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Journal articles on the topic 'Attribute information fusion'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Yuhong Zhao, Yuhong Zhao, Xiangming Ni Yuhong Zhao, Yue Yao Xiangming Ni, and Peng Mei Yue Yao. "Research on Link Prediction Method Based on Information Fusion Graph Embedding." 電腦學刊 35, no. 4 (2024): 059–73. http://dx.doi.org/10.53106/199115992024083504005.

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<p>To accurately and efficiently capture the topological and attribute information of nodes and apply them to the link prediction task, this paper proposes a Dual Channel Graph Convolution Link Prediction (DC-GCN). DC-GCN constructs a dual channel through the graph convolution network. DC-GCN can learn both topological embeddings and attribute embeddings of nodes; it introduces an attention mechanism to learn the weights of each embedding adaptively and then performs weighted fusion to obtain the final embedding representation of nodes. Finally, the Hadamard distance of nodes is used to

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Liu, Wan Jin, Jin Chao An, Hui Zhou, and Chao Su. "Application to Sedimentary Facies Identification Used RGB Fusion Imaging in Multi-Attribute." Advanced Materials Research 546-547 (July 2012): 656–60. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.656.

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The seismic attribute has multi-solution, and can not correspond to geological bodies exactly, a variety of seismic attributes information interpreted by changes in their characteristic parameters was prone to conflicts, the fusion technology of multi-attribute fuses the independent single-attribute in seismic data together, it can use the advantage of each attribute to display the characterization of geological body vividly. In this paper, we extract the attributes slice under the control of isochronous stratigraphic framework along layers, optimize the attribute using reference well data to

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Ren, Yuyuan, Hong Ma, Shuxin Liu, and Kai Wang. "Hypernetwork Link Prediction Method Based on Fusion of Topology and Attribute Features." Entropy 25, no. 1 (2022): 89. http://dx.doi.org/10.3390/e25010089.

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Link prediction aims at predicting missing or potential links based on the known information of complex networks. Most existing methods focus on pairwise low-order relationships while ignoring the high-order interaction and the rich attribute information of entities in the actual network, leading to the low performance of the model in link prediction. To mine the cross-modality interactions between the high-order structure and attributes of the network, this paper proposes a hypernetwork link prediction method for fusion topology and attributes (TA-HLP). Firstly, a dual channel coder is employ

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Zheng, Hui. "A Novel Information Fusion Method Based on Preference Selection Index." Advanced Materials Research 1078 (December 2014): 349–52. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.349.

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The aim of this paper is to propose a new information fusion method for the problem of multi-sensor target recognition. Multi-sensor information fusion problem contains many characteristic indexes, and thus it can be regarded as a multi-attribute decision making problem. The new fusion method is put forward based on preference selection index method. The new information fusion method is not necessary to assign relative importance between attributes, but overall preference value of attributes are calculated using concept of statistics. Thus the new method can overcome the subjective randomness

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Verma, Rajkumar, and Bhudev Sharma. "Prioritized Information Fusion Method for Triangular Fuzzy Information and Its Application to Multiple Attribute Decision Making." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 02 (2016): 265–89. http://dx.doi.org/10.1142/s0218488516500136.

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This study investigates the multiple attribute decision making under triangular fuzzy environment in which the attributes and experts are in different priority level. By combining the idea of quasi arithmetic mean and prioritized weighted average (PWA) operator, we first propose two new prioritized aggregation operators called quasi fuzzy prioritized weighted average (QFPWA) operator and the quasi fuzzy prioritized weighted ordered weighted average (QFPWOWA) operator for aggregating triangular fuzzy information. The properties of the new aggregation operators are studied in detail and their sp

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Zhang, Xiuquan, Lin Shen та Kaiquan Shi. "(αF,αF¯)-Information Fusion Generated by Information Segmentation and Its Intelligent Retrieval". Mathematics 10, № 5 (2022): 713. http://dx.doi.org/10.3390/math10050713.

