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Journal articles on the topic 'Collaborative Fusion'

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Author: Grafiati

Published: 27 January 2024

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1

Liu, Jing, Xuesong Hai, and Keqin Li. "TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts." Future Internet 16, no. 1 (December 25, 2023): 6. http://dx.doi.org/10.3390/fi16010006.

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Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data directly. Data resources are difficult to aggregate effectively, resulting in a lack of support for model training. How to collaborate between data sources in order to aggregate the value of data resources is therefore an important research question. However, existing distributed-collaborative-learning architectures still face serious challenges in collaborating between nodes that lack mutual trust, with security and trust issues seriously affecting the confidence and willingness of data sources to participate in collaboration. Blockchain technology provides trusted distributed storage and computing, and combining it with collaboration between data sources to build trusted distributed-collaborative-learning architectures is an extremely valuable research direction for application. We propose a trusted distributed-collaborative-learning mechanism based on blockchain smart contracts. Firstly, the mechanism uses blockchain smart contracts to define and encapsulate collaborative behaviours, relationships and norms between distributed collaborative nodes. Secondly, we propose a model-fusion method based on feature fusion, which replaces the direct sharing of local data resources with distributed-model collaborative training and organises distributed data resources for distributed collaboration to improve model performance. Finally, in order to verify the trustworthiness and usability of the proposed mechanism, on the one hand, we implement formal modelling and verification of the smart contract by using Coloured Petri Net and prove that the mechanism satisfies the expected trustworthiness properties by verifying the formal model of the smart contract associated with the mechanism. On the other hand, the model-fusion method based on feature fusion is evaluated in different datasets and collaboration scenarios, while a typical collaborative-learning case is implemented for a comprehensive analysis and validation of the mechanism. The experimental results show that the proposed mechanism can provide a trusted and fair collaboration infrastructure for distributed-collaboration nodes that lack mutual trust and organise decentralised data resources for collaborative model training to develop effective global models.

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2

P.C. Cheung, Patti, and Maria L.C. Lau. "From union catalogue to fusion catalogue." Library Management 35, no. 1/2 (January 7, 2014): 88–101. http://dx.doi.org/10.1108/lm-04-2013-0031.

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Purpose – The purpose of this paper is to reflect The Chinese University of Hong Kong Library's catalogue evolution as a result of electronic resources cataloguing and how collaborative cataloguing could be implemented in the context of Hong Kong. Design/methodology/approach – The paper outlines the challenges faced by The Chinese University of Hong Kong Library and the need to find alternative way to catalogue e-books come in large batches. It describes in particular the cataloguing of Chinese e-books in collaboration with the China Academic Library and Information System (CALIS). Findings – Different cataloguing data set are inevitably blended into the library catalogue to be used by users. Still, collaboration is feasible when libraries are ready to make compromise and accept variances in the library catalogue. Originality/value – The Chinese University of Hong Kong Library is the first library in Hong Kong to work collaboratively with CALIS to batch convert its records for cataloguing of Chinese e-books. The paper is useful for librarians exploring new source for Chinese cataloguing or collaborative initiatives with libraries in China.

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Arshad, Kamran, Muhammad Ali Imran, and Klaus Moessner. "Collaborative Spectrum Sensing Optimisation Algorithms for Cognitive Radio Networks." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/424036.

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The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance.

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4

Benli, Emrah, Richard Lee Spidalieri, and Yuichi Motai. "Thermal Multisensor Fusion for Collaborative Robotics." IEEE Transactions on Industrial Informatics 15, no. 7 (July 2019): 3784–95. http://dx.doi.org/10.1109/tii.2019.2908626.

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5

Biswas, Pratik K., Hairong Qi, and Yingyue Xu. "Mobile-agent-based collaborative sensor fusion." Information Fusion 9, no. 3 (July 2008): 399–411. http://dx.doi.org/10.1016/j.inffus.2007.09.001.

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6

Liu, Guohua, Juan Guan, Haiying Liu, and Chenlin Wang. "Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion." Mathematical Problems in Engineering 2020 (August 27, 2020): 1–16. http://dx.doi.org/10.1155/2020/5819409.

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Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.

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7

Kant, Surya, and Tripti Mahara. "Nearest biclusters collaborative filtering framework with fusion." Journal of Computational Science 25 (March 2018): 204–12. http://dx.doi.org/10.1016/j.jocs.2017.03.018.

