Expert Systems with Applications最新文献

{"title":"SD-MIL: Multiple instance learning with dual perception of scale and distance information fusion for whole slide image classification","authors":"Yining Xie ,&nbsp;Zequn Liu ,&nbsp;Jiajun Chen ,&nbsp;Wei Zhang ,&nbsp;Jing Zhao ,&nbsp;Jiayi Ma","doi":"10.1016/j.eswa.2025.126831","DOIUrl":"10.1016/j.eswa.2025.126831","url":null,"abstract":"<div><div>In computer-aided pathology diagnosis, multiple instance learning (MIL) has become a key method for addressing disease diagnosis problems in whole slide images (WSIs). However, current MIL models have limitations in capturing dependencies among instances and local contextual information. Additionally, the imbalance in the number of positive and negative instances affects MIL models’ ability to identify important instances. To address these issues, we propose a dual perception of scale and distance information fusion method (SD-MIL). SD-MIL consists of two parts: multi-scale window regional self-attention (MWRSA) and adaptive prototype distance-guided instance feature enhancement (PGFE). MWRSA utilizes three different-sized windows to compute regional multi-head self-attention (R-MSA) obtaining scale-aware instance features. This part explores instance long-range dependencies in local region and capture local contextual information at different scales. In the PGFE part, the distance parameter between instances and bag-level prototype is considered to assign different significance weights to instances resulting in distance-aware instance features, which guides model better focus on important instances. Then, learnable parameters optimize the fusion of scale-aware and distance-aware instance features, enhancing instance feature representation and ensuring the downstream aggregation model to generate high-quality bag features. Experimental results on three datasets show that SD-MIL outperforms state-of-the-art MIL methods. Meanwhile, SD-MIL consistently delivers performance improvements when the feature extraction network or downstream aggregation model is replaced.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126831"},"PeriodicalIF":7.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446055","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":"High-Precision machining energy consumption prediction based on multi-sensor data fusion and Ns-Transformer network","authors":"Meihang Zhang ,&nbsp;Hua Zhang ,&nbsp;Wei Yan ,&nbsp;Zhigang Jiang ,&nbsp;Rui Tian","doi":"10.1016/j.eswa.2025.126903","DOIUrl":"10.1016/j.eswa.2025.126903","url":null,"abstract":"<div><div>Achieving energy efficiency and cost-effectiveness in machining relies on accurate predictions of energy consumption. Despite the advancements in deep learning for predictive<!--> <!-->applications, precise energy modeling through multi-sensor data integration remains challenging, particularly due to the computational demands of large datasets. To address this, an Ns-Transformer-based strategy leveraging multi-sensor data fusion for high-precision energy consumption prediction is proposed. The methodology begins with data preprocessing, incorporating Lagrange interpolation, Butterworth filtering, principal component analysis, and correlation analysis to identify critical features. Key time-series features are then fused with energy consumption data to create an enriched feature space. The fused features are subjected to feature learning through a dual-layer Ns-Transformer network, followed by the application of linear regression to map the energy consumption state, thereby ensuring prediction accuracy. The framework employs distinct models for training and prediction, sharing parameters to reduce computational overhead. Experimental results demonstrate significant accuracy improvements, with mean squared error reductions exceeding 76% for carbon fiber and surpassing 83.2% for plastics, aluminum, and steel.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126903"},"PeriodicalIF":7.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453663","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":"XLight: An interpretable multi-agent reinforcement learning approach for traffic signal control","authors":"Sibin Cai ,&nbsp;Jie Fang ,&nbsp;Mengyun Xu","doi":"10.1016/j.eswa.2025.126938","DOIUrl":"10.1016/j.eswa.2025.126938","url":null,"abstract":"<div><div>Recently, deep reinforcement learning (DRL)-based traffic signal control (TSC) methods have garnered significant attention among researchers, achieving substantial progress. However, current research often focuses on performance improvement, neglecting interpretability. DRL-based TSC methods often face challenges in interpretability. This limitation poses significant obstacles to practical deployment, given the liability and regulatory constraints faced by governmental authorities responsible for traffic management and control. On the other hand, interpretable RL-based TSC methods offer greater flexibility to meet specific requirements. For instance, prioritizing the clearance of vehicles in a particular movement can be easily achieved by assigning higher weights to the state variables associated with that movement. To address this issue, we propose <strong><em>Xlight</em></strong>, an interpretable multi-agent reinforcement learning (MARL) approach for TSC, which enhances interpretability in three key aspects: (a) meticulously designing and selecting the