Expert Systems with Applications最新文献

{"title":"Multi-Perspective Dialogue Non-Quota Selection with loss monitoring for dialogue state tracking","authors":"Jinyu Guo ,&nbsp;Zhaokun Wang ,&nbsp;Jingwen Pu ,&nbsp;Wenhong Tian ,&nbsp;Guiduo Duan ,&nbsp;Guangchun Luo","doi":"10.1016/j.eswa.2025.127516","DOIUrl":"10.1016/j.eswa.2025.127516","url":null,"abstract":"<div><div>The task-oriented dialogue system is dedicated to helping users achieve specific goals. Within this system, dialogue state tracking (DST) is the core module to obtain the key compressed information of the dialogue. In the field of DST, effective management of dialogue history assumes paramount importance. However, existing models consistently employ a uniform dialogue history throughout the process of tracking states, irrespective of the slot that is being updated. This may lead to insufficiency or redundancy of information for different slots. To address this issue, we introduce DNQS-DST, a novel approach intended for dynamically choosing the pertinent dialogue contents matching each slot to update the state. Our method operates by initially retrieving turn-level utterances from the dialogue history. Subsequently, it evaluates the relevance of these utterances to the target slot through a three-perspective evaluation process and then yields a non-quota dialogue selection. To fully exploit the effectiveness of the dialogue selection module, we also propose a Loss Monitoring-based Supervision Reinforcement module to achieve directly supervised training of the dialogue selection module without given labels. After that, only the chosen dialogue content is fed into the State Generator. Our proposed DNQS-DST, for the first time, breaks the limitation of using fixed dialogues when updating slots, which effectively filters out irrelevant information while preserving the integrity of the input data. In addition, the proposed supervision-strengthened module also offers a general solution for optimization schemes in all similar unlabeled stages. The experimental results demonstrate that this approach outperforms baseline models across mainstream datasets.<span><span>¹</span></span></div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"283 ","pages":"Article 127516"},"PeriodicalIF":7.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879429","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":"Learning adaptive detection and tracking collaborations with augmented UAV synthesis for accurate anti-UAV system","authors":"Shihan Liu ,&nbsp;Tianyang Xu ,&nbsp;Xue-Feng Zhu ,&nbsp;Xiao-Jun Wu ,&nbsp;Josef Kittler","doi":"10.1016/j.eswa.2025.127679","DOIUrl":"10.1016/j.eswa.2025.127679","url":null,"abstract":"<div><div>In recent years, Unmanned Aerial Vehicles (UAVs) have witnessed a significant use upsurge across various domains. However, their low cost and elusive size have raised significant security concerns. In particular, UAVs, imaged in the infrared mode, located in a cluttered background and being relatively small targets, pose formidable challenges. To this end, in this paper, we propose a novel approach for accurate UAVs perception. The core innovation lies in learning an adaptive detection and tracking collaboration mechanism, supported by a novel method of training data augmentation (ADTC). During the test phase, ADTC begins by leveraging the detector to identify the potential target candidates within the image frame. These candidates are then refined by an adaptive selection module, where a Kalman filter is deployed to model and predict the motion trajectory of each candidate. The best result of the predicted trajectories with the detected candidates is adopted as the output. The adaptive selection module filters out less confident objects, efficiently decreasing processing time. Furthermore, we construct a new dataset Anti-MUAV15 to evaluate the performance of ADTC in multiple-UAV scenarios. Our approach has been rigorously evaluated through qualitative and quantitative experiments on the Anti-UAV, AntiUAV600 and Anti-MUAV15 datasets. The experimental results demonstrate that our method outperforms state-of-the-art anti-UAV solutions in terms of robustness and precision, without imposing additional computational burden. The code and dataset are available at <span><span>https://github.com/Shihan0325/Anti-MUAV15</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127679"},"PeriodicalIF":7.