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8

Hongxing Wang and Junsong Yuan. "Collaborative Multifeature Fusion for Transductive Spectral Learning." IEEE Transactions on Cybernetics 45, no. 3 (March 2015): 451–61. http://dx.doi.org/10.1109/tcyb.2014.2327960.

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9

Wang, Heyong, Ming Hong, and Jinjiong Lan. "Study on Collaborative Filtering Recommendation Model Fusing User Reviews." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 5 (September 20, 2019): 864–73. http://dx.doi.org/10.20965/jaciii.2019.p0864.

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The traditional collaborative filtering model suffers from high-dimensional sparse user rating information and ignores user preference information contained in user reviews. To address the problem, this paper proposes a new collaborative filtering model UL_SAM (UBCF_LDA_SIMILAR_ADD_MEAN) which integrates topic model with user-based collaborative filtering model. UL_SAM extracts user preference information from user reviews through topic model and then fuses user preference information with user rating information by similarity fusion method to create fusion information. UL_SAM creates collaborative filtering recommendations according to fusion information. It is the advantage of UL_SAM on improving recommendation effectiveness that UL_SAM enriches information for collaborative recommendation by integrating user preference with user rating information. Experimental results of two public datasets demonstrate significant improvement on recommendation effectiveness in our model.

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Chen Cheng, Haibin Lv, and Zhihan Lv. "Sensing fusion in vehicular network digital twins for 6G smart city." ITU Journal on Future and Evolving Technologies 3, no. 2 (June 3, 2022): 342–58. http://dx.doi.org/10.52953/cofv5663.

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The aims demonstrated in this article are to effectively monitor the complex road environment in smart city transportation using the sixth generation mobile communication technology (6G) Digital Twins (DTs), to perceive the complex road environment of smart city traffic. Vehicular Networks (VN) in the smart transportation system have been selected as the research object, and the multi-sensor collaboration and fusion technology in the network is explored, so as to meet the active control requirements of intelligent vehicles. A lidar and camera fusion-based segmentation network C-LNet is proposed. The structure of a C-LNet multi-sensing data fusion segmentation network is double encoder-single decoder. Two encoders are used to extract image features and lidar features respectively. The same heterogeneous data is realized through the synchronization of lidar point cloud data and image data in sensor space. For multimodal information, a multiscale feature fusion-based vehicle collaboration method is designed. In the simulation experiment part, the C-LNet multi-sensing data fusion segmentation network is verified on the KITTI data set. The accuracy, F1 value, and MIoU of C-LNet are 98.4%, 96.7%, and 94.51%, respectively, which are better than those of an RGB network and lidar network. In summary, the smart transportation system supported by DTs in a 6G environment is explored. The proposed VN sensing fusion method can effectively realize the collaborative positioning perception of multiple vehicles, which lays the foundation for the realization of complex collaborative decision-making and control in smart transportation.

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Anwar, Khoirul. "Collaborative Edmodo in writing: A conceivable course of fusion." Cypriot Journal of Educational Sciences 16, no. 3 (June 30, 2021): 1073–87. http://dx.doi.org/10.18844/cjes.v16i3.5823.

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A problem that still persists in Collaborative Writing is the lack of use of relevant technology to counter lessened interaction and learning participations in developing writing skills effectively. To offset these difficulties, this study examines the use of Edmodo on students' collaborative writing. This study used a quasi-experimental study of 56 students, grade 10, Gresik, Indonesia, with two assigned classes of experimental and the control group, each containing of 28 students. The results of this study reveal that there is a significant influence of Edmodo on Collaborative writing, evidenced by the results of sig. (2-tailed) is 0,000 (lower than 0.05). Edmodo has proven to be a dependable means when merged with a Collaborative Writing strategy, and has also been attested to reassure student participation and interaction. Suggestions and further research basis are also presented, especially to innovative scholars as treasured opportunities for accompanying enquiry to pay more courtesy to the progress of Collaborative Writing which is increasingly unlocked to always re-join hearty users’ wants. Key Words : Collaborative, Writing, Edmodo,Technology, Course of Fusion

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12

Sunil, Sooraj, Saeed Mozaffari, Rajmeet Singh, Behnam Shahrrava, and Shahpour Alirezaee. "Feature-Based Occupancy Map-Merging for Collaborative SLAM." Sensors 23, no. 6 (March 14, 2023): 3114. http://dx.doi.org/10.3390/s23063114.