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Making use of the mathematical model with dynamic features and attribute disjunctive characteristics, the new concepts of αF-information segmentation, αF¯-information segmentation, (αF,αF¯)-information segmentation and their attribute characteristics are given, and the intelligent acquisition of matrix reasoning and information segmentation is given, as well as the information segmentation theorem. Moreover, the equivalence between information segmentation and information fusion is discussed, and the information fusion intelligent acquisition intelligent retrieval algorithm is given. Based on

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Zhang, Wei, and Zhonglin Ye. "A Heterogeneous Network Text Attribute Fusion Method Based on Multi-Level Semantic Relation Contrastive Learning." International Journal of Data Warehousing and Mining 21, no. 1 (2025): 1–18. https://doi.org/10.4018/ijdwm.378680.

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Contrastive learning enables models to learn graph structural information through self-supervised learning in the absence of labels. However, real-world networks often contain both graph structural information and incomplete node attribute information. Based on this, this paper proposes a heterogeneous network text attribute fusion method based on multi-layer semantic relation contrastive learning. Firstly, the heterogeneous network components are reconstructed using semantic and thematic attribute acquisition methods at different levels, obtaining semantic representations of text attributes a

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Lin, Mugang, Kunhui Wen, Xuanying Zhu, Huihuang Zhao, and Xianfang Sun. "Graph Autoencoder with Preserving Node Attribute Similarity." Entropy 25, no. 4 (2023): 567. http://dx.doi.org/10.3390/e25040567.

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The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and th

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Ding, Haitao, Chu Sun, and Jianqiu Zeng. "Fuzzy Weighted Clustering Method for Numerical Attributes of Communication Big Data Based on Cloud Computing." Symmetry 12, no. 4 (2020): 530. http://dx.doi.org/10.3390/sym12040530.

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It is necessary to optimize clustering processing of communication big data numerical attribute feature information in order to improve the ability of numerical attribute mining of communication big data, and thus a big data clustering algorithm based on cloud computing was proposed. The cloud extended distributed feature fitting method was used to process the numerical attribute linear programming of communication big data, and the mutual information feature quantity of communication big data numerical attribute was extracted. Combined with fuzzy C-means clustering and linear regression analy

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Tian, Zhen, Lamei Pan, Pu Yin, and Rui Wang. "Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation." Algorithms 14, no. 10 (2021): 281. http://dx.doi.org/10.3390/a14100281.

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The emergence of the recommendation system has effectively alleviated the information overload problem. However, traditional recommendation systems either ignore the rich attribute information of users and items, such as the user’s social-demographic features, the item’s content features, etc., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which

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Chen, Fan, Ruoqi Hu, Jiaoxiong Xia, and Jie Tao. "Processing on Structural Data Faultage in Data Fusion." Data 5, no. 1 (2020): 21. http://dx.doi.org/10.3390/data5010021.

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With the rapid development of information technology, the development of information management system leads to the generation of heterogeneous data. The process of data fusion will inevitably lead to such problems as missing data, data conflict, data inconsistency and so on. We provide a new perspective that combines the theory in geology to conclude such kind of data errors as structural data faultage. Structural data faultages after data integration often lead to inconsistent data resources and inaccurate data information. In order to solve such problems, this article starts from the attrib

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Mehmood, Ehtisham, and Haishen Lu. "SEISMIC RESPONSE CHARACTERISTICS OF THE COAL SEAM IN THE KASHMIR BASIN BY USING MULTI-ATTRIBUTE FUSION TECHNOLOGY." Geological Behavior 8, no. 1 (2024): 27–31. https://doi.org/10.26480/gbr.01.2024.27.31.

