Expert Systems with Applications最新文献

{"title":"Competitive multi-task Bayesian optimization with an application in hyperparameter tuning of additive manufacturing","authors":"Songhao Wang ,&nbsp;Weiming Ou ,&nbsp;Zhihao Liu ,&nbsp;Bo Du ,&nbsp;Rui Wang","doi":"10.1016/j.eswa.2024.125618","DOIUrl":"10.1016/j.eswa.2024.125618","url":null,"abstract":"<div><div>Multi-task Bayesian optimization is an effective approach for optimization involving multiple correlated tasks. Typically, either all the tasks or one primary task should be optimized, depending on the objectives of the problems. We consider optimizing the primary task without explicitly pre-determining which is the primary task. Instead, the primary task is defined as the task whose optimal value is the best among all tasks. Due to the black-box nature of the tasks, the decision makers are not able to identify the primary task beforehand. It is thus critical for the algorithms to recognize and optimize the true primary task. Such problems are called competitive multi-task problems and arise in areas including machine learning and engineering design. In this work, we propose a competitive multi-task Bayesian optimization (CMTBO) algorithm to solve competitive multi-task problems. It selects the query point as well as the task to query in each optimization iteration. We theoretically analyze the regret bounds for the algorithm and test their performances on several synthetic and real-world problems. In addition, our algorithm is applied to a material extrusion (an important technology in additive manufacturing) problem to tune the process parameters and select material types.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125618"},"PeriodicalIF":7.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586043","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":"LMSFF: Lightweight multi-scale feature fusion network for image recognition under resource-constrained environments","authors":"Yuchen Liu ,&nbsp;Hu Liang ,&nbsp;Shengrong Zhao","doi":"10.1016/j.eswa.2024.125584","DOIUrl":"10.1016/j.eswa.2024.125584","url":null,"abstract":"<div><div>In many resource-constrained environments, recognition tasks often require efficient and fast execution. Currently, many methods designed for this field adopt a combination of convolutional operations and Vision Transformers (ViTs) to achieve comprehensive feature representation while maintaining efficient performance. However, these methods still have higher parameter counts or floating point operations (FLOPs), making it difficult to adapt more resource-constrained environments. Therefore, a lightweight Multi-Scale Feature Fusion Network (LMSFF) is proposed to address this issue. The proposed method mainly consists of three modules: lightweight local processing (LLP) modules, local–global fusion modules (LGFM), and lightweight information fusion (LIF) modules. The LLP modules, considering the issue of computational redundancy, propose a branch structure that effectively reduces parameter consumption while maintaining high performance. To capture more comprehensive contextual information, the LGFM fuses local and global features, thus enhancing the comprehensive representation of image features. The LIF extracts crucial features through pooling operations at different scales while preserving lightweight characteristics. Additionally, to enhance the model’s generalization, a new weighted loss function is introduced, which alleviates the long-tail distribution issue in real-world scenarios and improves recognition performance for rare categories. Experimental results demonstrate that LMSFF achieves better balance between recognition accuracy and resource consumption compared with other state-of-the-art lightweight hybrid models.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125584"},"PeriodicalIF":7.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539026","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":"Unveiling image source: Instance-level camera device linking via context-aware deep Siamese network","authors":"Mingjie Zheng ,&nbsp;Ngai Fong Law ,&nbsp;Wan-Chi Siu","doi":"10.1016/j.eswa.2024.125617","DOIUrl":"10.1016/j.eswa.2024.125617","url":null,"abstract":"<div><div>Unveiling the source of an image is one of the most effective ways to validate the originality, authenticity, and reliability in the field of digital forensics. Source camera device identification can identify the specific camera device used to take a photo under investigation. While great progress has been made by the photo-response non-uniformity (PRNU)-based methods over the past decade, the challenge of instance-level source camera device linking, which verifies whether two images in question were captured by the same camera device, remains significant. This challenge is mainly due to the absence of auxiliary images to construct a clean camera fingerprint for each camera, particularly dealing with small image sizes. To overcome this limitation, in this paper, we formulate the task of source device linking as a binary classification problem and propose a simple yet effective framework based on a context-aware deep Siamese network. We take advantage of a Siamese architecture to extract the intrinsic camera device-related noise patterns from a pair of image patches in parallel for comparisons without any auxiliary images. Moreover, a recurrent criss-cross group is utilized to aggregate contextual information in the noise residual maps to alleviate the problem that PRNU noise maps are easily contaminated by the additive noises from image contents. For reliable device linking, we employ a patch-selection strategy on a pair of test images to adaptively choose suitable image patch pairs according to image contents. The final decision of a pair of test images is obtained from the average similarity score of the selected image patch pairs. Compared with existing state-of-the-art methods, our proposed framework can achieve better performance on both the tasks of source camera identification and source device linking without any prior knowledge, <em>i.e.