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Journal articles on the topic 'Context-based fusion'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Snidaro, Lauro, Jesus Garcia, and Juan Manuel Corchado. "Context-based information fusion." Information Fusion 21 (January 2015): 82–84. http://dx.doi.org/10.1016/j.inffus.2014.02.001.

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Zongwen Bai, Zongwen Bai, Xiaohuan Chen Zongwen Bai, Meili Zhou Xiaohuan Chen, Tingting Yi Meili Zhou, and Wei-Che Chien Tingting Yi. "Low-rank Multimodal Fusion Algorithm Based on Context Modeling." 網際網路技術學刊 22, no. 4 (2021): 913–21. http://dx.doi.org/10.53106/160792642021072204018.

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Patricio, Miguel A., Jesús García, Juan M. Corchado, Javier Bajo, Alaa Khamis, and Eleni E. Mangina. "Intelligent Systems in Context-Based Distributed Information Fusion." International Journal of Distributed Sensor Networks 9, no. 11 (2013): 836463. http://dx.doi.org/10.1155/2013/836463.

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Snidaro, Lauro, Jesús García, and James Llinas. "Context-based Information Fusion: A survey and discussion." Information Fusion 25 (September 2015): 16–31. http://dx.doi.org/10.1016/j.inffus.2015.01.002.

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Cao, Yunfeng, Zhouyu Zhang, Yanming Fan, Meng Ding, and Jiang Tao. "Vision-Based Flying Targets Detection via Spatiotemporal Context Fusion." IEEE Access 7 (2019): 144090–100. http://dx.doi.org/10.1109/access.2019.2943068.

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Gómez-Romero, Juan, Miguel A. Serrano, Jesús García, José M. Molina, and Galina Rogova. "Context-based multi-level information fusion for harbor surveillance." Information Fusion 21 (January 2015): 173–86. http://dx.doi.org/10.1016/j.inffus.2014.01.011.

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Emmanouilidis, Christos, Petros Pistofidis, Apostolos Fournaris, et al. "Context-based and human-centred information fusion in diagnostics." IFAC-PapersOnLine 49, no. 28 (2016): 220–25. http://dx.doi.org/10.1016/j.ifacol.2016.11.038.

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He, Wei, Zhenmiao Deng, Yishan Ye, and Pingping Pan. "ConCs-Fusion: A Context Clustering-Based Radar and Camera Fusion for Three-Dimensional Object Detection." Remote Sensing 15, no. 21 (2023): 5130. http://dx.doi.org/10.3390/rs15215130.

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Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe and effective operation of environment perception systems in autonomous driving. In this study, we propose a method called context clustering-based radar and camera fusion for 3D object detection (ConCs-Fusion) that combines radar and camera sensors at the intermediate fusion level to achieve 3D object detection. We extract features from heterogeneous sensors and input them as feature point sets into the fusion module. Within the fusion module, we utilize context cluster blocks to learn multi-scale feat

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Hu, Tao, Pengwan Yang, Chiliang Zhang, Gang Yu, Yadong Mu, and Cees G. M. Snoek. "Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8441–48. http://dx.doi.org/10.1609/aaai.v33i01.33018441.

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Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremendous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixellevel annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attentionbased Multi-Context Guiding (A-MCG) network, which consists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between support

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KATO, H., S. HIDAKA, Z. HU, K. NAKANO, and Y. ISHIHARA. "Context-preserving XQuery fusion." Mathematical Structures in Computer Science 25, no. 4 (2014): 916–41. http://dx.doi.org/10.1017/s096012951300008x.

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This paper solves the known problem of elimination of unnecessary internal element construction as well as variable elimination in XML processing with (a subset of) XQuery without ignoring the issues of document order. The semantics of XQuery is context sensitive and requires preservation of document order. In this paper, we propose, as far as we are aware, the first XQuery fusion that can deal with both the document order and the context of XQuery expressions. More specifically, we carefully design a context representation of XQuery expressions based on the Dewey order encoding, develop a con

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Smirnov, Alexander, Tatiana Levashova, and Nikolay Shilov. "Patterns for context-based knowledge fusion in decision support systems." Information Fusion 21 (January 2015): 114–29. http://dx.doi.org/10.1016/j.inffus.2013.10.010.