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The increasing demand for efficient and sustainable coal extraction emphasizes the critical need for accurately characterizing coal seams. This study explores the utilization of multi-attribute seismic fusion technology to analyze the seismic response of coal seams in the Kashmir Basin. Through the application of a two-dimensional forward geological model incorporating coal layers and roadways, we extracted seismic attributes such as relative wave impedance, instantaneous amplitude, and frequency, aiming to assess their effectiveness in detecting anomalies caused by roadways within the coal se

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Sun, Yuting, and Lazzat Mukhtar. "New Media in China and Kazakhstan: The Information Agenda in the Context of Modernization." Modern Management Science & Engineering 6, no. 2 (2024): p1. http://dx.doi.org/10.22158/mmse.v6n2p1.

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Agenda-setting is a theory that examines the reconfiguration of the mass environment. Since 1968, when American communication scholars McCombs and Shaw began to systematically study the agenda-setting effect of mass communication, scholars around the world have conducted in-depth discussions on the topic and developed a number of theoretical hypotheses, including "attribute agenda-setting," "agenda fusion," "agenda setting by attributes," "agenda fusion," and "agenda setting by affiliated networks." To date, the concept of "agenda setting" remains a significant area of interest for scholars en

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Pei, Shengyu, and Xiaoping Fan. "Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness." Algorithms 15, no. 4 (2022): 120. http://dx.doi.org/10.3390/a15040120.

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Existing person re-recognition (Re-ID) methods usually suffer from poor generalization capability and over-fitting problems caused by insufficient training samples. We find that high-level attributes, semantic information, and part-based local information alignment are useful for person Re-ID networks. In this study, we propose a person re-recognition network with part-based attribute-enhanced features. The model includes a multi-task learning module, local information alignment module, and global information learning module. The ResNet based on non-local and instance batch normalization (IBN)

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Tu, Wenxuan, Sihang Zhou, Xinwang Liu, et al. "Deep Fusion Clustering Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9978–87. http://dx.doi.org/10.1609/aaai.v35i11.17198.

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Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i.e., “groundtruth” soft labels) generation. To tackle the above issues, w

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Li, Peize, Qingyi Si, Peng Fu, Zheng Lin, and Yan Wang. "Object Attribute Matters in Visual Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18545–53. http://dx.doi.org/10.1609/aaai.v38i17.29816.

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Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely through attention layers is insufficient to comprehensively understand and align information from both modalities. Intuitively, object attributes can naturally serve as a bridge to unify them, which has been overlooked in previous research. In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scen

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Jiaxin, Hu, Liu Weiwei, Cai Weiwei, Zhu Yanwei, and Huang Huan. "A decision fusion-based method for global sensitivity analysis of complicated experiments." Journal of Physics: Conference Series 2746, no. 1 (2024): 012004. http://dx.doi.org/10.1088/1742-6596/2746/1/012004.

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Abstract In real world, the relationship between experimental input and output has become more and more complex, which brings great challenges to the sensitivity analysis. This paper proposes a decision fusion based global sensitivity analysis method for complicated experiments, which not only provides quantitative evluation of the input factor influence on experimental results, but also mines the correlation and form the explicit criteria in IF-THEN fomation for further guidance. The theory of decision information system and continuous attribute discretization is presented first for transform

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Zhao, Guotao, and Jie Ding. "Image Network Teaching Resource Retrieval Algorithm Based on Deep Hash Algorithm." Scientific Programming 2021 (October 11, 2021): 1–7. http://dx.doi.org/10.1155/2021/9683908.

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In order to improve the retrieval ability of multiview attribute coded image network teaching resources, a retrieval algorithm of image network teaching resources based on depth hash algorithm is proposed. The pixel big data detection model of the multiview attribute coding image network teaching resources is constructed, the pixel information collected by the multiview attribute coding image network teaching resources is reconstructed, the fuzzy information feature components of the multiview attribute coding image are extracted, and the edge contour distribution image is combined. The distri

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Yang, Cheng, Chunxia Zhang, and Yihao Chen. "An entity alignment method with attribute augmentation and contrastive learning." Journal of Physics: Conference Series 2858, no. 1 (2024): 012049. http://dx.doi.org/10.1088/1742-6596/2858/1/012049.

