Expert Systems with Applications最新文献

{"title":"ATSDPC: Adaptive two-stage density peaks clustering with hybrid distance based on dispersion coefficient","authors":"Shengqiang Han,&nbsp;Xue Zhang,&nbsp;Xiyu Liu,&nbsp;Yuyan Zheng,&nbsp;Jianhua Qu","doi":"10.1016/j.eswa.2025.127639","DOIUrl":"10.1016/j.eswa.2025.127639","url":null,"abstract":"<div><div>Density peaks clustering (DPC) is a simple and effective clustering algorithm. However, the performance of DPC is greatly impacted by the chain reaction caused by its tree-liked allocation strategy. To overcome this issue, this study proposes an adaptive two-stage density peaks clustering algorithm with hybrid distance based on dispersion coefficient (ATSDPC). First, the Euclidean distance is replaced by the dispersion distance by the dispersion coefficient weighting the Euclidean distance. The dispersion distance outperforms the Euclidean distance because the dispersion coefficient can reflect the importances (contributions) of different attributes for (to) the clustering performance. Then, the hybrid distance combined of local and global distances, that can adaptively adjust by employing the overlapping coefficient, is proposed to overcome chain reaction by reducing the number of misallocated objects caused by chain reaction. Finally, the allocation strategy of DPC is improved by two-stage allocation strategy. In Stage 1, the initial clustering results are obtained with the dispersion distance instead of the Euclidean distance. In Stage 2, the clustering results obtained by Stage 1 as inputs are iteratively optimized by using the adjustable hybrid distance until the overlapping coefficient stabilize. Experimental results on synthetic and real world datasets demonstrate the superiority of ATSDPC over state-of-the-art baselines. ATSDPC can overcome chain reaction by reducing the number of misallocated objects caused by chain reaction with no destruction of the ability of detecting arbitrary cluster shapes. The project of ATSDPC will be available at <span><span>https://github.com/Hankbet/ATSDPC</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127639"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864691","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 novel three-way decision utility model considering decision-maker’s attribute preferences","authors":"Yiping Zhang ,&nbsp;Chunfang Chen ,&nbsp;Xiangjun Li","doi":"10.1016/j.eswa.2025.127506","DOIUrl":"10.1016/j.eswa.2025.127506","url":null,"abstract":"<div><div>In the study of Multi-Attribute Group Decision Making (MAGDM), determining the weights of decision-makers is a core aspect, as it directly affects the fairness and rationality of decision outcomes. The varying risk preferences of decision-makers also significantly impact the final decision. The Three-way Decision (TWD) utility model, a powerful tool for dealing with uncertainty and fuzziness, has shown its unique advantages in solving classification problems, primarily considering conditional probability and utility functions. This paper first proposes an optimization model for updating decision-makers’ weights based on their objective weights and subjective information. The Newton iteration method is employed to find the optimal solution iteratively. Subsequently, a method for determining conditional attributes using grey relational analysis (GRA) is introduced. And a new utility function is developed to reflect the individual risk attitudes and attribute preferences of decision-makers. Based on these developments, the paper constructs a new TWD utility model tailored for MAGDM. The paper demonstrates the model’s effectiveness and superiority through a case study involving real-world data. By comparing it with other methods, the model’s advantages are highlighted. A sensitivity analysis of the model’s parameters is conducted to test its robustness to changes, and the model is applied to large datasets, validating its potential and applicability in practical applications.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127506"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869638","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":"IllumiNet: A two-stage model for effective flare removal and light enhancement under complex lighting conditions","authors":"Lizhi Xu ,&nbsp;Liqiang Zhu ,&nbsp;Yaodong Wang ,&nbsp;Yao Wang","doi":"10.1016/j.eswa.2025.127638","DOIUrl":"10.1016/j.eswa.2025.127638","url":null,"abstract":"<div><div>Images captured under challenging lighting conditions suffer from both uneven illumination and flare artifacts (e.g., glare, streaks, and shimmer). Existing image enhancement methods mainly focus on either enhancing low-light regions or removing flares, but rarely address both issues simultaneously. When these methods are applied in sequence, they inevitably lead to over-enhancement and saturation in bright regions affected by flares or insufficient enhancement in low-light areas. In this article, we introduce a neural network model – IllumiNet, to enhance images captured under complex lighting conditions. Specifically, we propose a two-stage pixel-to-pixel generative model, that achieves both image flare removal and image light enhancement. Each stage is structured based on the U-Net model with the Mix Vision Transformer as the backbone, which is shared by the two stages to support domain knowledge interaction between the two tasks while helping to reduce model size. Additionally, we introduce a training strategy that combines knowledge distillation with simulated data generation, eliminating the need for real-paired datasets. Experiments across various benchmarks demonstrate that IllumiNet produces visually appealing enhanced images and exhibits robust generalization to diverse real-world scenarios.