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Tasnim, Samia, Niki Pissinou, S. Sitharama Iyengar, Kianoosh G. Boroojeni, and Kishwar Ahmed. "RCoD: Reputation-Based Context-Aware Data Fusion for Mobile IoT." Sensors 25, no. 4 (2025): 1171. https://doi.org/10.3390/s25041171.

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The rapid development of mobile sensing technologies (e.g., smart devices embedded with various powerful sensors) has encouraged the proliferation of the Internet of Things (IoT). Although data reliability and accuracy are crucial in many sensor applications (e.g., air-quality monitoring), it is often difficult to ensure these properties. Mobile IoT’s people-centric architecture allows for more inaccurate and corrupted data. In this manuscript, we are addressing the problem of how to predict data more accurately in the presence of malicious participants who inject false data to manipulate the

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Nguyen-Mau, Toan, Anh-Cuong Le, Duc-Hong Pham, and Van-Nam Huynh. "An information fusion based approach to context-based fine-tuning of GPT models." Information Fusion 104 (April 2024): 102202. http://dx.doi.org/10.1016/j.inffus.2023.102202.

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Zhang, Guoli. "Crowd Counting Based on Context-Aware and Multi Scale Feature Fusion." Frontiers in Computing and Intelligent Systems 2, no. 2 (2022): 12–15. http://dx.doi.org/10.54097/fcis.v2i2.3736.

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Crowd counting plays an important role in public security. Estimating the number of people in an image with congested crowd accurately is a challenging task. The crowd counting method based on fully convolutional network can perform well in crowd image with complex scene. In this paper, to address the counting problems of occlusion，background clutter and perspective effect, we proposed a simple but effective method called Context-aware Multi scale Fusion Network(CMF Net).The CMF Net applied VGG network as backbone to extract coarse features. Then, three context-aware multi-scale fusion modules

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Chen, Shengbo, Hongchang Zhang, and Zhou Lei. "Person Re-Identification Based on Attention Mechanism and Context Information Fusion." Future Internet 13, no. 3 (2021): 72. http://dx.doi.org/10.3390/fi13030072.

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Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information

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Borges, Vijay, and Wilson Jeberson. "Survey of Context Information Fusion for Sensor Networks Based Ubiquitous Systems." Computer Science and Information Technology 2, no. 3 (2014): 165–78. http://dx.doi.org/10.13189/csit.2014.020306.

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Zhao, Yurong, Yao Ling, Peng Xia, and Qianqian Xu. "Research on Target Tracking Algorithm Based on context Multi-feature Fusion." Journal of Physics: Conference Series 1678 (November 2020): 012097. http://dx.doi.org/10.1088/1742-6596/1678/1/012097.

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Wu, Heng, Yisi Liu, Chunhua He, and Shaojuan Luo. "MSDA-HLGCformer-based context-aware fusion network for underwater organism detection." Optics & Laser Technology 181 (February 2025): 111957. http://dx.doi.org/10.1016/j.optlastec.2024.111957.

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Yan, Yang, and Chen Fei. "Dehazing network based on residual context attention and cross-layer fusion." Scientific Insights and Discoveries Review 5 (October 14, 2024): 110–20. http://dx.doi.org/10.59782/sidr.v5i1.94.

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Current image dehazing algorithms based on deep learning usually use traditional convolutional layers when extracting features, which easily causes the loss of image details and edge information, ignores the position information of the image when extracting features, and ignores the original information of the image when fusing features, and cannot restore high-quality haze-free images with complete structure and clarity. To address this problem, a dehazing algorithm based on residual context attention and cross-layer feature fusion is proposed. Firstly, the residual group structure is obtaine

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Zhang, Shihui, He Li, Weihang Kong, Lei Wang, and Xiaofang Niu. "An object counting network based on hierarchical context and feature fusion." Journal of Visual Communication and Image Representation 62 (July 2019): 166–73. http://dx.doi.org/10.1016/j.jvcir.2019.05.003.

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Soriano, Antonio, Luis Vergara, Bouziane Ahmed, and Addisson Salazar. "Fusion of Scores in a Detection Context Based on Alpha Integration." Neural Computation 27, no. 9 (2015): 1983–2010. http://dx.doi.org/10.1162/neco_a_00766.

