similarity-weighted average token merging algorithm to combine tokens that is faster and more accurate than ordinary average token merging. Extensive experiments show that our approach yields better model performance when reducing comparable computational effort and improving throughput without extra training. For example, for ViT-B on ImageNet, our method reduces 49.58 % of tokens and improves throughput by 30 % with only a 0.44 % drop in accuracy.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"288 ","pages":"Article 128206"},"PeriodicalIF":7.5,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138872","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}

{"title":"TIPS: A text interaction evaluation metric for learning model interpretation","authors":"Zhenyu Nie ,&nbsp;Zheng Xiao ,&nbsp;Tao Wang ,&nbsp;Anthony Theodore Chronopoulos ,&nbsp;Răzvan Andonie ,&nbsp;Amir Mosavi","doi":"10.1016/j.eswa.2025.128184","DOIUrl":"10.1016/j.eswa.2025.128184","url":null,"abstract":"<div><div>Explaining the decision-making behavior of deep neural networks (DNNs) can increase their trustworthiness in real-world applications. For natural language processing (NLP) tasks, many existing interpretation methods split the text according to the interactions between words. Also, the evaluation of explanation capability focuses on justifying the importance of the divided text spans from the perspective of interaction contribution. However, the prior evaluations are misled by extra interactions, making the evaluation unable to acquire accurate interactions within the text spans. Besides, existing research considers only absolute interaction contribution, which causes the evaluation to underestimate the important text spans with lower absolute interaction contribution and to overestimate the unimportant text spans with higher absolute interaction contribution. In this work, we propose a metric called Text Interaction Proportional Score (TIPS) to evaluate faithful interpretation methods. More specifically, we use a pick scheme to acquire the interactions within the divided text span and eliminate the influence of the extra interactions. Meanwhile, we utilize the relative interaction contribution between the divided text span and whole text to measure the importance of the acquired interactions. The proposed metric is validated using two interpretation methods in explaining three neural text classifiers (LSTM, CNN and BERT) on six benchmark datasets. Experiments show that TIPS outperforms a baseline method in three ways consistently and significantly (i.e., acquiring interactions within the text span, measuring importance of interaction, and distinguishing the important and unimportant text spans).</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"287 ","pages":"Article 128184"},"PeriodicalIF":7.5,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131744","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}

{"title":"HyperSegmenter: Reappraising the potential of large kernel CNN architecture in efficient semantic segmentation","authors":"Shihao Wang ,&nbsp;Zhengxing Huang ,&nbsp;Xirali Ablat ,&nbsp;Alimjan Aysa ,&nbsp;Kurban Ubul","doi":"10.1016/j.eswa.2025.128221","DOIUrl":"10.1016/j.eswa.2025.128221","url":null,"abstract":"<div><div>Semantic segmentation aims to precisely delineate the semantic content of each pixel in images, providing profound comprehension and precise localization for diverse vision tasks. While the advent of the Vision Transformer architecture has significantly propelled the field forward, these approaches still encounter challenges such as local inductive bias and elevated time complexity stemming from self-attention mechanisms. Addressing these issues, this paper reassesses the convolutional neural network architecture. We introduce an efficient convolutional operator and establish the SCU module as foundational to alleviate constraints observed in current methodologies. Furthermore, to mitigate redundancy within decoder structures, we endeavored to redesign a ’Sandwich’ decoder integrating the LKD and AKConv modules, specifically designed for demanding semantic segmentation tasks. Our model, termed HyperSegmenter, endeavors to enhance both efficiency and adaptability. HyperSegmenter is categorized into four iterations: Tiny, Small, Base, and Large, and underwent rigorous evaluations across three benchmark datasets-ADE20K, Cityscape, and COCO-Stuff. Experimental outcomes demonstrate substantial performance gains, achieving respective accuracies of 52.23 %, 82.54 %, and 48.91 %. These results underscore its efficacy and applicability in intricate scenarios.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"287 ","pages":"Article 128221"},"PeriodicalIF":7.5,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131752","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}