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127638"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864796","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":"Att-Next for skin lesion segmentation with topological awareness","authors":"C. Katar ,&nbsp;O.B. Eryilmaz ,&nbsp;E.M. Eksioglu","doi":"10.1016/j.eswa.2025.127637","DOIUrl":"10.1016/j.eswa.2025.127637","url":null,"abstract":"<div><div>Skin lesion segmentation is crucial for the early detection and accurate diagnosis of dermatological conditions, as precise boundary delineation enables better identification of lesion features. While Convolutional Neural Networks (CNNs) and hybrid CNN-Attention models have achieved notable success in this task, they often struggle to segment fine-grained lesion boundaries and suppress irrelevant tumor-like artifacts. They also tend to neglect topological features, which are crucial for accurately identifying complex lesions. To address these limitations, we propose a novel hybrid model that integrates ConvNeXt blocks with self-attention mechanisms. The model is also enhanced by a topological loss combined with Binary Cross Entropy (BCE) loss. This approach enables the model to better capture both local and global context, accurately delineate lesion boundaries, and suppress irrelevant regions, all without relying on a pre-trained backbone. Our method is evaluated on four publicly available skin lesion datasets: ISIC 2016, ISIC 2018, HAM10000, and PH2. Performance is assessed using segmentation metrics such as the Dice coefficient and Jaccard index. Experimental results demonstrate that the proposed model outperforms state-of-the-art (SOTA) methods, including MISSFormer, Swin-UNet, LeViT-UNet, FAT-Net, Att-UNet, DoubleU-Net, DeepLabV3 and TransUNet. Notably, the model achieves a Jaccard index of 0.8529 and a Dice coefficient of 0.913 on the ISIC 2018 dataset, surpassing the performance of given SOTA models in boundary delineation and tumor-like region suppression. These results highlight the potential of our hybrid ConvNeXt-Attention model with topological loss to improve lesion segmentation accuracy, which would lead to more effective and precise dermatological diagnoses.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127637"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864799","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":"CoCNet: A Chain-of-Clues framework for zero-shot referring expression comprehension","authors":"Xuanyu Zhou ,&nbsp;Simin Zhang ,&nbsp;Zengcan Xue ,&nbsp;Xiao Lu ,&nbsp;Tianxing Xiao ,&nbsp;Lianhua Wu ,&nbsp;Lin Liu ,&nbsp;Xuan Li","doi":"10.1016/j.eswa.2025.127633","DOIUrl":"10.1016/j.eswa.2025.127633","url":null,"abstract":"<div><div>Zero-shot learning enables the reference expression comprehension (REC) model to adapt to a wide range of visual domains without training. However, the ambiguity of linguistic expression leads to the lack of a clear subject. Moreover, existing methods have not fully utilized the visual context and spatial information, resulting in low accuracy and robustness in complex scenes. To address these problems, we propose a Chain-of-Clues framework (CoCNet) to exploit multiple clues for zero-shot REC task to solve the inference confusion step by step. First, <strong>the subject clue module</strong> employs the strong ability of large language models (LLMs) to reason about the category in expression, which enhances the clarity of linguistic expression. In <strong>the attribute clue module</strong>, we propose the dual-track scoring which highlights the proposal by blurring its surroundings and enhances contextual sensitivity by blurring the proposal. Additionally, <strong>the spatial clue module</strong> utilizes a series of Gaussian-based soft heuristic rules to model the location words and the spatial relationship of the image. Experimental results show that CoCNet exhibits strong generalization capabilities in complex scenes. It significantly outperforms previous state-of-the-art zero-shot methods on RefCOCO, RefCOCO+, RefCOCOg, Flickr-Split-0 and Flickr-Split-1. Our code is released at <span><span>https://github.com/CoCNetHub/CoCNet-main</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127633"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869639","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":"End-to-end multi-scale attention convolutional recurrent network for online handwritten Chinese text recognition","authors":"Xiwen Qu ,&nbsp;Zhihong Wu","doi":"10.1016/j.eswa.2025.127626","DOIUrl":"10.1016/j.eswa.2025.127626","url":null,"abstract":"<div><div>Nowadays, for Online handwritten Chinese text recognition (OHCTR), convolutional recurrent network (CRN) based models have achieved excellent recognition performance. However, existing