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859341","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":"Real time brain stroke identification using face images based on machine learning and booby bird optimization","authors":"Alaa M. Mohamed ,&nbsp;Asmaa H. Rabie ,&nbsp;Hanan M. Amer ,&nbsp;Ahmed I. Saleh ,&nbsp;Mohy Eldin A. Abo-Elsoud","doi":"10.1016/j.eswa.2025.127719","DOIUrl":"10.1016/j.eswa.2025.127719","url":null,"abstract":"<div><div>Stroke one of the most common causes of death after heart disease and cancer and is the leading cause of severe long-term disability. The earlier a stroke is detected and the faster it is treated, the greater the chance of recovery. Therefore, early detection and treatment of strokes are essential to save lives and reduce permanent damage. In this paper, we present a stroke monitoring strategy based on face images for individuals who have a risk of stroke or those with chronic diseases. Patients are monitored using a smart camera located in their room. This camera sends data to a fog server in the hospital, where all processes are done there. The proposed strategy consists of three main stages, which are i) data preprocessing, ii) model training, and iii) model testing. In the data preprocessing stage, faces are extracted from images using Yolo v8. Then features are extracted using the Active Appearance Model (AAM) model and dlib library. The proposed Binary Booby Bird Optimization (B³O) is used as a new feature selection method to select only relevant features from data sets. In the model training stage, each feature is divided into ranges and these ranges are divided into regions to minimize the data set size for fast detection. Finally, the model testing stage tests all proposed stages to detect stroke patients using the Navie Bayes (NB) classifier. The experiment results show that the proposed B³O and proposed stroke monitoring strategy achieve high accuracy of 94.18% and 98.43%, respectively.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127719"},"PeriodicalIF":7.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851359","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":"Typical applications and perspectives of machine learning for advanced precision machining: A comprehensive review","authors":"Yiji Liang ,&nbsp;Canwen Dai ,&nbsp;Jingwei Wang ,&nbsp;Guoqing Zhang ,&nbsp;Suet To ,&nbsp;Zejia Zhao","doi":"10.1016/j.eswa.2025.127770","DOIUrl":"10.1016/j.eswa.2025.127770","url":null,"abstract":"<div><div>Advanced precision machining technologies, such as micro/ultraprecision mechanical machining and atomic and close-to-atomic scale manufacturing, are critical to high-value industries like aerospace and defense. However, extreme precision requirements and nonlinear dynamics pose significant challenges for accurate modeling, as traditional methods often struggle to capture intricate interactions and inherent variability. Machine learning emerges as a transformative solution, enabling data-driven modeling with unprecedented accuracy. This paper provides a comprehensive overview of the significant advancements and typical applications of machine learning in advanced precision machining, focusing on model architectures and methodologies to guide industrial implementation. For instance, this paper presents various examples, such as the application of LSTM networks in predicting tool life by capturing temporal dependencies in force signals, which illustrates how machine learning models are tailored to address specific challenges in precision machining. However, industrial adoption of machine learning remains hindered by limited datasets and computational constraints. This paper offers forward-looking recommendations to address these issues, integrating machine learning into precision machining within the framework of Industry 5.0 and providing robust support for the further promotion and application of machine learning in actual production environments. Furthermore, this research establishes a robust framework for recognizing similarities in machine learning applications across diverse machining domains, facilitating transfer learning among various advanced precision machining processes. By bridging the gap between theoretical models and industrial scalability, this review highlights the transformative role of machine learning in advanced precision machining toward intelligent, sustainable production, ultimately supporting high-performance component manufacturing.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"283 ","pages":"Article 127770"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879437","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":"CRC-MRC: Reader Comments Augmented Machine Reading Comprehension for social emotion prediction","authors":"Hengxin Gao ,&nbsp;Keyang Ding ,&nbsp;Qianlong Wang ,&nbsp;Bin Liang ,&nbsp;Ruifeng Xu","doi":"10.1016/j.eswa.2025.127336","DOIUrl":"10.1016/j.eswa.2025.127336","url":null,"abstract":"<div><div>The task of social emotion prediction aims to understand and predict the distribution of emotion that a text evokes in its readers. Previous research has primarily focused on modeling the textual representation of news while neglecting the human aspect of how news is read and the emotions it evokes. Thus, we utilize the Machine Reading Comprehension (MRC) framework to read articles like humans do. Additionally, previous studies have shown the significant help of integrating readers’ comments. However, in realistic scenarios, raw comments are not readily available and are often redundant and noisy. To address this, we suggest a clustering-based approach that utilizes LLMs for the automatic generation of comments, aiming to provide high-quality emotional information from the reader’s perspective. By integrating generated comments into the MRC framework, we propose a Clustering-based Reader Comments Augmented Machine Reading Comprehension framework (CRC-MRC) to comprehensively model the reading process from the readers’ perspective while browsing news and comments. Extensive tests on benchmark datasets demonstrate the high effectiveness of our proposed framework, surpassing current state-of-the-art methods.