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Abstract The entity alignment(EA) task is to identify entities with the same semantics in the knowledge graph(KG), an essential issue in KG fusion and big data mining. Existing entity alignment methods mainly adopt graph embedding-based methods. However, they still have some shortcomings. First, they heavily rely on high-quality alignment seed and external semantic information. Secondly, the present attention mechanism focuses on the entire graph information, neglecting the noise of attribute information. This paper proposes an EA approach based on Attribute Augmentation and Contrastive Learni

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Liu, Shuang, Man Xu, Yufeng Qin, and Niko Lukač. "Knowledge Graph Alignment Network with Node-Level Strong Fusion." Applied Sciences 12, no. 19 (2022): 9434. http://dx.doi.org/10.3390/app12199434.

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Entity alignment refers to the process of discovering entities representing the same object in different knowledge graphs (KG). Recently, some studies have learned other information about entities, but they are aspect-level simple information associations, and thus only rough entity representations can be obtained, and the advantage of multi-faceted information is lost. In this paper, a novel node-level information strong fusion framework (SFEA) is proposed, based on four aspects: structure, attribute, relation and names. The attribute information and name information are learned first, then s

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Guo, Ruiqiang, Juan Zou, Qianqian Bai, Wei Wang, and Xiaomeng Chang. "Community Detection Fusing Graph Attention Network." Mathematics 10, no. 21 (2022): 4155. http://dx.doi.org/10.3390/math10214155.

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It has become a tendency to use a combination of autoencoders and graph neural networks for attribute graph clustering to solve the community detection problem. However, the existing methods do not consider the influence differences between node neighborhood information and high-order neighborhood information, and the fusion of structural and attribute features is insufficient. In order to make better use of structural information and attribute information, we propose a model named community detection fusing graph attention network (CDFG). Specifically, we firstly use an autoencoder to learn a

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Yang, Kai, Shaoqin Liu, Junfeng Zhao, Yasha Wang, and Bing Xie. "COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 3025–32. http://dx.doi.org/10.1609/aaai.v34i03.5696.

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Entity alignment is a fundamental and vital task in Knowledge Graph (KG) construction and fusion. Previous works mainly focus on capturing the structural semantics of entities by learning the entity embeddings on the relational triples and pre-aligned "seed entities". Some works also seek to incorporate the attribute information to assist refining the entity embeddings. However, there are still many problems not considered, which dramatically limits the utilization of attribute information in the entity alignment. Different KGs may have lots of different attribute types, and even the same attr

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RODRÍGUEZ, INÉS GONZÁLEZ, JONATHAN LAWRY, and JIM F. BALDWIN. "INDUCTION AND FUSION OF FUZZY PROTOTYPES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 04 (2004): 409–46. http://dx.doi.org/10.1142/s0218488504002916.

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In the sequel, we shall be concerned with the automated induction of prototypes to represent a database in a way that combines transparency and accuracy. More precisely, our aim is to automatically summarize the information from a set of data using prototypes and simultaneously decide on the number of prototypes needed to the represent the data adequately. We propose to use fuzzy prototypes, which correspond to groupings of similar objects represented by tuples of fuzzy sets over attribute universes. In the case of numerical attributes, the universes will be discretized using linguistic variab

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Han, Ke, Xiyan Zhang, Wenlong Xu, and Long Jin. "A Text-Based Dual-Branch Person Re-Identification Algorithm Based on the Deep Attribute Information Mining Network." Symmetry 17, no. 1 (2025): 64. https://doi.org/10.3390/sym17010064.

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Text-based person re-identification enables the retrieval of specific pedestrians from a large image library using textual descriptions, effectively addressing the issue of missing pedestrian images. The main challenges in this task are to learn discriminative image–text features and achieve accurate cross-modal matching. Despite the potential of leveraging semantic information from pedestrian attributes, current methods have not yet fully harnessed this resource. To this end, we introduce a novel Text-based Dual-branch Person Re-identification Algorithm based on the Deep Attribute Information

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Liang, Wei, Zuo Chen, Ya Wen, and Weidong Xiao. "An Alert Fusion Method Based on Grey Relation and Attribute Similarity Correlation." International Journal of Online Engineering (iJOE) 12, no. 08 (2016): 25. http://dx.doi.org/10.3991/ijoe.v12i08.5958.