</em>, reliable camera fingerprints, regardless of whether the camera devices are “seen” or “unseen” in the training stage. The experimental results on two standard image forensic datasets demonstrate that the proposed method not only shows robustness with respect to different image patch sizes and image quality degenerations, but also has a generalization ability across digital camera and smartphone devices.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125617"},"PeriodicalIF":7.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662817","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":"An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming","authors":"Yarong Chen,&nbsp;Jinhao Du,&nbsp;Jabir Mumtaz,&nbsp;Jingyan Zhong,&nbsp;Mudassar Rauf","doi":"10.1016/j.eswa.2024.125616","DOIUrl":"10.1016/j.eswa.2024.125616","url":null,"abstract":"<div><div>Efficient scheduling in flow shop environments with lot streaming remains a critical challenge in various industrial settings, necessitating innovative approaches to optimize production processes. This study investigates a hybrid flow shop scheduling problem dominant in real-world printed circuit board assembly shops. A novel multi-objective hyper-heuristic combining Q-learning, i.e., two-stage improved spider monkey optimization (TS-ISMO), is tailored to address the complexities of the flow shop scheduling problems. The proposed method aims to simultaneously optimize conflicting objectives such as minimizing makespan, total energy consumption, and total tardiness time while incorporating lot streaming considerations. For multi-objective hyper-heuristic techniques, the algorithm dynamically selects and adapts a diverse set of low-level heuristics to explore the solution space comprehensively and strike a balance among competing objectives. The proposed TS-ISMO algorithm incorporates several significant features aimed at enhancing its performance. These features encompass hybrid heuristics for solution initialization, a contribution value method for comprehensive convergence and diversity assessment, diverse evolutionary state judgments to promote the algorithm’s balance between exploration and exploitation capabilities, and a Q-learning strategy for self-adaptive parameter tuning. The integration of Q-learning facilitates intelligent parameter control, enabling the algorithm to autonomously adjust its behavior based on past experiences and evolution dynamics. This adaptive mechanism enhances convergence speed and solution quality by effectively guiding the search process toward promising regions of the solution space. Extensive computational experiments are conducted on benchmark instances of hybrid flow shop scheduling problems with lot streaming to evaluate the performance of the proposed algorithm. Comparative analyses against state-of-the-art approaches demonstrate its superior solution quality and computational efficiency.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125616"},"PeriodicalIF":7.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538955","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":"SF-GAN: Semantic fusion generative adversarial networks for text-to-image synthesis","authors":"Bing Yang ,&nbsp;Xueqin Xiang ,&nbsp;Wanzeng Kong ,&nbsp;Jianhai Zhang ,&nbsp;Jinliang Yao","doi":"10.1016/j.eswa.2024.125583","DOIUrl":"10.1016/j.eswa.2024.125583","url":null,"abstract":"<div><div>Text-to-image synthesis aims to generate high-quality realistic images conditioned on text description. The major challenge of this task rests on the deep and seamless integration of text and image features. Therefore, in this paper, we present a novel approach, e.g., semantic fusion generative adversarial networks (SF-GAN), for fine-grained text-to-image generation, which enables efficient semantic interactions. Specifically, our proposed SF-GAN leverages a novel recurrent semantic fusion network to seamlessly manipulate the global allocation of text information across discrete fusion blocks. Moreover, with the usage of the contrastive loss and the dynamic convolution, SF-GAN could fuse the text and image information more accurately and further improve the semantic consistency in the generate stage. During the discrimination stage, we introduce a word-level discriminator designed to offer the generator precise feedback pertaining to each individual word. When compared to current state-of-the-art techniques, our SF-GAN demonstrates remarkable efficiency in generating realistic and text-aligned images, outperforming its contemporaries on challenging benchmark datasets.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125583"},"PeriodicalIF":7.