Expert Systems with Applications最新文献

{"title":"Vessel speed prediction using latent-invariant transforms in the presence of incomplete information","authors":"Xu Zhao ,&nbsp;Yuhan Guo ,&nbsp;Yiyang Wang ,&nbsp;Meirong Wang","doi":"10.1016/j.eswa.2024.125685","DOIUrl":"10.1016/j.eswa.2024.125685","url":null,"abstract":"<div><div>This paper presents a novel model designed to predict the vessel speed, specifically tailored to tackle the challenges posed by incomplete information of relevant operating parameters encountered in certain scenarios. In this method, a latent trend in the operating state of marine power system is firstly identified from historical time-series data to approximate the calm water speed information. Then, the modeling of the remaining component, which corresponds to the met-ocean-induced speed loss, can be more precisely targeted. Moreover, the elements situated at diverse temporal scales of the remaining component are disentangled, aiming to resolve the intricacies of coupled factor learning, thus improving the accuracy and validity of the model. For time-series with relatively steady-state, an LSTM network with a global attention mechanism is proposed to effectively capture the temporal evolution, and a differencing operation is incorporated to mitigate potential data inconsistencies between voyages. Finally, the proposed framework has demonstrated superior predictive capabilities for speed compared to a variety of data-driven methods, using a 400,000 DWT ore carrier as an example.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125685"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662847","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":"AuthorNet: Leveraging attention-based early fusion of transformers for low-resource authorship attribution","authors":"Md. Rajib Hossain ,&nbsp;Mohammed Moshiul Hoque ,&nbsp;M. Ali Akber Dewan ,&nbsp;Enamul Hoque ,&nbsp;Nazmul Siddique","doi":"10.1016/j.eswa.2024.125643","DOIUrl":"10.1016/j.eswa.2024.125643","url":null,"abstract":"<div><div>Authorship Attribution (AA) is crucial for identifying the author of a given text from a pool of suspects, especially with the widespread use of the internet and electronic devices. However, most AA research has primarily focused on high-resource languages like English, leaving low-resource languages such as Bengali relatively unexplored. Challenges faced in this domain include the absence of benchmark corpora, a lack of context-aware feature extractors, limited availability of tuned hyperparameters, and OOV issues. To address these challenges, this study introduces AuthorNet for authorship attribution using attention-based early fusion of transformer-based language models, i.e., concatenation of an embeddings output of two existing models that were fine-tuned. AuthorNet consists of three key modules: Feature extraction, Fine-tuning and selection of best-performing models, and Attention-based early fusion. To evaluate the performance of AuthorNet, a number of experiments using four benchmark corpora have been conducted. The results demonstrated exceptional accuracy: 98.86 ± 0.01%, 99.49 ± 0.01%, 97.91 ± 0.01%, and 99.87 ± 0.01% for four corpora. Notably, AuthorNet outperformed all foundation models, achieving accuracy improvements ranging from 0.24% to 2.92% across the four corpora.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125643"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662850","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 face super-resolution through identity features and distilling facial prior knowledge","authors":"Anurag Singh Tomar ,&nbsp;K.V. Arya ,&nbsp;Shyam Singh Rajput","doi":"10.1016/j.eswa.2024.125625","DOIUrl":"10.1016/j.eswa.2024.125625","url":null,"abstract":"<div><div>Deep learning techniques in electronic surveillance have shown impressive performance for super-resolution (SR) of captured low-quality face images. Most of these techniques adopt facial priors to improve the feature details in the resultant super-resolved images. However, the estimation of facial priors from the captured low-quality images is often inaccurate in real-life situations because of their tiny, noisy, and blurry nature. Thus, the fusion of such priors badly affects the performance of these models. Therefore, this work presents a teacher–student-based face SR framework that efficiently preserves the personal facial structure information in the super-resolved faces. In the proposed framework, the teacher network exploits the facial heatmap-based ground-truth-prior to learn the facial structure that is utilized by the student network. The student network is trained with the identity feature loss for maintaining the identity and facial structure information in reconstructed high-resolution (HR) face images. The performance of the proposed framework is evaluated by conducting the experimental study on standard datasets namely CelebA-HQ and LFW face. The experimental results reveal that the proposed technique conquers the existing methods for the face SR task.