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One of the most frequently used approaches to represent collaborative mapping are probabilistic occupancy grid maps. These maps can be exchanged and integrated among robots to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such map fusion requires solving the unknown initial correspondence problem. This article presents an effective feature-based map fusion approach that includes processing the spatial occupancy probabilities and detecting features based on locally adaptive nonlinear diffusion filtering. We also present a procedure to verify and accept the correct transformation to avoid ambiguous map merging. Further, a global grid fusion strategy based on the Bayesian inference, which is independent of the order of merging, is also provided. It is shown that the presented method is suitable for identifying geometrically consistent features across various mapping conditions, such as low overlapping and different grid resolutions. We also present the results based on hierarchical map fusion to merge six individual maps at once in order to constrict a consistent global map for SLAM.

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Li, Yiting, Liyuan Sun, Jianping Gou, Lan Du, and Weihua Ou. "Feature fusion-based collaborative learning for knowledge distillation." International Journal of Distributed Sensor Networks 17, no. 11 (November 2021): 155014772110570. http://dx.doi.org/10.1177/15501477211057037.

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Deep neural networks have achieved a great success in a variety of applications, such as self-driving cars and intelligent robotics. Meanwhile, knowledge distillation has received increasing attention as an effective model compression technique for training very efficient deep models. The performance of the student network obtained through knowledge distillation heavily depends on whether the transfer of the teacher’s knowledge can effectively guide the student training. However, most existing knowledge distillation schemes require a large teacher network pre-trained on large-scale data sets, which can increase the difficulty of knowledge distillation in different applications. In this article, we propose a feature fusion-based collaborative learning for knowledge distillation. Specifically, during knowledge distillation, it enables networks to learn from each other using the feature/response-based knowledge in different network layers. We concatenate the features learned by the teacher and the student networks to obtain a more representative feature map for knowledge transfer. In addition, we also introduce a network regularization method to further improve the model performance by providing a positive knowledge during training. Experiments and ablation studies on two widely used data sets demonstrate that the proposed method, feature fusion-based collaborative learning, significantly outperforms recent state-of-the-art knowledge distillation methods.

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Liu, Hongyi, Tongtong Fang, Tianyu Zhou, and Lihui Wang. "Towards Robust Human-Robot Collaborative Manufacturing: Multimodal Fusion." IEEE Access 6 (2018): 74762–71. http://dx.doi.org/10.1109/access.2018.2884793.

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15

OYAMA, By Yukio, and Hiroshi MAEKAWA. "JAERI/USDOE Collaborative Program on Fusion Blanket Neutronics." Journal of the Atomic Energy Society of Japan / Atomic Energy Society of Japan 36, no. 7 (1994): 611–18. http://dx.doi.org/10.3327/jaesj.36.611.

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Cabero Zabalaga, Marco. "A New Blueprint of Collaboration the Science Culture Construction fostering innovation and green development." Journal of Latin American Sciences and Culture 5, no. 8 (December 27, 2023): 99–109. http://dx.doi.org/10.52428/27888991.v5i8.1075.

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In an era defined by the urgency of environmental challenges, a transformative paradigm is emerging: a fusion of collaboration and building scientific culture that catalyzes innovation for sustainable green development. This brief explores the dynamic interplay between collaboration and science culture, emphasizing their collective impact on fostering innovative solutions to propel us towards a greener, more sustainable future. The new paradigm of collaboration transcends disciplinary boundaries, fostering the convergence of diverse perspectives and experiences. This brief argues that cross-pollination of ideas and skills, facilitated by collaborative efforts, cultivates an environment conducive to innovation. The synthesis of collective intelligence and the establishment of a collaborative ecosystem become essential drivers to address complex environmental problems, leading to novel solutions in the field of green development.

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Wang, Hao, Yadi Song, Peng Mi, and Jianyong Duan. "The Collaborative Filtering Method Based on Social Information Fusion." Mathematical Problems in Engineering 2019 (April 2, 2019): 1–9. http://dx.doi.org/10.1155/2019/9387989.

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In the social network, similar users are assumed to prefer similar items, so searching the similar users of a target user plays an important role for most collaborative filtering methods. Existing collaborative filtering methods use user ratings of items to search for similar users. Nowadays, abundant social information is produced by the Internet, such as user profiles, social relationships, behaviors, interests, and so on. Only using user ratings of items is not sufficient to recommend wanted items and search for similar users. In this paper, we propose a new collaborative filtering method using social information fusion. Our method first uses social information fusion to search for similar users and then updates the user rating of items for recommendation using similar users. Experiments show that our method outperforms the existing methods based on user ratings of items and using social information fusion to search similar users is an available way for collaborative filtering methods of recommender systems.