CRN methods cannot directly process chronological sequence coordinates of online handwritten Chinese text lines, overlook multi-scale local semantic context, and fail to capture multi-level dependencies between characters. To address the above issues and further improve recognition performance, this paper proposes an end-to-end multi-scale attention convolutional recurrent network (EMACRN) for OHCTR. Concretely, this study proposes an end-to-end multi-scale attention convolutional neural network to directly extract multi-scale local contextual features from original chronological sequence coordinates. Then, bidirectional long short-term memory (BiLSTM) is used to capture the correlation between the multi-scale local contextual features and obtain the temporal sequence features of the local context features. After BiLSTM, multi-head attention is employed to weigh the outputs of BiLSTM. Finally, focal connectionist temporal classification (FCTC) is utilized to make predictions. Experiments on three public datasets demonstrate that EMACRN obtains a higher accuracy rate (<span><math><mrow><mi>A</mi><mi>R</mi></mrow></math></span>) and correct rate (<span><math><mrow><mi>C</mi><mi>R</mi></mrow></math></span>) with faster computation speed and less storage cost compared with the state-of-the-art algorithms on OHCTR.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127626"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859163","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":"Optimizing task offloading in IIoT via intelligent resource allocation and profit maximization in fog computing","authors":"Chia-Cheng Hu","doi":"10.1016/j.eswa.2025.127810","DOIUrl":"10.1016/j.eswa.2025.127810","url":null,"abstract":"<div><div>The rapid growth of Internet of Things (IoT) technology has revolutionized industrial and manufacturing sectors, with the Industrial Internet of Things (IIoT) playing a central role in enhancing operational efficiency. However, IIoT applications are challenged by limited computational and power resources, which impact the Quality of Service (QoS) requirements. While cloud computing alleviates some of these challenges, it introduces latency and server overload, leading to delays in task processing. Fog computing offers a promising solution by reducing latency and deploying computationally capable nodes at the network edge.</div><div>This paper proposes a novel framework for optimizing task offloading in IIoT environments by focusing on intelligent resource allocation and profit maximization within a fog computing architecture. Unlike traditional methods, our approach integrates a unified cost function that simultaneously addresses task delay and energy consumption, improving efficiency by balancing these conflicting objectives. We present an Integer Linear Programming (ILP) model that minimizes the total offloading cost while adhering to strict power and resource constraints. To handle the NP-hard nature of ILP problems, we introduce a computationally efficient approximation method based on rounding techniques, achieving near-optimal solutions without excessive computational overhead.</div><div>A key novelty of our work is the inclusion of profit maximization for IIoT application providers, which is often overlooked in existing solutions. We develop a second ILP model specifically for profit optimization, supported by an efficient solution method. Additionally, we propose a strategic resource expansion algorithm that adapts to insufficient system resources, ensuring the alignment of available resources with application demands. Our simulations demonstrate the practical impact of this approach, showcasing significant improvements in task processing time and energy efficiency, as well as optimizing profitability in real-world IIoT applications.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127810"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874001","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":"Investigations into deep Reinforcement Learning for wind farm set-point optimisation","authors":"Helen Sheehan ,&nbsp;Daniel Poole ,&nbsp;Telmo Silva Filho ,&nbsp;Ervin Bossanyi ,&nbsp;Lars Landberg","doi":"10.1016/j.eswa.2025.127627","DOIUrl":"10.1016/j.eswa.2025.127627","url":null,"abstract":"<div><div>Wake steering is a form of wind farm flow control in which upstream turbines are deliberately yawed to misalign with the incoming wind in order to reduce the impact of wakes on downstream turbines. This technique can give a net increase in the power generated by an array of turbines compared to standard greedy control where each turbine acts for its own benefit by aligning with the incoming wind. However, optimising the set-points of multiple turbines under varying wind conditions can be prohibitively complex for traditional, white-box models. Reinforcement Learning (RL) agents learn optimal long-term behaviours through “trial-and-error”, making them suited to controlling arrays of wind turbines under changing wind conditions for maximum farm power. Related works applying RL to this problem have tended to concentrate on either single wind directions or ranges up to around <span><math><mrow><mo>±</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>∘</mo></mrow></msup></mrow></math></span>. Here, the