state space, action space, and reward function. Especially, we propose an interpretable reward function for network-wide TSC and prove that maximizing this reward is equivalent to minimizing the average travel time (ATT) in the road network; (b) introducing more practical regulatable (i.e., interpretable) functions as TSC controllers; and (c) employing maximum entropy policy optimization, which simultaneously enhances interpretability and improves transferability. Next, to better align with practical applications of network-wide TSC, we propose several interpretable MARL-based methods. Among these, Multi-Agent Regulatable Soft Actor-Critic (MARSAC) not only possesses interpretability but also achieves superior performance. Finally, comprehensive experiments conducted across various TSC scenarios, including isolated intersection, synthetic network-wide intersections, and real-world network-wide intersections, demonstrate the effectiveness. For example, in terms of the ATT metric, our proposed method achieves improvements of 9.55%, 34.17%, 3.98%, and 42.93% compared to the Actuated Traffic Signal Control (ATSC) across a synthetic road network and 3 real-world road networks. Furthermore, in the synthetic network, our method demonstrates improvements of 4.04% and 3.21% in the Safety Score and Fuel Consumption metrics, respectively, when compared to the ATSC.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126938"},"PeriodicalIF":7.5,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453669","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":"Research on global trajectory planning for UAV based on the information interaction and aging mechanism Wolfpack algorithm","authors":"Jinyu Zhang ,&nbsp;Xin Ning ,&nbsp;Shichao Ma ,&nbsp;Rugang Tang","doi":"10.1016/j.eswa.2025.126867","DOIUrl":"10.1016/j.eswa.2025.126867","url":null,"abstract":"<div><div>The planning of trajectories for multi-unmanned aerial vehicles (UAVs) has been a topic of intensive research in both military and civilian contexts. It is a crucial aspect of the overall intelligence capabilities of UAV formation systems. In order to enhance the capability of multi-UAVs autonomous trajectory planning and to facilitate attainment of optimal paths in mountainous environments, this paper proposes an information interaction and aging mechanism Wolfpack Algorithm (IIAM-WPA). Firstly, a mission environment model is established using digital elevation modelling technology to simulate the real mountainous environment. Secondly, a trajectory planning model is established by comprehensively considering the terrain, threats and formation security factors. Meanwhile, in order to comprehensively evaluate the planning results, a new composite objective function is proposed. The proposed IIAM-WPA method is finally employed to identify the optimal paths for multiple UAVs. The key improvements to the method are as follows: the initialization effect is enhanced by the Chebyshev chaotic mapping in initialization phase, thereby accelerating the convergence of the population. Furthermore, the aging mechanism of wolves is incorporated into the model to enhance the efficiency of wolf search. Meanwhile, communication between populations is augmented during the encirclement phase, which serves to enhance population diversity. Finally, a selective mutation mechanism is introduced to rescue the population from the local optimum trap. In order to ascertain the effectiveness of the proposed algorithm, the simulation results of UAV trajectory planning under different mission scenarios are presented and compared with various optimization techniques. The simulation results demonstrate that the maximum improvement rate of the proposed algorithm is 96.73% and 4.2% in single UAV and multi-UAV planning tasks, respectively. This further verifies the planning accuracy and efficiency of the IIAM-WPA method and effectively proves the effectiveness of the method in solving UAV trajectory planning problems.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126867"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438084","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 multi-task minutiae transformer network for fingerprint recognition of young children","authors":"Manhua Liu ,&nbsp;Aitong Liu ,&nbsp;Yelin Shi ,&nbsp;Shuxin Liu","doi":"10.1016/j.eswa.2025.126825","DOIUrl":"10.1016/j.eswa.2025.126825","url":null,"abstract":"<div><div>Fingerprint recognition of children have attracted increasing attention for real applications such as identity certificate. However, the recognition performance is greatly reduced if the existing systems are directly used on the fingerprints of young children due to their low resolution and poor image quality. Towards more accurate fingerprint recognition of young children, this paper proposes multi-task deep learning framework based on Pyramid Densely-connected U-shaped Swin-transformer network (PDUSwin-Net) to jointly learn the reconstruction of enhanced high-resolution images and detection of minutiae points, which is compatible with existing adult fingerprint sensors (500 dpi) and minutiae matchers. First, a pyramid densely-connected U-shaped convolutional network is proposed to learn the features of fingerprints for multiple tasks. Then, a swin-transformer attention block is added to model the correlations of long-spatial features. In the decoding part, two branches are built for the tasks of fingerprint enhancement and minutiae extraction. Finally, our method is tested with the existing matchers on two independent fingerprint datasets of young children aged from 0–2 years. Results and comparison show that our method performs better than other methods for fingerprint recognition of young children.