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Various security devices which produce a large volume of logs and alerts have been used widely. It is such a troublesome and time-consuming task for network managers to analyze and deal with the information. This paper presented an improved alerts aggregation method based on grey correlation and attribute similarity method. We used grey correlation to ascertain the importance of alert attributes in network security, and considered it as the weight of attributes. Then we combined with the attribute similarity method and calculated the overall feature similarity in order to complete alert aggreg

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Yang, Jing, Lianwei Qu, and Yong Wang. "Multidomain Fusion Data Privacy Security Framework." Wireless Communications and Mobile Computing 2021 (December 20, 2021): 1–26. http://dx.doi.org/10.1155/2021/8492223.

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With the collaborative collection of the Internet of Things (IoT) in multidomain, the collected data contains richer background knowledge. However, this puts forward new requirements for the security of data publishing. Furthermore, traditional statistical methods ignore the attributes sensitivity and the relationship between attributes, which makes multimodal statistics among attributes in multidomain fusion data set based on sensitivity difficult. To solve the above problems, this paper proposes a multidomain fusion data privacy security framework. First, based on attributes recognition, cla

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Guo, Zhenwei, Ruiqiang Zhao, Zebo Huang, Yongyan Jiang, Haojie Li, and Yingcai Deng. "Transient Voltage Information Entropy Difference Unit Protection Based on Fault Condition Attribute Fusion." Entropy 27, no. 1 (2025): 61. https://doi.org/10.3390/e27010061.

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Transient protection has the advantage of ultra-high-speed action, but traditional transient protection is susceptible to the influence of two fault condition attributes, namely, transition resistance and initial angle of fault, and there are the problems of insufficient sensitivity and insufficient reliability under weak faults. To this end, the propagation characteristics of high-frequency components of transient voltage in bus and line systems are explored, and a new method of unit protection based on the entropy difference in transient voltage information is proposed. In order to solve the

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Li, Yafang, Caiyan Jia, Xiangnan Kong, Liu Yang, and Jian Yu. "Locally Weighted Fusion of Structural and Attribute Information in Graph Clustering." IEEE Transactions on Cybernetics 49, no. 1 (2019): 247–60. http://dx.doi.org/10.1109/tcyb.2017.2771496.

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Fan, Linchuan, Xiaolong Chen, Yi Chai, and Wenyi Lin. "Attribute fusion transfer for zero-shot fault diagnosis." Advanced Engineering Informatics 58 (October 2023): 102204. http://dx.doi.org/10.1016/j.aei.2023.102204.

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Pietkiewicz, Tadeusz. "Fusion of Identification Information from ESM Sensors and Radars Using Dezert–Smarandache Theory Rules." Remote Sensing 15, no. 16 (2023): 3977. http://dx.doi.org/10.3390/rs15163977.

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This paper presents a method of fusion of identification (attribute) information provided by two types of sensors: combined primary and secondary (IFF) surveillance radars and ESMs (electronic support measures). In the first section, the basic taxonomy of attribute identification is adopted in accordance with the standards of STANAG 1241 ed. 5 and STANAG 1241 ed. 6 (draft). These standards provide the following basic values of the attribute identifications: FRIEND; HOSTILE; NEUTRAL; UNKNOWN; and additional values, namely ASSUMED FRIEND and SUSPECT. The basis of theoretical considerations is De

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Mo, Yan, Xudong Kang, Puhong Duan, Bin Sun, and Shutao Li. "Attribute filter based infrared and visible image fusion." Information Fusion 75 (November 2021): 41–54. http://dx.doi.org/10.1016/j.inffus.2021.04.005.