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125625"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578464","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":"Machine learning for predicting used car resale prices using granular vehicle equipment information","authors":"Svenja Bergmann ,&nbsp;Stefan Feuerriegel","doi":"10.1016/j.eswa.2024.125640","DOIUrl":"10.1016/j.eswa.2024.125640","url":null,"abstract":"<div><div>Millions of used cars are sold every year, and, hence, accurate estimates of resale values are needed. One reason is that under- and overestimating the value of used cars at the end of their leasing period is directly related to the financial return of car retailers. However, in previous literature, granular vehicle equipment information (e.g., alloy rims, park assistance systems) as a predictor has been largely overlooked. In order to address this research gap, we assess the predictive power of granular information about vehicle equipment when forecasting the resale value of used cars. To achieve this, we first preprocess 50,000 equipment options through a tailored, end-to-end automated procedure. Subsequently, we employ machine learning using a comprehensive real-world dataset comprising 92,239 sales where each vehicle is characterized by a unique equipment configuration. We find that including equipment information improves the prediction performance (i.e., mean absolute error) by 3.27% and at a statistically significant level. Altogether, car retailers can use information about the specific vehicle configuration to more accurately predict prices of used vehicles, and, as an implication for businesses, this may eventually increase returns.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125640"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662113","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":"Bim-based Digital Twin development for university Campus management. Case study ETSICCP","authors":"Rubén Muñoz Pavón ,&nbsp;Marcos García Alberti ,&nbsp;Antonio Alfonso Arcos Álvarez ,&nbsp;Jorge Jerez Cepa","doi":"10.1016/j.eswa.2024.125696","DOIUrl":"10.1016/j.eswa.2024.125696","url":null,"abstract":"<div><div>Innovation and digitalization are outstanding topics acquiring each day more importance for local governments, especially in Facility Management sector. Moreover, during the COVID-19 situation, new management needs emerged, especially in large public buildings. Building Information Modeling (BIM) is considered as one of the emerging technologies used to reach a total digitalization of the infrastructure. Nevertheless, BIM implementation carries important barriers with itself like, high software and hardware investments, initial BIM skills training or low data interoperability. The objective of this project is to overpass those implementation barriers. For this purpose, the paper shows the creation of a BIM-based intelligent platform for infrastructure management that leads to the development of a Digital Twin (DT). To show the potential of the software developed, a real implementation in the Civil Engineering School at Universidad Politécnica de Madrid was carried out, obtaining significant results thanks to the actual feedback of infrastructure users and managers. The novelty of this project relies on the final results achieved, obtaining a complete DT for management functionalities like space reservation, live sensors data or assets management. All of it, linking BIM models with own software and hardware development using Internet of Things and cloud computing. A multidisciplinary work is compiled in this paper, providing the reader with the most relevant challenges detected in a real digitalization process.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125696"},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662778","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":"A distribution-preserving method for resampling combined with LightGBM-LSTM for sequence-wise fraud detection in credit card transactions","authors":"Behnam Yousefimehr,&nbsp;Mehdi Ghatee","doi":"10.1016/j.eswa.2024.125661","DOIUrl":"10.1016/j.eswa.2024.125661","url":null,"abstract":"<div><div>Fraud detection is a challenging task that can be difficult to carry out. To address these challenges, a comprehensive framework has been developed which includes a new resampling method combined with a data-dependent classifier that can detect fraud