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539020","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":"Explainable artificial intelligence-based framework for efficient content placement in elastic optical networks","authors":"Róża Goścień","doi":"10.1016/j.eswa.2024.125541","DOIUrl":"10.1016/j.eswa.2024.125541","url":null,"abstract":"<div><div>The rapid development of telecommunication networks brings new optimization problems and the urgent need for dedicated and highly efficient solution methods. Recently, the idea of aiding network optimization with machine learning (<span>ml</span>) algorithms has gained more and more attention in the research society. Despite numerous successful applications of these methods, their adaption in real networks and systems is hindered due to the lack of a full explainability of their decisions and, in turn — the lack of trust. Hopefully, these aspects may be addressed by explainable artificial intelligence methods (<span>xai</span>). In this paper, we study an essential problem of the anycast content placement. Having a set of physical data centers (<span>dc</span>s) located in selected network nodes and a set of different contents (services), the task is to decide in which <span>dc</span>s place each of the contents in order to improve the optical network performance (measured as a bandwidth blocking probability (<span>bbp</span>)). To this end, we propose a dedicated <span>ml</span>-based framework, which approaches the placement problem as a supervised learning task of predicting network’s <span>bbp</span> for a content placement configuration. We perform extensive numerical experiments to tune the framework, considering five supervised learning algorithms and three comparison metrics. We also use explainable artificial intelligence methods to interpret the models’ decisions and draw general conclusions regarding beneficial content placement in a real network. Lastly, we compare the performance of the proposed <span>ml</span>-based placement framework with three reference methods. The results prove our approach’s extremely high efficiency, which reduced the <span>bbp</span> significantly compared to the best reference approach. Depending on the network settings and the offered traffic volume, the framework allowed to serve up to 47% of the traffic that would be rejected by the best reference method (corresponding to 3.76 Tbps of data).</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125541"},"PeriodicalIF":7.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538956","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 two-stage hybrid flow-shop formulation for sterilization processes in hospitals","authors":"Sebastian Kraul","doi":"10.1016/j.eswa.2024.125624","DOIUrl":"10.1016/j.eswa.2024.125624","url":null,"abstract":"<div><div>Sterile processing is a critical secondary process and a major cost factor in the processing, acquisition, and storage of costly medical devices. This article aims to improve the performance of sterile processing by developing, implementing, and evaluating a dispatching rule-based algorithm to reduce the time medical devices spend in the central sterile supply department using a two-stage hybrid flow-shop formulation. The algorithm combines dispatching rules with stage decomposition and compatibility conditions. A genetic algorithm is designed to benchmark the performance in addition to an analytic bound. Real-world data from a large German hospital were used to test the effectiveness of the heuristics. The case study demonstrated the practical implications of the approach, leading to a reduction in the time medical devices spend in the system and improved utilization of washer-disinfector machines and sterilizers. It also highlighted the importance of aligning machine capacity with demand and the potential trade-offs associated with batch processing decisions. Our approach can contribute to substantial operational cost savings and efficiency gains, offering significant benefits to decision makers at both the operational and tactical levels.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125624"},"PeriodicalIF":7.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial bee colony algorithm based on multiple indicators for many-objective optimization with irregular Pareto fronts","authors":"Hui Wang ,&nbsp;Dong Xiao ,&nbsp;Shahryar Rahnamayan ,&nbsp;Wei Li ,&nbsp;Jia Zhao","doi":"10.1016/j.eswa.2024.125613","DOIUrl":"10.1016/j.eswa.2024.125613","url":null,"abstract":"<div><div>Artificial bee colony (ABC) algorithm has shown excellent performance over many single and multi-objective optimization problems (MOPs). However, ABC encounters some difficulties when solving many-objective optimization problems (MaOPs) with irregular Pareto fronts (PFs). The possible reasons include two aspects: (1) there are many non-dominated solutions in the population and the low selection pressure cannot move the population toward the PF; and (2) it is hard to maintain population diversity for PFs having irregular geometric structures. To address these issues, a new many-objective ABC variant based on multiple indicators (called MIMaOABC) is proposed in this paper. Firstly, a convergence indicator <span><math><msub><mrow><mi>I</mi></mrow><mrow><msub><mrow><mi>ɛ</mi></mrow><mrow><mo>+</mo></mrow></msub></mrow></msub></math></span> and a diversity indicator (<span><math><mrow><mi>D</mi><mi>i</mi><mi>v</mi></mrow></math></span>) based on parallel distance are utilized. A single indicator may