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126825"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446056","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":"Recommendation of TV programs via information filtering in RCA tripartite networks","authors":"Kean Li ,&nbsp;An Zeng ,&nbsp;Jianlin Zhou ,&nbsp;Yijun Chen ,&nbsp;Xiaohua Cui","doi":"10.1016/j.eswa.2025.126836","DOIUrl":"10.1016/j.eswa.2025.126836","url":null,"abstract":"<div><div>Television remains an indispensable medium for information and entertainment, even in the era of widespread streaming media. With the expansion of TV channels through set-top boxes, users now face an overwhelming variety of choices, leading to information overload problems. Recommendation systems have effectively solved the information overload problem and can thus be naturally applied to television. Prior research has focused on improvements in algorithms and the addition of other data. In this paper, without introducing external data, we generate recommendations based on 3 months of TV viewing data from a Chinese city. Considering the large amount of noisy data caused by short stays in TV programs, we simplify the original almost fully connected tripartite network by eliminating the insignificant links with the Revealed Comparative Advantage (RCA) metric to comprehensively reflect user preferences. The inclusion of channel nodes allows the network structure to better align with user behavior characteristics, which differs from traditional bipartite networks that only include user-program interactions. We examine data with different sparsity and find that our approach continues to outperform conventional bipartite network recommendations in terms of accuracy. The advantages of our approach have been validated through comparisons with other advanced methods and across different datasets. Overall, only based on viewing records of users, our work provides accurate TV program recommendations that can capture the underlying user behavior characteristics.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126836"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453667","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":"Stockformer: A price–volume factor stock selection model based on wavelet transform and multi-task self-attention networks","authors":"Bohan Ma ,&nbsp;Yushan Xue ,&nbsp;Yuan Lu,&nbsp;Jing Chen","doi":"10.1016/j.eswa.2025.126803","DOIUrl":"10.1016/j.eswa.2025.126803","url":null,"abstract":"<div><div>As the Chinese stock market continues to evolve and its market structure grows increasingly complex, traditional quantitative trading methods face escalating challenges. Due to policy uncertainty and frequent market fluctuations triggered by sudden economic events, existing models often struggle to predict market dynamics accurately. To address these challenges, this paper introduces “Stockformer,” a price–volume factor stock selection model that integrates wavelet transformation and a multitask self-attention network to enhance responsiveness and predictive accuracy regarding market instabilities. Through discrete wavelet transform, Stockformer decomposes stock returns into high and low frequencies, meticulously capturing long-term market trends and short-term fluctuations, including abrupt events. Moreover, the model incorporates a Dual-Frequency Spatiotemporal Encoder and graph embedding techniques to capture complex temporal and spatial relationships among stocks effectively. Employing a multitask learning strategy, it simultaneously predicts stock returns and directional trends. Experimental results show that Stockformer outperforms existing advanced methods on multiple real stock market datasets. In strategy backtesting, Stockformer consistently demonstrates exceptional stability and reliability across market conditions—whether rising, falling, or fluctuating—particularly maintaining high performance during downturns or volatile periods, indicating high adaptability to market fluctuations. To foster innovation and collaboration in the financial analysis sector, the Stockformer model’s code has been open-sourced and is available on the GitHub repository: <span><span>https://github.com/Eric991005/Multitask-Stockformer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126803"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428992","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":"Dynamic programming-based exact and heuristic algorithms for single machine scheduling with sequence-dependent setups","authors":"Tengmu Hu ,&nbsp;Shih-Hsien Tseng ,&nbsp;Theodore T. Allen","doi":"10.1016/j.eswa.2025.126866","DOIUrl":"10.1016/j.eswa.2025.126866","url":null,"abstract":"<div><div>This study presents a novel algorithmic framework and an inventory flow mixed integer programming formulation designed to minimize total tardiness and the number of setups. The approach decomposes the problem into three stages: intra-family scheduling, family sequence optimization, and family-switch timing. We propose a specialized heuristic with <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>5</mn></mrow></msup><mo>log</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span> complexity efficiently handles intra-family scheduling and is extended to accommodate subfamily groupings. Dynamic programming is employed for family-switch optimization, with state complexity constrained to <span><math><mrow><msup><mrow><mn>2</mn></mrow><mrow><mi>n</mi></mrow></msup><mo>+</mo><mn>1</mn></mrow></math></span>. In the last stage of algorithmic framework, we propose a branch-and-bound method to handle family-switch timing, utilizing lower bounds derived from the results of previous stages. Our overall proposed ”branch-and-bound-regulated dynamic programming (B&amp;B-DP)” algorithm excels in solving large-scale scheduling problems, demonstrating superior performance against four benchmark methods across 150 test cases. This algorithmic framework extends the capabilities of single-machine scheduling with family setup times to handle a large number of jobs. In our experiments, we show that the proposed algorithm reduces total tardiness by 10%–25% compared to other methods. This research not only advances the state of the art in single-machine scheduling but also provides a scalable and effective framework for addressing complex production scheduling challenges.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126866"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438082","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":"Maximizing data utility while preserving privacy through database fragmentation","authors":"Ali Amiri","doi":"10.1016/j.eswa.2025.126873","DOIUrl":"10.1016/j.eswa.2025.126873","url":null,"abstract":"<div><div>Efficiently managing databases that balance data privacy with utility is a critical challenge in today’s data-driven landscape. This study addresses the problem of database fragmentation, which involves dividing a database into smaller fragments, each containing a subset of attributes. The primary objective is to strike a balance between safeguarding the confidentiality of sensitive attribute sets and optimizing the database’s utility. Sensitive attribute sets include combinations of attributes that could disclose private information or identify individuals, such as personal quasi-identifiers, necessitating their separation into distinct fragments to reduce the risk of sensitive data exposure. Conversely, utility attribute sets consist of attributes that enhance data usability and query efficiency. Maximizing utility requires grouping attributes from the same utility set into as few fragments as possible. To effectively solve this complex NP-hard problem, A column generation-based solution leveraging a set partitioning formulation is presented. Experimental evaluations on real and synthetic datasets validate the efficiency of the proposed approach, demonstrating its superiority over the state-of-the-art commercial solver, CPLEX.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126873"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438160","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":"Development and validation of a human-machine interface for unmanned aerial vehicle (UAV) control via hand gesture teleoperation","authors":"Fevzi Çakmak Bolat ,&nbsp;Mustafa Cem Avci","doi":"10.1016/j.eswa.2025.126828","DOIUrl":"10.1016/j.eswa.2025.126828","url":null,"abstract":"<div><div>In this research, a drone-style unmanned aerial vehicle is maneuvered using hand gestures through the creation of a specialized glove design. The analytical formulas pertaining to the drone framework developed during the research were derived, leading to the establishment of a mathematical representation. These formulas were implemented in the Matlab &amp; Simulink environment, and simulations of the system based on this mathematical representation were conducted. Next, to carry out verification tests, a unique device was crafted and set up for the drone, enabling real-time data exchange with the glove. A series of distinct signal sets for the glove were examined to confirm the functionality of the system. After confirming the control mechanism, it was seamlessly incorporated into the electronic hardware framework, leveraging the Arduino Uno microcontroller as the focal point. Within the hand gesture apparatus, an innovative circuit was devised, managed by the Atmega328P microcontroller chip. The primary motivation behind this exploration resides in the desire to establish a user interface for UAV operators that is both seamless and unobtrusive, moving beyond the artificial and cumbersome elements tied to traditional control systems. For this purpose, the research aims to empower users to utilize hand gestures—frequently employed in various everyday scenarios—for piloting activities, thus improving user performance and simplicity of use. The findings of this study highlight the parity between the glove apparatus designed for hand gesture manipulation and the conventional joystick-based system, thereby confirming its effectiveness for multiple applications. Furthermore, a one-handed method was embraced for hand gesture control, with the supplementary aim of offering pilot training opportunities for individuals with upper limb impairments.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"273 ","pages":"Article 126828"},"PeriodicalIF":7.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428993","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}