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18

Song, Xiyu, Ying Zeng, Li Tong, Jun Shu, Qiang Yang, Jian Kou, Minghua Sun, and Bin Yan. "A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets." Computational Intelligence and Neuroscience 2022 (January 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/4752450.

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The superiority of collaborative brain-computer interface (cBCI) in performance enhancement makes it an effective way to break through the performance bottleneck of the BCI-based dynamic visual target detection. However, the existing cBCIs focus on multi-mind information fusion with a static and unidirectional mode, lacking the information interaction and learning guidance among multiple agents. Here, we propose a novel cBCI framework to enhance the group detection performance of dynamic visual targets. Specifically, a mutual learning domain adaptation network (MLDANet) with information interaction, dynamic learning, and individual transferring abilities is developed as the core of the cBCI framework. MLDANet takes P3-sSDA network as individual network unit, introduces mutual learning strategy, and establishes a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level. The results indicate that the proposed MLDANet-cBCI framework can achieve the best group detection performance, and the mutual learning strategy can improve the detection ability of individual networks. In MLDANet-cBCI, the F1 scores of collaborative detection and individual network are 0.12 and 0.19 higher than those in the multi-classifier cBCI, respectively, when three minds collaborate. Thus, the proposed framework breaks through the traditional multi-mind collaborative mode and exhibits a superior group detection performance of dynamic visual targets, which is also of great significance for the practical application of multi-mind collaboration.

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19

Zhang, Shenfu, Xiangchao Meng, Qiang Liu, Gang Yang, and Weiwei Sun. "Feature-Decision Level Collaborative Fusion Network for Hyperspectral and LiDAR Classification." Remote Sensing 15, no. 17 (August 24, 2023): 4148. http://dx.doi.org/10.3390/rs15174148.

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The fusion-based classification of hyperspectral (HS) and light detection and ranging (LiDAR) images has become a prominent research topic, as their complementary information can effectively improve classification performance. The current methods encompass pixel-, feature- and decision-level fusion. Among them, feature- and decision-level fusion have emerged as the mainstream approaches. Collaborative fusion of these two levels can enhance classification accuracy. Although various methods have been proposed, some shortcomings still exist. On one hand, current methods ignore the shared advanced features between HS and LiDAR images, impeding the integration of multimodal features and thereby limiting the classification performance. On the other hand, the existing methods face difficulties in achieving a balance between feature- and decision-level contributions, or they simply overlook the significance of one level and fail to utilize it effectively. In this paper, we propose a novel feature-decision level collaborative fusion network (FDCFNet) for hyperspectral and LiDAR classification to alleviate these problems. Specifically, a multilevel interactive fusion module is proposed to indirectly connect hyperspectral and LiDAR flows to refine the spectral-elevation information. Moreover, the fusion features of the intermediate branch can further enhance the shared-complementary information of hyperspectral and LiDAR to reduce the modality differences. In addition, a dynamic weight selection strategy is meticulously designed to adaptively assign weight to the output of three branches at the decision level. Experiments on three public benchmark datasets demonstrate the effectiveness of the proposed methods.

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Naidu, Vegi M., Vijay P. S. Rawat, Christina Schessl, Konstantin Petropoulus, Monica Cusan, Aniruddha J. Deshpande, Leticia Quintanilla Martinez, Wolfgang Hiddemann, Michaela Feuring Buske, and Christian Buske. "AML1-ETO Collaborates with the Homeobox Gene Meis1 in Inducing Acute Leukemia in the Mouse Bone Marrow Transplantation Model." Blood 112, no. 11 (November 16, 2008): 928. http://dx.doi.org/10.1182/blood.v112.11.928.928.