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Li, Zuchao, Ruhan Gong, Yineng Chen, and Kehua Su. "Fine-Grained Position Helps Memorizing More, a Novel Music Compound Transformer Model with Feature Interaction Fusion." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5203–12. http://dx.doi.org/10.1609/aaai.v37i4.25650.

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Due to the particularity of the simultaneous occurrence of multiple events in music sequences, compound Transformer is proposed to deal with the challenge of long sequences. However, there are two deficiencies in the compound Transformer. First, since the order of events is more important for music than natural language, the information provided by the original absolute position embedding is not precise enough. Second, there is an important correlation between the tokens in the compound word, which is ignored by the current compound Transformer. Therefore, in this work, we propose an improved

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Zhang, Chang Qing. "An Improved Close Value Method for Multi-Sensor Object Recognition." Applied Mechanics and Materials 707 (December 2014): 487–90. http://dx.doi.org/10.4028/www.scientific.net/amm.707.487.

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Multi-sensor information fusion problem contains many characteristic indexes, and thus it can be resolved using a multi-attribute decision making method. Information entropy is used to objectively determine the attributes weights, and thus it can overcome the subjective randomness. The aim of this paper is to develop a new multi-sensor object recognition method based on close value method. The example of part recognition proves that the proposed method is both feasible and effective.

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Yang, Shu, JingWang, Sheeraz Arif, Minli Jia, and Shunan Zhong. "SAL-Net: Self-Supervised Attribute Learning for Object Recognition and Segmentation." Wireless Communications and Mobile Computing 2021 (September 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/2891303.

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Existing attribute learning methods rely on predefined attributes, which require manual annotations. Due to the limitation of human experience, the predefined attributes are not capable enough of providing enough description. This paper proposes a self-supervised attribute learning (SAL) method, which automatically generates attribute descriptions by differentially occluding the object region to deal with the above problems. The relationship between attributes is formulated with triplet loss functions and is utilized to supervise the CNN. Attribute learning is used as an auxiliary task of a mu

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Jaradat, Ashraf, Fadi Safieddine, Aziz Deraman, Omar Ali, Ahmad Al-Ahmad, and Yehia Ibrahim Alzoubi. "A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes." Big Data and Cognitive Computing 6, no. 4 (2022): 114. http://dx.doi.org/10.3390/bdcc6040114.

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Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty managemen

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Wu, Ju, Fang Liu, Yuan Rong, Yi Liu, and Chengxi Liu. "Hesitant Fuzzy Generalised Bonferroni Mean Operators Based on Archimedean Copula for Multiple-Attribute Decision-Making." Mathematical Problems in Engineering 2020 (November 26, 2020): 1–16. http://dx.doi.org/10.1155/2020/8712376.

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Information fusion is an important part of multiple-attribute decision-making, and aggregation operator is an important tool of decision information fusion. Integration operators in a variety of fuzzy information environments have a slight lack of consideration for the correlation between variables. Archimedean copula provides information fusion patterns that rely on the intrinsic relevance of information. This paper extends the Archimedean copula to the aggregation of hesitant fuzzy information. Firstly, the Archimedean copula is used to generate the operation rules of the hesitant fuzzy elem

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Gu, Junlin, Weiwei Liu, and Xiong Yang. "An attribute-enhanced relationship-aware neighborhood matching model with dual attention." PLOS One 20, no. 6 (2025): e0324290. https://doi.org/10.1371/journal.pone.0324290.

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The entity alignment task aims to match semantically corresponding entities in different knowledge graphs, which is important for knowledge fusion. Traditional graph-based methods often lose information due to insufficient use of attributes and imperfect relationship modeling, which makes it difficult to capture the deep semantic relationship between entities fully. To improve the effect of entity alignment, we propose a new model named ARNM-DAE2A, which strengthens the information aggregation capability of GCN by introducing a dual-attention mechanism to ensure a more balanced and comprehensi

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Ke, Jingying. "Design and Research of Economic Management Problem Fusion Method Based on Decision Information System." Security and Communication Networks 2022 (July 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/8778545.