effectively. The proposed framework uses two hybrid approaches that leverage the strengths of a One-Class Support Vector Machine (OCSVM) with the Synthetic Minority Oversampling Technique (SMOTE) and random undersampling. The distribution of fraud instances is effectively preserved by this innovative framework. The comparison of the probability functions of fraud data before and after resampling is demonstrated, indeed. Afterward, The outputs of our hybrid approaches are analyzed using two distinct models, the Light Gradient-Boosting Machine (LightGBM) and the Long Short-Term Memory (LSTM) model. Our case study on European credit cards has consistently demonstrated the effectiveness of our techniques over existing methods, achieving a high F1 score of 87% with a corresponding AUC score of 96% in non-sequential fraud detection and The F1 score of 85% with an AUC score of 87% in sequential fraud detection. Additionally, we have developed an innovative algorithm for determining optimal window sizes for sequence-wise fraud analysis, which recommends window sizes of 3 for the European dataset, highlighting the efficacy of sequence-wise analysis. Overall, the proposed framework, not only offers a promising solution to enhance fraud detection accuracy, but it also reduces false positives.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125661"},"PeriodicalIF":7.5,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662815","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":"Mitigating false negatives in imbalanced datasets: An ensemble approach","authors":"Marcelo Vasconcelos ,&nbsp;Luís Cavique","doi":"10.1016/j.eswa.2024.125674","DOIUrl":"10.1016/j.eswa.2024.125674","url":null,"abstract":"<div><div>Imbalanced datasets present a challenge in machine learning, especially in binary classification scenarios where one class significantly outweighs the other. This imbalance often leads to models favoring the majority class, resulting in inadequate predictions for the minority class, specifically in false negatives. In response to this issue, this work introduces the MinFNR ensemble algorithm, designed to minimize False Negative Rates (FNR) in imbalanced datasets. The new approach strategically combines data-level, algorithmic-level, and hybrid-level approaches to enhance overall predictive capabilities while minimizing computational resources using the Set Covering Problem (SCP) formulation. Through a comprehensive evaluation of diverse datasets, MinFNR consistently outperforms individual algorithms, showing its potential for applications where the cost of false negatives is substantial, such as fraud detection and medical diagnosis. This work also contributes to ongoing efforts to improve the reliability and effectiveness of machine learning algorithms in real imbalanced scenarios.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125674"},"PeriodicalIF":7.5,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662818","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":"Vehicle trajectory extraction with interacting multiple model for low-channel roadside LiDAR","authors":"Bowen Gong ,&nbsp;Binwen Zhao ,&nbsp;Yue Wang ,&nbsp;Ciyun Lin ,&nbsp;Hongchao Liu","doi":"10.1016/j.eswa.2024.125662","DOIUrl":"10.1016/j.eswa.2024.125662","url":null,"abstract":"<div><div>High-precision and consistent vehicle trajectories encompass microscopic traffic parameters, mesoscopic traffic flow characteristics, and macroscopic traffic flow features, which is the cornerstone of innovation in data-driven traffic management and control applications. However, occlusion and trajectory interruption remain challenging in multivehicle tracking under complex traffic environments using low-channel roadside LiDAR. To address the challenge, a novel framework for vehicle trajectory extraction using low-channel roadside LiDAR was proposed. First, the geometric features of the cluster and its L-shape bounding box were used to address the over-segmentation in vehicle detection arising from occlusion and point cloud sparse. Then, objects within adjacent point cloud frames were associated by developing an improved Hungarian algorithm integrated with an adaptive distance threshold to solve the mismatching problem caused by objects entrancing and exiting in a new point cloud frame. Finally, an improved interacting multiple model by considering vehicle driving patterns was deployed to predict the location of missing vehicles and connect the interrupted trajectories. Experimental results showed that the proposed methods achieve 98.76 % of vehicle detection accuracy and 97.40 % of data association precision. The mean absolute error (MAE) and mean square error (MSE) of the vehicle position estimation are 0.2252 m and 0.0729 m², respectively. The trajectory extraction precision outperforms most of the state-of-the-art algorithms.