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Abstract AML1-ETO is the most frequent fusion gene in human AML. Previously, we and others have demonstrated that the fusion is not able to cause leukaemia on its own in experimental murine models, but that it needs collaborative partners. However, although mutations such as the FLT3-length mutation and C-KIT mutations were defined as important collaborative genetic events in AML1-ETO positive AML, most human AML1-ETO cases do not carry these mutations, indicating the presence of unkown collaborative partners in these patients. On the other hand Meis1, a HOX gene co-factor, belonging to the TALE family of homeodomain proteins, has a well established function as a protooncogene with a strong collaborative potential in Hox gene associated AML in mice. First we confirmed expression of MEIS1 in some patients with AML1-ETO positive AML by real-time PCR. Based on this we sought to determine if AML1-ETO can collaborate with Meis1 in inducing acute leukemias: single constructs or both genes were co-transfected in 5-FU treated primary murine bone marrow cells by retroviral gene transfer, using MSCV retroviral constructs with an IRES–GFP or YFP cassette. Mice were transplanted with BM cells expressing Meis1 alone (n=10), with BM cells solely expressing the fusion gene (n=10) or EGFP (n=7, control) or with BM expressing both genetic alterations (n=14). None of the mice in the Meis1 and AML1-ETO as well as in the control group developed disease. In contrast, 14 mice transplanted with BM co-expressing AML1-ETO and Meis1 developed lethal disease after a median latency of 102 days. Three mice succumbed to a myeloproliferative syndrome and nine mice died by acute leukemia (6 mice developed AML, 3 mice ALL), which was serially transplantable into secondary recipients (median = 57 days). Immunohistochemistry of various organs of leukemic mice showed massive infiltration with blast cells. In MPS and AML 85 ± 9.3 % of the blast cells co-expressed Gr-1+ and Mac1+. In ALL cases 40 ± 19.9 % of the malignant cells co-expressed Mac1 and the lymphoid-associated B220 antigen. Analysis of retroviral integration did not reveal recurrent integration sites as an indication for insertional mutagenesis. In summary, our data demonstrate for the first time that AML1-ETO can collaborate with Meis1 and identify a novel collaborative partner in t(8;21) positive AML. Furthermore, our analyses demonstrate that Meis1 can collaborate with non-homeobox genes in inducing acute leukemia.

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Ma, Meng. "Construction and Realization Path Analysis of Cross-Border E-Commerce Logistics Collaboration Model." Advances in Multimedia 2022 (August 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/7758785.

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In order to improve the synergy effect of cross-border e-commerce slogistics, this paper combines intelligent logistics communication technology to construct a cross-border e-commerce collaborative logistics model. Moreover, this paper proposes a parallel energy-efficient routing algorithm based on 3D cell space to improve the system operation effect. In addition, this paper deeply analyzes the data fusion function performed by MA when accessing nodes and proposes a data fusion criterion to judge the necessity of data fusion on nodes. Finally, this paper balances the energy consumption of e-commerce logistics data fusion and data transmission so that the sum of the two is minimized. The data simulation analysis shows that the cross-border e-commerce logistics collaboration model proposed in this paper has certain effects, and it can be applied to the actual operation of cross-border e-commerce.

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22

Nilam, Waverly. "Fusion of Ornamental Art and Architectural Design: Exploring the Interplay and Creation of Unique Spatial Experiences." Studies in Art and Architecture 2, no. 3 (September 2023): 10–27. http://dx.doi.org/10.56397/saa.2023.09.03.

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This article delves into the intricacies of integrating ornamental art and architectural design, highlighting the challenges, opportunities, and future prospects associated with this fusion. It explores the delicate balance required to harmonize aesthetics and functionality, while also addressing the importance of preserving cultural heritage. Effective collaboration between artists, architects, and stakeholders is emphasized as a key factor in successfully integrating these disciplines. The article also examines the potential opportunities for enhanced aesthetics, cultural expression, and sustainable design that arise from this fusion. However, it acknowledges the need for careful consideration of cultural sensitivity and the development of collaborative approaches. Ultimately, the article underscores the potential impact of this fusion on the field of architecture and society as a whole, shaping the future of architectural design.

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MAO Ning, 毛宁, 杨德东 YANG De-dong, 杨福才 YANG Fu-cai, and 蔡玉柱 CAI Yu-zhu. "Multi-template collaborative correlation tracking with multi-feature fusion." Chinese Journal of Liquid Crystals and Displays 32, no. 2 (2017): 153–62. http://dx.doi.org/10.3788/yjyxs20173202.0153.

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Jung, Roland, and Stephan Weiss. "Modular Multi-Sensor Fusion: A Collaborative State Estimation Perspective." IEEE Robotics and Automation Letters 6, no. 4 (October 2021): 6891–98. http://dx.doi.org/10.1109/lra.2021.3096165.

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Dey, I., D. Ciuonzo, and P. Salvo Rossi. "Wideband Collaborative Spectrum Sensing Using Massive MIMO Decision Fusion." IEEE Transactions on Wireless Communications 19, no. 8 (August 2020): 5246–60. http://dx.doi.org/10.1109/twc.2020.2991113.

