Opinion formation analysis for Expressed and Private Opinions (EPOs) models: Reasoning private opinions from behaviors in group decision-making systems

Expert Systems with Applications, Год журнала: 2023, Номер 236, С. 121292 - 121292

Опубликована: Сен. 1, 2023

Язык: Английский

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Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

Tsinghua Science & Technology, Год журнала: 2024, Номер 29(5), С. 1249 - 1265

Опубликована: Май 2, 2024

With the emergence of artificial intelligence era, all kinds robots are traditionally used in agricultural production. However, studies concerning robot task assignment problem agriculture field, which is closely related to cost and efficiency a smart farm, limited. Therefore, Multi-Weeding Robot Task Assignment (MWRTA) addressed this paper minimize maximum completion time residual herbicide. A mathematical model set up, Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm presented solve problem. In MOTLBO algorithm, heuristic-based initialization comprising an improved Nawaz Enscore, Ham (NEH) heuristic load-based generate initial population with high level quality diversity. An effective teaching-learning-based optimization process designed dynamic grouping mechanism redefined individual updating rule. multi-neighborhood-based local search strategy provided balance exploitation exploration algorithm. Finally, comprehensive experiment conducted compare proposed several state-of-the-art algorithms literature. Experimental results demonstrate significant superiority for solving under consideration.

Язык: Английский

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Graph Contrastive Learning With Feature Augmentation for Rumor Detection

IEEE Transactions on Computational Social Systems, Год журнала: 2023, Номер 11(4), С. 5158 - 5167

Опубликована: Май 4, 2023

While online social media brings convenience to people's communication, it has also caused the widespread spread of rumors and brought great harm. Recent deep-learning approaches attempt identify by engaging in interactive user feedback. However, performance these models suffers from insufficient noisy labeled data. In this article, we propose a novel rumor detection model called graph contrastive learning with feature augmentation (FAGCL), which injects noise into space learns contrastively constructing asymmetric structures. FAGCL takes preference news embedding as initial features propagation tree then adopts attention network update node representations. To obtain graph-level representation for classification, fuses multiple pooling techniques. Moreover, an auxiliary task constrain consistency. Contrastive on data mines supervision information itself, making more robust effective. Results two real-world datasets demonstrate that our proposed achieves significant improvements over baseline models.

Язык: Английский

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Modeling and Scheduling a Constrained Flowshop in Distributed Manufacturing Environments

Journal of Manufacturing Systems, Год журнала: 2024, Номер 72, С. 519 - 535

Опубликована: Янв. 9, 2024

Язык: Английский

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Time-aware multi-behavior graph network model for complex group behavior prediction

Information Processing & Management, Год журнала: 2024, Номер 61(3), С. 103666 - 103666

Опубликована: Янв. 20, 2024

Язык: Английский

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Product ranking through fusing the wisdom of consumers extracted from online reviews on multiple platforms

Knowledge-Based Systems, Год журнала: 2023, Номер 284, С. 111275 - 111275

Опубликована: Дек. 10, 2023

Язык: Английский

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Opinion formation over dynamic hierarchical networks with acquaintances and strangers: A genetic variation based double-mechanism framework

Applied Soft Computing, Год журнала: 2024, Номер 158, С. 111583 - 111583

Опубликована: Апрель 4, 2024

Язык: Английский

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Modeling public opinion dynamics in social networks using a GAN-SEIR framework

Social Network Analysis and Mining, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 16, 2025

Язык: Английский

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Information diffusion over cyber–physical conjoined networks: An immunity perspective

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113637 - 113637

Опубликована: Май 1, 2025

Язык: Английский

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CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Chinese Physics B, Год журнала: 2024, Номер 33(8), С. 088901 - 088901

Опубликована: Июнь 3, 2024

Abstract The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation various rumors. In order to block outbreak rumor, one most effective containment measures is spreading positive information counterbalance diffusion rumor. mechanism rumors suppression strategies are significant challenging research issues. Firstly, in simulate dissemination multiple types information, we propose competitive linear threshold model with state transition (CLTST) describe process rumor anti-rumor same network. Subsequently, put forward community-based blocking (CRB) algorithm based on influence maximization theory networks. Its crucial step identify set influential seeds that propagate other nodes, which includes community detection, selection candidate generation seed set. Under CLTST model, CRB has been compared six state-of-the-art algorithms nine verify performance. Experimental results show proposed can better reflect propagation, review Moreover, performance weakening ability, select more accurately achieve spread, sensitivity analysis, distribution running time.

Язык: Английский

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