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Relevant bibliographies by topics / Attribute information fusion

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Academic literature on the topic 'Attribute information fusion'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Attribute information fusion"

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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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Dissertations / Theses on the topic "Attribute information fusion"

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Chen, Joyce I. Z., and 陳雍宗. "Applying Kinematic Information and Target Attributes In a Multi- Sensor Fusion Algorithm." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/84470162148407318602.

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Book chapters on the topic "Attribute information fusion"

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Ding, Huilin, Yuzhou Gong, Shoudong Han, et al. "Pedestrian Attribute Distillation Fusion Model." In Proceedings of the 6th International Conference on Informatics Engineering and Information Science (ICIEIS 2024). Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1108-9_24.

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Mönks, Uwe. "Multilayer Attribute-based Conflict-reducing Observation." In Information Fusion Under Consideration of Conflicting Input Signals. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-53752-7_4.

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Su, Zhan, Zeshui Xu, and Shen Zhang. "Multi-Attribute Decision-Making Method Based on Probabilistic Hesitant Fuzzy Entropy." In Hesitant Fuzzy and Probabilistic Information Fusion. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3140-4_4.

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Chen, Qiuhong, Caimao Li, Hao Lin, Hao Li, and Yuquan Hou. "User Attribute Prediction Method Based on Stacking Multimodel Fusion." In Communications in Computer and Information Science. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5209-8_12.

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Socievole, Annalisa, and Clara Pizzuti. "Kernel-based Early Fusion of Structure and Attribute Information for Detecting Communities in Attributed Networks." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31183-3_12.

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Ding, Zhengyan, and Yanfeng Shang. "Pedestrian Attribute Recognition Method Based on Multi-source Teacher Model Fusion." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0856-1_2.

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Dang, Qian, Bo Zhao, Biying Sun, Yu Qiu, and Chunhui Du. "A Secure Image-Video Retrieval Scheme with Attribute-Based Encryption and Multi-feature Fusion in Smart Grid." In Communications in Computer and Information Science. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7769-5_12.

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Lin, Ronghua, Yong Tang, Chengzhe Yuan, Chaobo He, and Weisheng Li. "SCHOLAT Link Prediction: A Link Prediction Dataset Fusing Topology and Attribute Information." In Computer Supported Cooperative Work and Social Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4549-6_26.

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Carletti, Vincenzo, Rosario Di Lascio, Pasquale Foggia, and Mario Vento. "A Semantic Reasoner Using Attributed Graphs Based on Intelligent Fusion of Security Multi-sources Information." In Activity Monitoring by Multiple Distributed Sensing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13323-2_7.

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Tang, Minghu. "A Joint Weighted Nonnegative Matrix Factorization Model via Fusing Attribute Information for Link Prediction." In Mobile Multimedia Communications. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23902-1_15.

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Conference papers on the topic "Attribute information fusion"

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Liu, Jie, and Shaorong Xie. "Multi-Source Information Fusion Navigation Based on Dynamic Attribute Scene Graphs." In 2024 2nd International Conference on Artificial Intelligence and Automation Control (AIAC). IEEE, 2024. https://doi.org/10.1109/aiac63745.2024.10899632.

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Pan, Weiqiang, Dan Chen, Yiqin Lu, Jiarui Chen, and Jiancheng Qin. "Knowledge Graph Embedding Model with Attribute Information Fusion in Hybrid Space." In 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2025. https://doi.org/10.1109/icaibd64986.2025.11082078.

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Wan, Rui, Xiaohan Sun, Jiaqi Zhang, and Yan Liang. "Design of Data lineage Analysis Framework Based on Multivariate Attribute Fusion." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10868210.

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Du, Jinlian, Xiaolin Du, Zhenwei Lu, and Xiao Zhang. "Extraction of Chinese Medical Entity Attribute Values Based on Multi-type Feature Fusion." In 2025 8th International Conference on Information and Computer Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64582.2025.00085.

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Chen, Yanru, Senzhi Chai, Wentao Xu, Pu Yang, and Yixuan Lu. "Aggregation Method for Electric Vehicles with Multi-Source Information Fusion Including Social Attributes." In 2024 Second International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE). IEEE, 2024. http://dx.doi.org/10.1109/iccsie61360.2024.10698089.

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Song, Tiexu, Xiaonian Wang, and Ruizhi Sha. "A fusion lossy and lossless data compression method based on signal attributes." In 2024 7th International Conference on Data Science and Information Technology (DSIT). IEEE, 2024. https://doi.org/10.1109/dsit61374.2024.10881307.

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Tao, Yan, and Han Chongzhao. "Entropy based attribute reduction approach for incomplete decision table." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009752.

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Bosse, Eloi, and Marc-Alain Simard. "Identity and attribute information fusion using evidential reasoning." In AeroSense '97, edited by Belur V. Dasarathy. SPIE, 1997. http://dx.doi.org/10.1117/12.276128.

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Li, Zhe, and Junping Zhang. "Ship formation detection based on spatial distribution and attribute information." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXX, edited by Lynne L. Grewe, Erik P. Blasch, and Ivan Kadar. SPIE, 2021. http://dx.doi.org/10.1117/12.2587561.

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Yi, Shanzhen, Zhongqian Tang, and Yangfan Xiao. "Multiple sources geographic attribute data uncertainty and information fusion schemes." In 2017 25th International Conference on Geoinformatics. IEEE, 2017. http://dx.doi.org/10.1109/geoinformatics.2017.8090909.

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Reports on the topic "Attribute information fusion"

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Bar-Shalom, Yaakov. Network Level Association and Fusion of Kinematic and Attribute Information. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada545335.

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