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Lin, Daw-Tung, and Kai-Yung Huang. "Collaborative Pedestrian Tracking and Data Fusion With Multiple Cameras." IEEE Transactions on Information Forensics and Security 6, no. 4 (December 2011): 1432–44. http://dx.doi.org/10.1109/tifs.2011.2159972.

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Keahey, K., M. E. Papka, Q. Peng, D. Schissel, G. Abla, T. Araki, J. Burruss, et al. "Grid Support for Collaborative Control Room in Fusion Science." Cluster Computing 8, no. 4 (October 2005): 305–11. http://dx.doi.org/10.1007/s10586-005-4097-z.

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Yu, Guoxian, Yuehui Wang, Jun Wang, Carlotta Domeniconi, Maozu Guo, and Xiangliang Zhang. "Attributed heterogeneous network fusion via collaborative matrix tri-factorization." Information Fusion 63 (November 2020): 153–65. http://dx.doi.org/10.1016/j.inffus.2020.06.012.

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Wang, Lili, Ting Shi, and Shijin Li. "Research on the Application of User Recommendation Based on the Fusion Method of Spatially Complex Location Similarity." Complexity 2021 (April 17, 2021): 1–8. http://dx.doi.org/10.1155/2021/9998948.

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Since the user recommendation complex matrix is characterized by strong sparsity, it is difficult to correctly recommend relevant services for users by using the recommendation method based on location and collaborative filtering. The similarity measure between users is low. This paper proposes a fusion method based on KL divergence and cosine similarity. KL divergence and cosine similarity have advantages by comparing three similar metrics at different K values. Using the fusion method of the two, the user’s similarity with the preference is reused. By comparing the location-based collaborative filtering (LCF) algorithm, user-based collaborative filtering (UCF) algorithm, and user recommendation algorithm (F2F), the proposed method has the preparation rate, recall rate, and experimental effect advantage. In different median values, the proposed method also has an advantage in experimental results.

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Lu, Donglei, Dongjie Zhu, Haiwen Du, Yundong Sun, Yansong Wang, Xiaofang Li, Rongning Qu, Ning Cao, and Russell Higgs. "Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph." Computer Systems Science and Engineering 42, no. 3 (2022): 1133–46. http://dx.doi.org/10.32604/csse.2022.021525.

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Gu, Fei, Jianwei Niu, and Lingjie Duan. "WAIPO: A Fusion-Based Collaborative Indoor Localization System on Smartphones." IEEE/ACM Transactions on Networking 25, no. 4 (August 2017): 2267–80. http://dx.doi.org/10.1109/tnet.2017.2680448.

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Sun, Hongbo, Wenhui Fan, Weiming Shen, and Tianyuan Xiao. "Ontology Fusion in High-Level-Architecture-Based Collaborative Engineering Environments." IEEE Transactions on Systems, Man, and Cybernetics: Systems 43, no. 1 (January 2013): 2–13. http://dx.doi.org/10.1109/tsmca.2012.2190138.

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Protogerou, A., Y. Caloghirou, and E. Siokas. "Research networking and technology fusion through EU-funded collaborative projects." Science and Public Policy 40, no. 5 (March 26, 2013): 576–90. http://dx.doi.org/10.1093/scipol/sct008.

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Kampis, George, and Paul Lukowicz. "Collaborative Knowledge Fusion by Ad-Hoc Information Distribution in Crowds." Procedia Computer Science 51 (2015): 542–51. http://dx.doi.org/10.1016/j.procs.2015.05.319.

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Wu, Hao, Yijian Pei, Bo Li, Zongzhan Kang, Xiaoxin Liu, and Hao Li. "Item recommendation in collaborative tagging systems via heuristic data fusion." Knowledge-Based Systems 75 (February 2015): 124–40. http://dx.doi.org/10.1016/j.knosys.2014.11.026.

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Latha, R., and R. Nadarajan. "Analysing exposure diversity in collaborative recommender systems—Entropy fusion approach." Physica A: Statistical Mechanics and its Applications 533 (November 2019): 122052. http://dx.doi.org/10.1016/j.physa.2019.122052.

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Zhang, Jia, Candong Li, Yaojin Lin, Youwei Shao, and Shaozi Li. "Computational drug repositioning using collaborative filtering via multi-source fusion." Expert Systems with Applications 84 (October 2017): 281–89. http://dx.doi.org/10.1016/j.eswa.2017.05.004.