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We present a new method for fusing scores corresponding to different detectors (two-hypotheses case). It is based on alpha integration, which we have adapted to the detection context. Three optimization methods are presented: least mean square error, maximization of the area under the ROC curve, and minimization of the probability of error. Gradient algorithms are proposed for the three methods. Different experiments with simulated and real data are included. Simulated data consider the two-detector case to illustrate the factors influencing alpha integration and demonstrate the improvements o

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Liang, Hong, Hui Zhou, Qian Zhang, and Ting Wu. "Object Detection Algorithm Based on Context Information and Self-Attention Mechanism." Symmetry 14, no. 5 (2022): 904. http://dx.doi.org/10.3390/sym14050904.

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Pursuing an object detector with good detection accuracy while ensuring detection speed has always been a challenging problem in object detection. This paper proposes a multi-scale context information fusion model combined with a self-attention block (CSA-Net). First, an improved backbone network ResNet-SA is designed with self-attention to reduce the interference of the image background area and focus on the object region. Second, this work introduces a receptive field feature enhancement module (RFFE) to combine local and global features while increasing the receptive field. Then this work a

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Zhang, Wenxiang, Chunmeng Wang, and Jun Zhu. "MEF-CAAN: Multi-Exposure Image Fusion Based on a Low-Resolution Context Aggregation Attention Network." Sensors 25, no. 8 (2025): 2500. https://doi.org/10.3390/s25082500.

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Recently, deep learning-based multi-exposure image fusion methods have been widely explored due to their high efficiency and adaptability. However, most existing multi-exposure image fusion methods have insufficient feature extraction ability for recovering information and details in extremely exposed areas. In order to solve this problem, we propose a multi-exposure image fusion method based on a low-resolution context aggregation attention network (MEF-CAAN). First, we feed the low-resolution version of the input images to CAAN to predict their low-resolution weight maps. Then, the high-reso

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Ma, Xiaolin, Kaiqi Wu, Hailan Kuang, and Xinhua Liu. "An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion." Applied Sciences 12, no. 3 (2022): 1532. http://dx.doi.org/10.3390/app12031532.

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Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-featu

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Saranya, S. S., and Dr N. Sabiyath Fatima. "Context Aware Data Fusion on Massive IOT Data in Dynamic IOT Analytics." Webology 17, no. 2 (2020): 957–70. http://dx.doi.org/10.14704/web/v17i2/web17080.

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Educational Data management is a critical task for the researchers due to mammoth data generated by sensors and IoT (Internet of Things) devices. Managing this huge volume of data, cleaning this data from impurities is an inherent need. DF (Data Fusion) processes combine data from multiple sources based on their similarity for an easy management. DF processes focus on many factors like nature of data and application that uses that data. Many DFAs (Data Fusion approaches) have been proposed without detailing on the context for integrating data in fusion tasks. This work attempts to cover this g

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Li, Yingzhi, and Laiyuan Xiao. "Research on public health crisis early warning system based on context awareness." Technology and Health Care 30 (February 25, 2022): 303–14. http://dx.doi.org/10.3233/thc-thc228029.

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BACKGROUND: With the continuous expansion of urban scale and the increasing concentration of population, public health crisis has become an important part of urban residents’ health management. The outbreak of the COVID-19 pandemic in Wuhan in 2020 has sounded the alarm. OBJECTIVE: With the government at all levels to carry out the construction of urban Internet of things and information internet, the Internet backbone network has been built, deployed a large number of sensors, and collected a large number of urban situation data. METHODS: In this paper, situational awareness technology is int

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Wang, Yusong, Xuanye Fang, Huifeng Yin, et al. "BIG-FUSION: Brain-Inspired Global-Local Context Fusion Framework for Multimodal Emotion Recognition in Conversations." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 1574–82. https://doi.org/10.1609/aaai.v39i2.32149.