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125662"},"PeriodicalIF":7.5,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586044","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 new look of dispatching for multi-objective interbay AMHS in semiconductor wafer manufacturing: A T–S fuzzy-based learning approach","authors":"Hua Li ,&nbsp;Zhenghong Jin","doi":"10.1016/j.eswa.2024.125615","DOIUrl":"10.1016/j.eswa.2024.125615","url":null,"abstract":"<div><div>Semiconductor wafer fabrication systems (SWFS) are among the most intricate discrete processing environments globally. Since the costs associated with automated material handling systems (AMHS) within fabs account for 20%–50% of manufacturing expenses, it is crucial to enhance the efficiency of material handling in semiconductor production lines. However, optimizing AMHS is difficult due to the complexities inherent in large-scale, nonlinear, dynamic, and stochastic production settings, as well as differing objectives and goals. To overcome these challenges, this paper presents a novel fuzzy-based learning algorithm to enhance the multi-objective dispatching model, which incorporates both transportation and production aspects for interbay AMHS in wafer fabrication manufacturing, aligning it more closely with real-world conditions. Furthermore, we formulate a new constrained nonlinear dispatching problem. To tackle the inherent nonlinearity, a Takagi-Sugeno (T–S) fuzzy modeling approach is developed, which transforms nonlinear terms into a fuzzy linear dispatching model and optimizes the weight in multi-objective problems to obtain the optimal solution. The effectiveness and superiority of the proposed approach are demonstrated through extensive simulations and comparative analysis with existing methods. As a result, the proposed method significantly improves transport efficiency, increases wafer throughput, and reduces processing cycle times.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125615"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572124","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 of A deep Learning-based algorithm for High-Pitch helical computed tomography imaging","authors":"Xiaoman Duan ,&nbsp;Xiao Fan Ding ,&nbsp;Samira Khoz ,&nbsp;Xiongbiao Chen ,&nbsp;Ning Zhu","doi":"10.1016/j.eswa.2024.125663","DOIUrl":"10.1016/j.eswa.2024.125663","url":null,"abstract":"<div><div>High-pitch X-ray helical computed tomography (HCT) imaging has been recently drawing considerable attention in biomedical fields due to its ability to reduce the scanning time and thus lower the radiation dose that objects (being imagined) may receive. However, the issue of compromised reconstruction quality caused by incomplete data in these high-pitch CT scans remains, thus limiting its applications. By addressing the aforementioned issue, this paper presents our study on the development of a novel deep leaning (DL)-based algorithm, ViT-U, for high-pitch X-ray propagation-based imaging HCT (PBI-HCT) reconstruction. ViT-U consists of two key process modules of a vision transformer (ViT) and a convolutional neural network (i.e., U-Net), where ViT addresses the missing information in the data domain and U-Net enhances the post data-processing in the reconstruction domain. For verification, we designed and conducted simulations and experiments with both low-density-biomaterial samples and biological-tissue samples to exemplify the biomedical applications, and then examined the ViT-U performance with varying pitches of 3, 3.5, 4, and 4.5, respectively, for comparison in term of radiation does and reconstruction quality. Our results showed that the high-pitch PBI-HCT allowed for the dose reduction from 72% to 93%. Importantly, our results demonstrated that the ViT-U exhibited outstanding performance by effectively removing the missing wedge artifacts thus enhancing the reconstruction quality of high-pitch PBI-HCT imaging. Also, our results showed the superior capability of ViT-U to achieve high quality of reconstruction from the high-pitch images with the helical pitch value up to 4 (which allowed for the substantial reduction of radiation doses). Taken together, our DL-based ViT-U algorithm not only enables high-speed imaging with low radiation dose, but also maintains the high quality of imaging reconstruction, thereby offering significant potentials for biomedical imaging applications.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"262 ","pages":"Article 125663"},"PeriodicalIF":7.5,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578467","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}