{"title":"Bidirectional fusion for deep contrastive multi-view clustering","authors":"Yongbo Yu,&nbsp;Jie Wang,&nbsp;Weizhong Yu,&nbsp;Zihua Zhao,&nbsp;Zongcheng Miao,&nbsp;Feiping Nie","doi":"10.1016/j.eswa.2025.128193","DOIUrl":"10.1016/j.eswa.2025.128193","url":null,"abstract":"<div><div>In recent years, contrastive learning has found applications in multi-view clustering. Although these methods have achieved some performance improvements, they still suffer from the negative impact of incorrect contrastive pairs. Similar to many traditional multi-view clustering methods that focus solely on either similarity matrices or feature matrices, existing contrastive learning methods often emphasize learning from the perspective of feature matrices. This unidirectional approach limits the selection of high-quality contrastive samples. To address these challenges, we propose a novel bidirectional fusional deep contrastive multi-view clustering method (BFCMC). Specifically, BFCMC simultaneously focuses on similarity matrices and low-dimensional feature matrices to learn a clearer, ground truth-aligned unified affinity matrix. Employing this matrix to guide the selection of contrastive samples effectively addresses the issue of incorrect contrastive pairs. Building on this, we propose a bidirectional fusion contrastive learning strategy that incorporates intra-view modules to enhance feature discrimination and inter-view modules to ensure representation consistency. Extensive experiments on multiple real-world datasets demonstrate the superiority of BFCMC compared to state-of-the-art methods.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"287 ","pages":"Article 128193"},"PeriodicalIF":7.5,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131172","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}

{"title":"A denoising majority weighted minority oversampling technique for imbalanced classification","authors":"Fei Han ,&nbsp;Chuanzhen Wang ,&nbsp;Qinghua Ling ,&nbsp;Henry Han","doi":"10.1016/j.eswa.2025.128199","DOIUrl":"10.1016/j.eswa.2025.128199","url":null,"abstract":"<div><div>The Majority Weighted Minority Oversampling Technique (MWMOTE) is a prevalent approach for addressing imbalanced classification challenges. However, MWMOTE has difficulties in identifying noise, selects only minority class instances as reference instances, and interpolates within the range of 0 to 1. To overcome the limitations, this study presents the Denoising Majority Weighted Minority Oversampling Technique (DN-MWMOTE). This innovative technique introduces an adaptive noise removal strategy that optimizes noise processing by evaluating the impact of potential noise on classification outcomes. It also expands reference selection to include both majority and minority class instances, using instance weights and <em>k</em>-nearest neighbor instances. Furthermore, DN-MWMOTE generates synthetic instances grounded in an instance’s significance ratio and specific linear interpolation rules. Experimental results across a synthetic dataset and 12 benchmark datasets confirm that DN-MWMOTE consistently outperforms its traditional counterparts.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"288 ","pages":"Article 128199"},"PeriodicalIF":7.5,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138871","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}

{"title":"Auto-calibrated adaptive integrated AHRS/TAM system for orientation estimation of long-range AUVs","authors":"Hossein Nourmohammadi,&nbsp;Mohammadtaghi Sabet","doi":"10.1016/j.eswa.2025.128186","DOIUrl":"10.1016/j.eswa.2025.128186","url":null,"abstract":"<div><div>Accurate, reliable, and real-time orientation estimation is one of the crucial requirements for the safety, performance, and effectiveness of autonomous underwater vehicles (AUVs). Many capabilities in AUVs such as collision-avoidance, trajectory tracking, exploration, and cooperative mission rely heavily on the orientation information including attitude and heading angles. Electromagnetic signal attenuation in the underwater environments as well as time-growing error of the inertial navigation bring about substantial challenges in the orientation estimation of the AUVs. It is more crucial as we use low-cost sensors and technologies for long-term navigation, especially in long-range AUVs. Accordingly, this research is mainly devoted to present an appropriate attitude and heading reference system (AHRS) applied to long-term navigation of the underwater vehicles based on off-the-shelf components. Due to cost constraints, micro-electro mechanical system (MEMS)-grade inertial sensors are used as the inertial measurement unit (IMU) of the proposed navigation system. Considering the above challenges, an auto-calibrated adaptive algorithm is developed for orientation estimation based on decomposed back-stepping (DBS) magnetometer calibration and intelligent fuzzy integration. In the proposed DBS calibration, the accuracy of the traditional magnetic field calibration (MFC) is enhanced through a backward multi-step evaluation-based strategy. In order for better performance, vertical channel and horizontal plane components of the magnetic field vector are decomposed during the evaluation process. In the intelligent fuzzy integration scheme, a Mamdani-based fuzzy inference engine is developed to calculate the maneuvering level of the motion. Consequently, the state-estimation filter is adaptively tuned. The assessment of the proposed low-cost auto-tuned AHRS is conducted through real data obtained in several sea tests.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"288 ","pages":"Article 128186"},"PeriodicalIF":7.5,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138796","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}