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Considering the importance of capturing both global conversational topics and local speaker dependencies for multimodal emotion recognition in conversations, current approaches first utilize sequence models like Transformer to extract global context information, then apply Graph Neural Networks to model local speaker dependencies for local context information extraction, coupled with Graph Contrastive Learning (GCL) to enhance node representation learning. However, this sequential design introduces potential biases: the extracted global context information inevitably influences subsequent proc

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Zhang, Xianfeng, Bin Hu, Shukan Liu, Qiao Sun, and Lin Chen. "AttenFlow: Context-Aware Architecture with Consensus-Based Retrieval and Graph Attention for Automated Document Processing." Applied Sciences 15, no. 13 (2025): 7517. https://doi.org/10.3390/app15137517.

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Automated document processing and circulation systems face critical challenges in achieving reliable retrieval accuracy and robust classification performance, particularly in security-critical organizational environments. Traditional approaches suffer from fundamental limitations, including fixed fusion strategies in hybrid retrieval systems, inability to model inter-document relationships in classification tasks, and lack of confidence estimation for result reliability. This paper introduces AttenFlow, a novel context-aware architecture that revolutionizes document management through two core

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Hubner, Michael, Kilian Wohlleben, Martin Litzenberger, et al. "Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion." Sensors 24, no. 13 (2024): 4118. http://dx.doi.org/10.3390/s24134118.

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Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propos

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Koido, Shigeo, Eiichi Hara, Sadamu Homma, et al. "Cancer Vaccine by Fusions of Dendritic and Cancer Cells." Clinical and Developmental Immunology 2009 (2009): 1–13. http://dx.doi.org/10.1155/2009/657369.

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Dendritic cells (DCs) are potent antigen-presenting cells and play a central role in the initiation and regulation of primary immune responses. Therefore, their use for the active immunotherapy against cancers has been studied with considerable interest. The fusion of DCs with whole tumor cells represents in many ways an ideal approach to deliver, process, and subsequently present a broad array of tumor-associated antigens, including those yet to be unidentified, in the context of DCs-derived costimulatory molecules. DCs/tumor fusion vaccine stimulates potent antitumor immunity in the animal t

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Xia, Han Hank, Hao Gao, Hang Shao, Kun Gao, and Wei Liu. "Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture." Applied Sciences 13, no. 23 (2023): 12798. http://dx.doi.org/10.3390/app132312798.

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In this study, we introduce the U-Swin fusion model, an effective and efficient transformer-based architecture designed for the fusion of multi-focus microscope images. We utilized the Swin-Transformer with shifted window and path merging as the encoder for extracted hierarchical context features. Additionally, a Swin-Transformer-based decoder with patch expansion was designed to perform the un-sampling operation, generating the fully focused image. To enhance the performance of the feature decoder, the skip connections were applied to concatenate the hierarchical features from the encoder wit

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Wu, Yongxiang, Yili Fu, and Shuguo Wang. "Real-time pixel-wise grasp affordance prediction based on multi-scale context information fusion." Industrial Robot: the international journal of robotics research and application 49, no. 2 (2021): 368–81. http://dx.doi.org/10.1108/ir-06-2021-0118.

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Purpose This paper aims to use fully convolutional network (FCN) to predict pixel-wise antipodal grasp affordances for unknown objects and improve the grasp detection performance through multi-scale feature fusion. Design/methodology/approach A modified FCN network is used as the backbone to extract pixel-wise features from the input image, which are further fused with multi-scale context information gathered by a three-level pyramid pooling module to make more robust predictions. Based on the proposed unify feature embedding framework, two head networks are designed to implement different gra

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Delicato, Flávia C., Tayssa Vandelli, Mario Bonicea, and Claudio M. de Farias. "Heracles: A Context-Based Multisensor Sensor Data Fusion Algorithm for the Internet of Things." Information 11, no. 11 (2020): 517. http://dx.doi.org/10.3390/info11110517.

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In the Internet of Things (IoT), extending the average battery duration of devices is of paramount importance, since it promotes uptime without intervention in the environment, which can be undesirable or costly. In the IoT, the system’s functionalities are distributed among devices that (i) collect, (ii) transmit and (iii) apply algorithms to process and analyze data. A widely adopted technique for increasing the lifetime of an IoT system is using data fusion on the devices that process and analyze data. There are already several works proposing data fusion algorithms for the context of wirel

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Jiang, Renyan, and Bowei Zou. "A DEA-based multi-response fusion model in the context of Taguchi method." Journal of Physics: Conference Series 1983, no. 1 (2021): 012108. http://dx.doi.org/10.1088/1742-6596/1983/1/012108.