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The construction of the economic management information system ensures the flow and sharing of information, business coordination, and scientific decision-making between economic functions. Therefore, project construction is absolutely necessary and urgent. Project management has achieved significant results in terms of shortening project time and reducing management costs. Information system is an important model information structure in artificial intelligence. This paper focuses on the design of a fusion method for economic management problems based on decision information systems. This pap

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Yin, Chuan, Binyu Zhang, Wanzeng Liu, et al. "Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis." ISPRS International Journal of Geo-Information 11, no. 7 (2022): 360. http://dx.doi.org/10.3390/ijgi11070360.

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Expansion of the entity attribute information of geographic knowledge graphs is essentially the fusion of the Internet’s encyclopedic knowledge. However, it lacks structured attribute information, and synonymy and polysemy always exist. These reduce the quality of the knowledge graph and cause incomplete and inaccurate semantic retrieval. Therefore, we normalize the attributes of a geographic knowledge graph based on optimal granularity clustering and co-occurrence analysis, and use structure and the semantic relation of the entity attributes to identify synonymy and correlation between attrib

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Wang, Jinghong, Zhixia Zhou, Bi Li, and Mancai Wu. "Attribute Network Representation Learning with Dual Autoencoders." Symmetry 14, no. 9 (2022): 1840. http://dx.doi.org/10.3390/sym14091840.

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The purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. The current network representation learning methods have insufficient interaction with structure when learning attribute information, and the structure and attribute information cannot be well integrated. In this paper, we propose an attribute network representation learning method for dual-channel autoencoder. One channel is for the network structure, and adopting the multi-hop attention mechanism is used to capture the

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Zhou, Mingqiang, Yihan Kong, Shenshen Zhang, Dan Liu, and Haijiang Jin. "The Deep Fusion of Topological Structure and Attribute Information for Link Prediction." IEEE Access 8 (2020): 34398–406. http://dx.doi.org/10.1109/access.2020.2974016.

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Ren, Yanze, Yan Liu, Jing Chen, Xiaoyu Guo, Junyu Shi, and Mengmeng Jia. "News Stance Discrimination Based on a Heterogeneous Network of Social Background Information Fusion." Entropy 25, no. 1 (2022): 78. http://dx.doi.org/10.3390/e25010078.

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Media with partisan tendencies publish news articles to support their preferred political parties to guide the direction of public opinion. Therefore, discovering political bias in news texts has important practical significance for national election prediction and public opinion management. Some biased news often has obscure expressions and ambiguous writing styles. By bypassing the language model, the accuracy of methods that rely on news semantic information for position discrimination is low. This manuscript proposes a news standpoint discrimination method based on social background inform

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Chen, Zhilong, Renyi Wang, Biao Xu, and Jianghang Zhu. "Research on Oil and Gas-Bearing Zone Prediction and Identification Based on the SVD–K-Means Algorithm—A Case Study of the WZ6-1 Oil-Bearing Structure in the Beibu Gulf Basin, South China Sea." Energies 17, no. 22 (2024): 5771. http://dx.doi.org/10.3390/en17225771.

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The WZ6-1 oil-bearing structure in the Beibu Gulf Basin of the South China Sea has well-developed faults with significant variations in fault sealing capacity, resulting in a complex and highly variable distribution of oil, gas, and water, and limited understanding of hydrocarbon accumulation patterns. Traditional methods, such as single seismic attributes and linear fusion of multiple seismic attributes, have proven ineffective in identifying and predicting oil and gas-bearing areas in this region, leading to five unsuccessful wells. Through comprehensive analysis of drilled wells and seismic

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Wang, Chao, Hui Liu, Yi Shen, Kaiguang Zhao, Hongyan Xing, and Haotian Wu. "High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion." Complexity 2020 (March 23, 2020): 1–17. http://dx.doi.org/10.1155/2020/8360361.