have preferences and it easily causes the population to converge to a subregion of the PF. Then, a two-stage environmental selection method is designed based on the two indicators. In the first stage, the <span><math><msub><mrow><mi>I</mi></mrow><mrow><msub><mrow><mi>ɛ</mi></mrow><mrow><mo>+</mo></mrow></msub></mrow></msub></math></span> based environmental selection is used to improve the convergence. In the second stage, the <span><math><mrow><mi>D</mi><mi>i</mi><mi>v</mi></mrow></math></span> based environmental selection is employed to maintain diversity and handle irregular PFs. To balance exploration and exploitation during the search, multiple search strategies are used in different search stages, respectively. In the onlooker bee stage, solutions with good convergence are chosen for further search based on a new selection mechanism. In order to verify the performance of MIMaOABC, a set of well-known benchmark problems with degenerate, discontinuous, inverted, and regular PFs are tested. Performance of MIMaOABC is compared with eight state-of-the-art algorithms. Computational results shows that the proposed MIMaOABC is competitive in solving MaOPs with both irregular and regular PFs.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125613"},"PeriodicalIF":7.5,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571808","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":"Industrial robot energy consumption model identification: A coupling model-driven and data-driven paradigm","authors":"Pei Jiang ,&nbsp;Jiajun Zheng ,&nbsp;Zuoxue Wang ,&nbsp;Yan Qin ,&nbsp;Xiaobin Li","doi":"10.1016/j.eswa.2024.125604","DOIUrl":"10.1016/j.eswa.2024.125604","url":null,"abstract":"<div><div>Due to wide distribution and low energy efficiency, the energy-saving in industrial robots (IRs) is attracting extensive attention. Accurate energy consumption (EC) models of IRs lay the foundation for energy-saving. However, most dynamic and electrical parameters of IRs are not disclosed by manufacturers, which leads to the invalidity of most model-based EC prediction methods. To bridge this gap, a mechanism-data hybrid-driven method is proposed to predict the EC of IRs in this paper. First, a joint torque prediction model integrating a hybrid-driven parameter identification is developed based on deep reinforcement learning (DRL). The framework for DRL-based parameter identification is constructed through tailored design of interfaces and training mechanisms, wherein the DRL agent can learn to identify the dynamic parameters from the trajectory database. And a deep neural network based on long short-term memory (LSTM) is proposed to predict the EC of IRs according to the joint torques and velocities. The nonlinear item, which is not modeled in the robot dynamic equation, are also encapsulated in the deep neural network with one-dimensional convolutional neural network (1D-CNN) layers to improve the prediction accuracy. To validate the accuracy and efficacy of the proposed method, experiments are conducted on a KUKA KR60-3 industrial robot with different loads. The results demonstrate that the proposed method can predict EC with a mean absolute percentage error of less than 2% under a fixed load and less than 3% under loads not used for agent training.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125604"},"PeriodicalIF":7.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571807","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":"Multiplex graph fusion network with reinforcement structure learning for fraud detection in online e-commerce platforms","authors":"Zheng Zhang ,&nbsp;Xiang Ao ,&nbsp;Claudio J. Tessone ,&nbsp;Gang Liu ,&nbsp;Mingyang Zhou ,&nbsp;Rui Mao ,&nbsp;Hao Liao","doi":"10.1016/j.eswa.2024.125598","DOIUrl":"10.1016/j.eswa.2024.125598","url":null,"abstract":"<div><div>Fraudulent activities on e-commerce platforms, such as spamming product reviews or fake payment behaviors, seriously mislead users’ purchasing decisions and harm platform integrity. To effectively identify fraudsters, recent research mainly attempts to employ graph neural networks (GNNs) with aggregating neighborhood features for detecting the fraud suspiciousness. However, GNNs are vulnerable to carefully-crafted perturbations in the graph structure, and the camouflage strategies of collusive fraudsters limit the effectiveness of GNNs-based fraud detectors. To address these issues, a novel multiplex graph fusion network with reinforcement structure learning (RestMGFN) is proposed in this paper to reveal the collaborative camouflage review fraud. Specifically, an adaptive graph structure learning module is designed to generate high-quality graph representation by utilizing paradigm constraints on the intrinsic properties of graph. Multiple relation-specific graphs are then constructed using meta-path search for capturing the deep semantic features of fraudulent activities. Finally, we incorporate the multiplex graph representations module into a unified framework, jointly optimizing the graph structure and corresponding embedding representations. Comprehensive experiments on real-world datasets verify the effectiveness and robustness of the proposed model compared with state-of-the-art approaches.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125598"},"PeriodicalIF":7.5,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593546","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}