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Elayaperumal, Dinesh, and Young Hoon Joo. "Robust visual object tracking using context-based spatial variation via multi-feature fusion." Information Sciences 577 (October 2021): 467–82. http://dx.doi.org/10.1016/j.ins.2021.06.084.

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Kim, Svetlana, and YongIk Yoon. "A Fusion Context-Aware Model based on Hybrid Sensing for Recommendation Smart Service." KIPS Transactions on Computer and Communication Systems 2, no. 1 (2013): 1–6. http://dx.doi.org/10.3745/ktccs.2013.2.1.001.

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37

Zhang, Wancheng, Yanmin Luo, Zhi Chen, Yongzhao Du, Daxin Zhu, and Peizhong Liu. "A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion." Algorithms 12, no. 1 (2018): 8. http://dx.doi.org/10.3390/a12010008.

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Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual object tracking. However, visual tracking is still challenging when the target objects undergo complex scenarios such as occlusion, deformation, scale changes and illumination changes. In this paper, we utilize the hierarchical features of convolutional neural networks (CNNs) and learn a spatial-temporal context correlation filter on convolutional layers. Then, the translation is estimated by fusing the response score of the filters on the three convolutional layers. In terms of scale estimation, we learn

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Shi, Lei, Xiang Xu, and Ioannis A. Kakadiaris. "Detecting Multi-Scale Faces Using Attention-Based Feature Fusion and Smoothed Context Enhancement." IEEE Transactions on Biometrics, Behavior, and Identity Science 2, no. 3 (2020): 235–44. http://dx.doi.org/10.1109/tbiom.2020.2993242.

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Tabib Mahmoudi, Fatemeh, Farhad Samadzadegan, and Peter Reinartz. "Object Recognition Based on the Context Aware Decision-Level Fusion in Multiviews Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 1 (2015): 12–22. http://dx.doi.org/10.1109/jstars.2014.2362103.

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Wang, Yong, Zhenglong He, Xiangqiang Zeng, et al. "GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion." Remote Sensing 16, no. 10 (2024): 1797. http://dx.doi.org/10.3390/rs16101797.

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Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to

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Ren, Jinglei, Hailong Zhang, Yongjuan Zhao, and Cong Lan. "Semantic context-induced fast fusion network based driver attention prediction in complex scenarios." International Journal of Vehicle Systems Modelling and Testing 19, no. 2 (2025): 91–104. https://doi.org/10.1504/ijvsmt.2025.147336.

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Zhao, Dandan, and Guocai Yin. "Teaching Method of Japanese Professional Writing Course Based on Big Data Fusion." Security and Communication Networks 2022 (May 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/2838008.

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In recent years, the number of Japanese learners has increased year by year. When Japanese is learned as a language, knowledge of the language itself is indispensable. This article aims to explore the teaching methods of Japanese professional writing courses based on the integration of big data. This article first introduces big data fusion and introduces the definition and model of data fusion. Generally, there are two types of data fusion models: distributed and centralized. Then, I analyzed the artificial neural network algorithm, which is a computational model that imitates the animal brai

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Huang, Cuiyang, and Zihan Hu. "A multimodal transformer-based visual question answering method integrating local and global information." PLOS One 20, no. 7 (2025): e0324757. https://doi.org/10.1371/journal.pone.0324757.

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Addressing the limitations in current visual question answering (VQA) models face limitations in multimodal feature fusion capabilities and often lack adequate consideration of local information, this study proposes a multimodal Transformer VQA network based on local and global information integration (LGMTNet). LGMTNet employs attention on local features within the context of global features, enabling it to capture both broad and detailed image information simultaneously, constructing a deep encoder-decoder module that directs image feature attention based on the question context, thereby enh

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Ji Yuanfa, 纪元法, 何传骥 He Chuanji, 孙希延 Sun Xiyan та 郭宁 Guo Ning. "基于自适应特征融合与上下文感知的目标跟踪". Laser & Optoelectronics Progress 58, № 16 (2021): 1610011. http://dx.doi.org/10.3788/lop202158.1610011.