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Change detection (CD) is essential for accurate understanding of land surface changes with multitemporal Earth observation data. Due to the great advantages in spatial information modeling, Morphological Attribute Profiles (MAPs) are becoming increasingly popular for improving the recognition ability in CD applications. However, most of the MAPs-based CD methods are implemented by setting the scale parameters of Attribute Profiles (APs) manually and ignoring the uncertainty of change information from different sources. To address these issues, a novel method for CD in high-resolution remote se

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Lu, Yuan Zhang, and Bing Zhang. "Based on Data Fusion Intelligent Traffic Information Analysis and Optimization." Applied Mechanics and Materials 568-570 (June 2014): 831–34. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.831.

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In this paper, we propose an analysis refine scheme based on data fusion towards some existing problems in data analysis of intelligent transportation systems .This method constructed the data into a plurality of time-series according to the characteristics of each attribute data. Providing an objective scientific basis for dynamic traffic management through intelligent analysis of traffic information based on the gray advantage analysis among data and system model of Intelligent Traffic Information decision support and auxiliary decision analysis.

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Wang, Yaru, Lilong Feng, Xiaoke Song, Dawei Xu, and Yongjie Zhai. "Zero-Shot Image Classification Method Based on Attention Mechanism and Semantic Information Fusion." Sensors 23, no. 4 (2023): 2311. http://dx.doi.org/10.3390/s23042311.

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The zero-shot image classification (ZSIC) is designed to solve the classification problem when the sample is very small, or the category is missing. A common method is to use attribute or word vectors as a priori category features (auxiliary information) and complete the domain transfer from training of seen classes to recognition of unseen classes by building a mapping between image features and a priori category features. However, feature extraction of the whole image lacks discrimination, and the amount of information of single attribute features or word vector features of categories is ins

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Liu, Yang, Feng Hou, Yunjie Peng, et al. "DoGA: Enhancing Grounded Object Detection via Grouped Pre-Training with Attributes." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 5658–66. https://doi.org/10.1609/aaai.v39i6.32603.

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Recent advances in vision-language pre-training have significantly enhanced the model capabilities on grounded object detection. However, these studies often pre-train with coarse-grained text prompts, such as plain category names and brief grounded phrases. This limitation curtails the model's capacity for fine-grained linguistic comprehension and leads to a significant decline in performance when faced with detailed descriptions or contextual information. To tackle these problems, we develop DoGA: Detect objects with Grouped Attributes, which employs commonly apparent attributes to bridge di

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Huang, Xiaoli, Haibo Chen, and Zheng Zhang. "Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information." Entropy 25, no. 2 (2023): 361. http://dx.doi.org/10.3390/e25020361.

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Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into embedded vectors (FPHD). The design uses the hash method to quickly extract entity features, and uses a deep neural network to learn the implicit association between entity features. This design solves two main problems in large-scale dynamic data addition: (1) The linear growth of the size of the embedded vector tab

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Zhai, Xianfeng. "Dance Movement Recognition Based on Feature Expression and Attribute Mining." Complexity 2021 (April 30, 2021): 1–12. http://dx.doi.org/10.1155/2021/9935900.

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There are complex posture changes in dance movements, which lead to the low accuracy of dance movement recognition. And none of the current motion recognition uses the dancer’s attributes. The attribute feature of dancer is the important high-level semantic information in the action recognition. Therefore, a dance movement recognition algorithm based on feature expression and attribute mining is designed to learn the complicated and changeable dancer movements. Firstly, the original image information is compressed by the time-domain fusion module, and the information of action and attitude can

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Liang, HongYan. "Intelligent Tourism Personalized Recommendation Based on Multi-Fusion of Clustering Algorithms." Advances in Multimedia 2021 (December 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/4517973.

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Actual tourism mining models are often used to discover potential information in documents, but tourism models without human knowledge often produce unexplainable topics. This paper combines big data technology to build a personalized recommendation system for smart tourism, model the contextual information usage ontology under the tourism information system, and give the association between various ontologies. Then, this paper uses a matrix to describe each discrete attribute and interval attribute and uses a vector to model the user’s preferences. In addition, this paper constructs an intell

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