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127336"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874000","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":"IoT-enabled recurrent spatio-temporal adaptive attention of temporal convolutional transformer with continual learning for dairy farming","authors":"G. Anand Kumar ,&nbsp;B. Muni Lavanya ,&nbsp;Md. Khaja Mohiddin ,&nbsp;Sourabh Mitra","doi":"10.1016/j.eswa.2025.127712","DOIUrl":"10.1016/j.eswa.2025.127712","url":null,"abstract":"<div><div>Cattle health and behavior monitoring is critical in the maintenance of livestock welfare and efficient farm productivity. However, meaningful features for a health alert system are hard to extract from voluminous data generated by IoT sensors, which track various health and environmental parameters. In this paper, a new system along these lines for challenges as described above is presented: an advanced alert system using the Recurrent Spatio-Temporal Adaptive Attention of Temporal Convolutional Transformer (RecSTAA-TCT) model. The newly proposed model has integrated the following major components: a dynamic residual bidirectional gated recurrent unit, intensive spatial attention, and a Temporal Adaptive Temporal Convolutional Transformer module. This makes feature extraction with time series data rather challenging due to the complexity and variability of the data generated by IoT sensors. The Adaptive Residual Bi-GRU achieves this by efficiently handling the temporal dependencies to improve the robustness of the model in the presence of missing data and noise. Critical spatial features are then extracted by intensive spatial attention, hence allowing the system to focus attention only on the most informative data in a biometric and environmental database. The Temporal Adaptive TCT module refines further the model capability by extracting temporal features to make precise predictions that trigger the alert system in response to possible health and behavioral anomalies. It embeds continual learning into the model, through which it learns new patterns and data with time, giving back predictive accuracy and reliability to the model. It is based on this foundation that an integrated approach provides proactive management and timely interventions, hence substantially improving real-time anomaly detection over the traditional methods of monitoring. The proposed RecSTAA-TCT model is giving a classification accuracy of 96.5% and delivering alert notifications at a response time of 10.3 s.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127712"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859161","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":"Diffulex: Diffusion based lexically constrained text generation with mixed absorbing state and constraint balance","authors":"Fengrui Kang,&nbsp;Xianying Huang,&nbsp;Bingyu Li","doi":"10.1016/j.eswa.2025.127614","DOIUrl":"10.1016/j.eswa.2025.127614","url":null,"abstract":"<div><div>Lexically constrained text generation aims to generate complete text with given keywords, which can be applied in many fields, such as dialogue systems, automatic summarization, and story generation. However, the current methods often find it difficult to strike a balance between generation quality, constraint ability, and generation speed, and most of them can only focus on one aspect, which seriously limits their applications. To solve this problem, we propose Diffulex, a lexically constrained text generation model based on the diffusion model, which achieves faster generation speed and higher flexibility. In response to the characteristics of lexically constrained text generation tasks, Diffulex employs a forward process of mixed absorbing states, converting tokens into different types of [MASK] tags to capture the semantic relationship between constraints and tokens. Through the constraint balance of the reverse process, more attention will be paid to the prediction tokens that meet the constraint conditions and promote the dynamic fusion of the hidden state constraint information, achieving the balance between the generation quality and the constraint ability. We compared Diffulex with advanced work in the field and popular large language models as baselines, and our results on multiple datasets show that Diffulex outperforms the baseline in various aspects. Our code is available on <span><span>https://github.com/Kenfree0/Diffulex</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"281 ","pages":"Article 127614"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848627","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":"ShuiAttNet: Fully convolutional attention network for Shuishu character recognition","authors":"Xiaojun Bi ,&nbsp;Lu Han ,&nbsp;Weizheng