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Jia, Bing, Shuai Liu, Yushuai Guan, Wuyungerile Li, and Weiwu Ren. "The Fusion Model of Multidomain Context Information for the Internet of Things." Wireless Communications and Mobile Computing 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/6274824.

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The Internet of Things aims to provide the user with deep adaptive intelligence services according to the user’s personalized characteristics. Most of the characteristics are presented in the form of high-level context. But it often lacks methods to obtain high-level context information directly in the Internet of Things. In this paper, so as to achieve the corresponding high-level context information using the specific low-level multidomain context directly obtained by different sensors in the Internet of Things, we present a machine learning method to construct a context fusion model based o

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Yong, Xinyou, Chongqing Zeng, Lican Dai, Wanli Liu, and Shimin Cai. "Short Text Event Coreference Resolution Based on Context Prediction." Applied Sciences 14, no. 2 (2024): 527. http://dx.doi.org/10.3390/app14020527.

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Event coreference resolution is the task of clustering event mentions that refer to the same entity or situation in text and performing operations like linking, information completion, and validation. Existing methods model this task as a text similarity problem, focusing solely on semantic information, neglecting key features like event trigger words and subject. In this paper, we introduce the event coreference resolution based on context prediction (ECR-CP) as an alternative to traditional methods. ECR-CP treats the task as sentence-level relationship prediction, examining if two event desc

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Wang, Shuhai, and Linfu Sun. "Chinese Named Entity Recognition for Automobile Fault Texts Based on External Context Retrieving and Adversarial Training." Entropy 27, no. 2 (2025): 133. https://doi.org/10.3390/e27020133.

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Identifying key concepts in automobile fault texts is crucial for understanding fault causes and enabling diagnosis. However, effective mining tools are lacking, leaving much latent information unexplored. To solve the problem, this paper proposes Chinese named entity recognition for automobile fault texts based on external context retrieval and adversarial training. First, we retrieve external contexts by using a search engine. Then, the input sentence and its external contexts are respectively fed into Lexicon Enhanced BERT to improve the text embedding representation. Furthermore, the input

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Liu, Gang, Jiangtao Xi, Jun Tong, and Hongpeng Xu. "An Infrared Aircraft Detection Algorithm Based on Context Perception Feature Enhancement." Electronics 13, no. 14 (2024): 2695. http://dx.doi.org/10.3390/electronics13142695.

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To address the issue of insufficient extraction of target features and the resulting impact on detection performance in long-range infrared aircraft target detection caused by small imaging area and weak radiation intensity starting from the idea of perceiving target context to enhance the features extracted by convolutional neural network, this paper proposes a detecting algorithm based on AWFGLC (adaptive weighted fusion of global–local context). Based on the mechanism of AWFGLC, the input feature map is randomly reorganized and partitioned along the channel dimension, resulting in two featu

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Zhukov, Alexey, Alain Rivero, Jenny Benois-Pineau, Akka Zemmari, and Mohamed Mosbah. "A Hybrid System for Defect Detection on Rail Lines through the Fusion of Object and Context Information." Sensors 24, no. 4 (2024): 1171. http://dx.doi.org/10.3390/s24041171.

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Defect detection on rail lines is essential for ensuring safe and efficient transportation. Current image analysis methods with deep neural networks (DNNs) for defect detection often focus on the defects themselves while ignoring the related context. In this work, we propose a fusion model that combines both a targeted defect search and a context analysis, which is seen as a multimodal fusion task. Our model performs rule-based decision-level fusion, merging the confidence scores of multiple individual models to classify rail-line defects. We call the model “hybrid” in the sense that it is com

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Zhao, Hongyu, Fang Lyu, and Yalan Luo. "Research on the Effect of Online Marketing Based on Multimodel Fusion and Artificial Intelligence in the Context of Big Data." Security and Communication Networks 2022 (January 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/1516543.

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Traditional online marketing methods use a single model to predict the advertising conversion rate, but the prediction results are not accurate, and users are not satisfied with the recommendation results. Therefore, this paper proposes an online marketing method based on multimodel fusion and artificial intelligence algorithms under the background of big data. First, it introduces big data technology and analyzes the characteristics of network advertising marketing model (RTB). Second, combined with multitask learning and fusion technology to improve the single model in advertising conversion

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