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Song, Wei, Mingyun Wen, Yulong Xi, Phuong Minh Chu, Hoang Vu, Shokh-Jakhon Kayumiy, and Kyungeun Cho. "A collaborative client participant fusion system for realistic remote conferences." Journal of Supercomputing 72, no. 7 (November 28, 2015): 2720–33. http://dx.doi.org/10.1007/s11227-015-1580-z.

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Jia, Sen, Xianglong Deng, Jiasong Zhu, Meng Xu, Jun Zhou, and Xiuping Jia. "Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 10 (October 2019): 7770–84. http://dx.doi.org/10.1109/tgrs.2019.2916329.

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Fang, Juan, Baocai Li, and Mingxia Gao. "Collaborative filtering recommendation algorithm based on deep neural network fusion." International Journal of Sensor Networks 34, no. 2 (2020): 71. http://dx.doi.org/10.1504/ijsnet.2020.10032760.

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Fang, Juan, Baocai Li, and Mingxia Gao. "Collaborative filtering recommendation algorithm based on deep neural network fusion." International Journal of Sensor Networks 34, no. 2 (2020): 71. http://dx.doi.org/10.1504/ijsnet.2020.110460.

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Yang, Yuanyuan, and Guiyun Zhang. "Study on Collaborative Filtering Algorithm of Fusion Context-Aware Calculation." International Journal of Computer Theory and Engineering 10, no. 2 (2018): 63–66. http://dx.doi.org/10.7763/ijcte.2018.v10.1200.

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43

Shambour, Qusai Y., Mosleh M. Abualhaj, Qasem M. Kharma, and Faris M. Taweel. "A fusion multi-criteria collaborative filtering algorithm for hotel recommendations." International Journal of Computing Science and Mathematics 16, no. 4 (2022): 399. http://dx.doi.org/10.1504/ijcsm.2022.128653.

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Kharma, Qasem M., Faris M. Taweel, Qusai Y. Shambour, and Mosleh M. Abualhaj. "A fusion multi-criteria collaborative filtering algorithm for hotel recommendations." International Journal of Computing Science and Mathematics 16, no. 4 (2022): 399. http://dx.doi.org/10.1504/ijcsm.2022.10053737.

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45

Cai, Zhi, Jiahang Liu, Weijian Chi, and Bo Zhang. "A Low-Cost and Robust Multi-Sensor Data Fusion Scheme for Heterogeneous Multi-Robot Cooperative Positioning in Indoor Environments." Remote Sensing 15, no. 23 (November 30, 2023): 5584. http://dx.doi.org/10.3390/rs15235584.

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Abstract:

The latest development of multi-robot collaborative systems has put forward higher requirements for multi-sensor fusion localization. Current position methods mainly focus on the fusion of the carrier’s own sensor information, and how to fully utilize the information of multiple robots to achieve high-precision positioning is a major challenge. However, due to the comprehensive impact of factors such as poor performance, variety, complex calculations, and accumulation of environmental errors used by commercial robots, the difficulty of high-precision collaborative positioning is further exacerbated. To address this challenge, we propose a low-cost and robust multi-sensor data fusion scheme for heterogeneous multi-robot collaborative navigation in indoor environments, which integrates data from inertial measurement units (IMUs), laser rangefinders, cameras, and so on, into heterogeneous multi-robot navigation. Based on Discrete Kalman Filter (DKF) and Extended Kalman Filter (EKF) principles, a three-step joint filtering model is used to improve the state estimation and the visual data are processed using the YOLO deep learning target detection algorithm before updating the integrated filter. The proposed integration is tested at multiple levels in an open indoor environment following various formation paths. The results show that the three-dimensional root mean square error (RMSE) of indoor cooperative localization is 11.3 mm, the maximum error is less than 21.4 mm, and the motion error in occluded environments is suppressed. The proposed fusion scheme is able to satisfy the localization accuracy requirements for efficient and coordinated motion of autonomous mobile robots.

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YAMASHITA, AKIHIRO, HIDENORI KAWAMURA, and KEIJI SUZUKI. "ADAPTIVE FUSION METHOD FOR USER-BASED AND ITEM-BASED COLLABORATIVE FILTERING." Advances in Complex Systems 14, no. 02 (April 2011): 133–49. http://dx.doi.org/10.1142/s0219525911003001.