Deep Deterministic Policy Gradient algorithm has been used to train RL agents to control a nine-turbine array to implement wake steering under multiple wind directions between <span><math><mrow><mo>±</mo><mn>4</mn><msup><mrow><mn>5</mn></mrow><mrow><mo>∘</mo></mrow></msup></mrow></math></span>. While the agents were trained on steady-state (time-averaged) wind flow data, the performance of the final agent was tested on “quasi-dynamic” wind flow with varying wind direction. Under these conditions, the final agent achieved an average of 7% more power than greedy control per direction. This agent was then used to control the wind farm under a smaller subset of directions including many not seen during training, gaining on average 17% additional farm power per direction compared to greedy control.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127627"},"PeriodicalIF":7.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876885","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":"Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method","authors":"Yuchao Wang ,&nbsp;Kelin Tong ,&nbsp;Chunhai Fu ,&nbsp;Yuhang Wang ,&nbsp;Qiuhua Li ,&nbsp;Xingni Wang ,&nbsp;Yunzhe He ,&nbsp;Lijia Xu","doi":"10.1016/j.eswa.2025.127808","DOIUrl":"10.1016/j.eswa.2025.127808","url":null,"abstract":"<div><div>Traditional path planning algorithms still face significant challenges in large-scale scenarios with high-density irregular obstacles, such as low search efficiency, limited obstacle avoidance capabilities, and a tendency to get trapped in local optimum. To overcome these challenges, a hybrid route planning algorithm combining the Modified Golden Jackal Optimization (MGJO) algorithm and the Improved Dynamic Window Approach (IDWA) is proposed. To resolve the issue of getting trapped in local optimum and enhance global search efficiency in global path planning, the MGJO algorithm is synthesized based on nonlinear energy attenuation, diverse search strategies, and a guiding mechanism inspired by African vultures. To improve obstacle avoidance efficiency and ensure smoother local paths, the IDWA algorithm is redesigned by optimizing the obstacle distance evaluation function. In global path planning, the MGJO algorithm is evaluated against some state-of-the-art optimizers on 23 benchmark functions. In three different environments, the average path length of the MGJO algorithm over the original algorithm is improved by 10.76%, 16.72% and 25.46%. In local path planning experiments for mobile robots, the IDWA algorithm avoids the local optimum in small and medium-sized maps. In large maps, it significantly reduces the number of the local optimum occurrences, from 6 times to 2 times. The feasibility of the algorithm is validated in real-world experiments.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127808"},"PeriodicalIF":7.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859162","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":"Predicting wind turbines faults using Multi-Objective Genetic Programming","authors":"Marwa Daaji ,&nbsp;Mohamed-Amin Benatia ,&nbsp;Ali Ouni ,&nbsp;Mohamed Mohsen Gammoudi","doi":"10.1016/j.eswa.2025.127487","DOIUrl":"10.1016/j.eswa.2025.127487","url":null,"abstract":"<div><div>Wind turbines are a key component of renewable energy, converting wind into electricity with minimal environmental impact. Ensuring their continuous operation is crucial for maximizing energy production and reducing costly downtimes. To extend their operational lifespan, proactive maintenance strategies that predict and address potential faults are essential. While Machine Learning (ML) and Deep Learning (DL) algorithms have demonstrated significant promise in detecting wind turbine faults, they often prioritize maximizing the detection of failures without giving sufficient attention to false alarms. In practice, false alarms are just as problematic as undetected failures, as they reduce efficiency and waste resources. In this paper, we propose a novel optimization approach using Multi-Objective Genetic Programming (MOGP) to predict wind turbine faults. Our approach seeks to identify the best combination of features and their threshold values by optimizing two conflicting objectives: maximizing fault detection while minimizing false alarms. This dual-objective strategy ensures reliable fault prediction while minimizing unnecessary maintenance actions. We assess the effectiveness of our approach using real-world Supervisory Control and Data Acquisition (SCADA) data from a wind turbine in southern Ireland. The results demonstrate the efficiency of our approach in fault identification, achieving a competitive balance between recall (91%) and false positive rate (21%). While machine learning (ML), specifically Random Forest (RF), shows promising performance with a recall of 91% and a 10% false alarm rate, it remains a black-box model. RF lacks interpretability, making it challenging to extract meaningful insights into the relationships between sensor features and fault occurrences.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"281 ","pages":"Article 127487"},"PeriodicalIF":7.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842931","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}