Qiao","doi":"10.1016/j.eswa.2025.127613","DOIUrl":"10.1016/j.eswa.2025.127613","url":null,"abstract":"<div><div>Shuishu is one of the most representative hieroglyphs and precious cultural heritage in China, currently facing the risk of extinction. Preserving this endangered script requires innovative approaches to accurately recognize its characters. However, existing methods face significant challenges, including the inability to handle the broad diversity of Shuishu characters and the complexities of authentic ancient manuscripts. To address these issues, we present a comprehensive study that combines dataset construction and advanced deep learning methods. First, we establish the largest and most diverse Shuishu single-character dataset named S842 to date, addressing the critical lack of publicly available resources for Shuishu. Then we propose a novel Fully Convolutional Attention Network named ShuiAttNet, which is specifically designed for Shuishu character recognition. ShuiAttNet introduces two key innovations: the Attentional MBConv (AMC) block and the Fully Convolutional Attention (FCA) block. The AMC block utilizes a novel feature fusion mechanism to capture fine-grained local details while reducing feature redundancy caused by the low-rank characteristics of Shuishu characters. Meanwhile, the FCA block employs Depthwise Separable Dilated Convolution to establish long-range dependencies while preserving the two-dimensional spatial structure of the images. These components enable ShuiAttNet to achieve superior performance with significantly fewer parameters compared to existing methods. Extensive experiments validate the effectiveness and superiority of ShuiAttNet in both quantitative and qualitative assessments. Experimental results show that our proposed model achieves a Top-1 Acc of 97.04%, outperforming other state-of-the-art methods.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127613"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876889","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":"Domain knowledge integrated CAM system based on multi-objective path optimal planning and deep convolutional neural network","authors":"Cheng Guo ,&nbsp;Zexin Wang ,&nbsp;Kangsen Li ,&nbsp;Long Ye ,&nbsp;Nan Yu ,&nbsp;Feng Gong","doi":"10.1016/j.eswa.2025.127788","DOIUrl":"10.1016/j.eswa.2025.127788","url":null,"abstract":"<div><div>In the era of intelligent manufacturing, increasing consumers’ demands for customized products necessitates innovative approaches to design and processing efficiency. This research proposes an intelligent computer-aided manufacturing (CAM) system integrating domain knowledge, multi-objective optimization and deep convolutional neural networks (DCNNs). Using wire electrical discharge machining (WEDM) process as a case study, a multi-objective optimization model was developed to enhance machining quality, accuracy, and efficiency. A dataset of optimal machining paths and corresponding surface models was utilized to train the DCNN, enabling predictive path generation and real-time application. Comparative experiments between the optimized paths and traditional equidistant interpolation paths were conducted on a six-axis WEDM machine tool with constant RC power supply parameters. The machining efficiency finds an improvement of 18.83% to 40.7% on five randomly generated non-uniform rational B-splines (NURBS) free ruled surfaces. These findings underscore the high efficiency and practicality of the proposed system, advancing intelligent CAM solutions for complex manufacturing scenarios.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127788"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864813","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":"DeepHGNN: Study of graph neural network based forecasting methods for hierarchically related multivariate time series","authors":"Abishek Sriramulu ,&nbsp;Nicolas Fourrier ,&nbsp;Christoph Bergmeir","doi":"10.1016/j.eswa.2025.127658","DOIUrl":"10.1016/j.eswa.2025.127658","url":null,"abstract":"<div><div>Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN lies in its innovative graph-based hierarchical interpolation and an end-to-end reconciliation mechanism. This approach ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them, addressing a key challenge in hierarchical forecasting. A critical insight in hierarchical time series is the variance in forecastability across levels, with upper levels typically presenting more predictable components. DeepHGNN capitalizes on this insight by pooling and leveraging knowledge from all hierarchy levels, thereby enhancing the overall forecast accuracy. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN. This research not only demonstrates DeepHGNN’s effectiveness in achieving significantly improved forecast accuracy but also contributes to the understanding of graph-based methods in hierarchical time series forecasting.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"282 ","pages":"Article 127658"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869520","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}