{"title":"On the constrained online convex optimization with feedback delay","authors":"Heyan Huang,&nbsp;Ping Wu,&nbsp;Haolin Lu,&nbsp;Zhengyang Liu","doi":"10.1016/j.eswa.2025.127871","DOIUrl":"10.1016/j.eswa.2025.127871","url":null,"abstract":"<div><div>We investigate the problem of online convex optimization (OCO) under feedback delay, where feedback for a decision is received after a delay, and long-term constraints, where constraints can be violated in intermediate iterations but must be satisfied over the long run. Existing approaches are primarily limited to fixed delay settings and general convex loss functions. In this paper, we employ a stricter metric based on cumulative constraint violations. We first propose a novel algorithm tailored for the fixed <span><math><mi>d</mi></math></span>-slot delay setting, achieving a regret bound of <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msqrt><mrow><mi>d</mi><mi>T</mi></mrow></msqrt><mo>)</mo></mrow></mrow></math></span> and a cumulative constraint violation of <span><math><mi>O</mi></math></span> (<span><math><msup><mrow><mi>T</mi></mrow><mrow><mfrac><mrow><mn>1</mn></mrow><mrow><mn>4</mn></mrow></mfrac></mrow></msup></math></span>), demonstrating superior performance compared to existing results. Moreover, when the loss functions are strongly convex, the regret and violation bounds can be further reduced to <span><math><mi>O</mi></math></span> (<span><math><mrow><mi>d</mi><mo>ln</mo><mi>T</mi></mrow></math></span>) and <span><math><mi>O</mi></math></span> (<span><math><mrow><msqrt><mrow><mi>d</mi></mrow></msqrt><mo>ln</mo><mi>T</mi></mrow></math></span>), respectively. Additionally, we extend our algorithm to the more realistic re-indexed delay setting, achieving <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msqrt><mrow><mi>d</mi><mi>T</mi></mrow></msqrt><mo>)</mo></mrow></mrow></math></span> regret and <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>T</mi></mrow><mrow><mfrac><mrow><mn>1</mn></mrow><mrow><mn>4</mn></mrow></mfrac></mrow></msup><mo>)</mo></mrow></mrow></math></span> cumulative constraint violation. Under strong convexity, these bounds are further improved to <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mover><mrow><mi>d</mi></mrow><mrow><mo>ˆ</mo></mrow></mover><mo>ln</mo><mi>T</mi><mo>)</mo></mrow></mrow></math></span> and <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msqrt><mrow><mover><mrow><mi>d</mi></mrow><mrow><mo>ˆ</mo></mrow></mover></mrow></msqrt><mo>ln</mo><mi>T</mi><mo>)</mo></mrow></mrow></math></span>, where <span><math><mrow><mover><mrow><mi>d</mi></mrow><mrow><mo>ˆ</mo></mrow></mover><mo>=</mo><msub><mrow><mo>max</mo></mrow><mrow><mi>t</mi><mo>∈</mo><mrow><mo>[</mo><mi>T</mi><mo>]</mo></mrow></mrow></msub><msub><mrow><mi>d</mi></mrow><mrow><mi>t</mi></mrow></msub></mrow></math></span> denotes the maximum delay.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"287 ","pages":"Article 127871"},"PeriodicalIF":7.5,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084273","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}