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In many e-commerce sites, recommender systems, which provide personalized recommendations from among a large number of items, have recently been introduced. Collaborative filtering is one of the most successful algorithms which provide recommendations using ratings of users on items. There are two approaches: user-based and item-based collaborative filtering. Additionally a unifying method for user-based and item-based collaborative filtering was proposed to improve the recommendation accuracy. The unifying approach uses a constant value as a weight parameter to unify both algorithms. However, because the optimal weight for unifying is actually different depending on the situation, the algorithm should estimate an appropriate weight dynamically, and should use it. In this research, we first investigate the relationship between recommendation accuracy and the weight parameter. The results show that the optimal weight is different depending on the situation. Second, we propose an approach for estimation of the appropriate weight value based on collected ratings. Then, we discuss the effectiveness of the proposed approach based on both multi-agent simulation and the MovieLens dataset. The results show that the proposed approach can estimate the weight value within an error rate of 0.5% for the optimal weight.

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Yao, Ya Chuan, Yi Yao, Ren Yi Zhang, and Qiang Han. "Design of the Multi-Sensor Target Tracking System Based on Data Fusion." Advanced Materials Research 219-220 (March 2011): 1407–10. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1407.

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The paper is about designing fusion tracking system based on multi-sensor information processing which combined with data fusion. It solves the multiple sensor nodes collaborative work problems of the target tracking system, which makes wireless sensor networks can process a large number of instantaneous data in time. Its practicability becomes strong after practicing and simulating.

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Saorín, José Luis, Jorge de la Torre-Cantero, Dámari Melián Díaz, and Vicente López-Chao. "Cloud-Based Collaborative 3D Modeling to Train Engineers for the Industry 4.0." Applied Sciences 9, no. 21 (October 27, 2019): 4559. http://dx.doi.org/10.3390/app9214559.

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In the present study, Autodesk Fusion 360 software (which includes the A360 environment) is used to train engineering students for the demands of the industry 4.0. Fusion 360 is a tool that unifies product lifecycle management (PLM) applications and 3D-modeling software (PDLM—product design and life management). The main objective of the research is to deepen the students’ perception of the use of a PDLM application and its dependence on three categorical variables: PLM previous knowledge, individual practices and collaborative engineering perception. Therefore, a collaborative graphic simulation of an engineering project is proposed in the engineering graphics subject at the University of La Laguna with 65 engineering undergraduate students. A scale to measure the perception of the use of PDLM is designed, applied and validated. Subsequently, descriptive analyses, contingency graphical analyses and non-parametric analysis of variance are performed. The results indicate a high overall reception of this type of experience and that it helps them understand how professionals work in collaborative environments. It is concluded that it is possible to respond to the demand of the industry needs in future engineers through training programs of collaborative 3D modeling environments.

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Zhang, Hua-ying, and Tiande Pan. "Public Health Risk Assessment and Prevention Based on Big Data." Journal of Environmental and Public Health 2022 (September 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/7965917.

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In order to improve the ability of public health risk assessment in the context of community collaborative prevention and control, a mathematical model of public health risk assessment in the context of community collaborative prevention and control based on the integration and balanced allocation of big data features in the prevention horizon is proposed. The constraint parameter model of public health risk assessment under the background of community collaborative prevention and control is constructed, the method of dynamic feature analysis of joint prevention and control is adopted to realize the dynamic risk point detection of public health risk assessment data and the integration of constraint mechanism related feature points, and the fuzzy dynamic statistical feature matching method is adopted to carry out the adaptive dynamic statistics and resource balanced allocation analysis of public health risk assessment set under the background of community collaborative prevention and control. A public health risk parameter fusion model is established under the background of community collaborative prevention and control, the methods of balanced resource allocation and joint management and control are combined to realize balanced scheduling and prevention area block matching in the process of dynamic parameter estimation of public health risk evaluation data under the background of community collaborative prevention and control, the correlation distribution of public health risk under the background of community collaborative prevention and control is taken as the cost function, and balanced allocation is realized according to the statistical information sampling results of public health risk evaluation data under the background of community collaborative prevention and control. Combined with differential clustering analysis, the data clustering and attribute merging of public health risk assessment under the background of community collaborative prevention and control are realized, and the mathematical modeling optimization of public health risk assessment under the background of community collaborative prevention and control is realized. The simulation results show that this method has good adaptability, high degree of parameter fusion, and strong ability of matching risk prevention areas and balancing resource allocation in the context of community collaborative prevention and control.

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Wang, Yichen. "Research on recommendation algorithm based on collaborative filtering of fusion model." Journal of Physics: Conference Series 1774, no. 1 (January 1, 2021): 012058. http://dx.doi.org/10.1088/1742-6596/1774/1/012058.

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