An Enhanced Misinformation Detection Model Based on an Improved Beluga Whale Optimization Algorithm and Cross-Modal Feature Fusion

Biomimetics, Journal Year: 2025, Volume and Issue: 10(3), P. 128 - 128

Published: Feb. 20, 2025

The proliferation of multimodal misinformation on social media has become a critical concern. Although detection methods have advanced, feature representation and cross-modal semantic alignment challenges continue to hinder the effective use data. Therefore, this paper proposes an IBWO-CASC model that integrates improved Beluga Whale Optimization algorithm with attention fusion. Firstly, is enhanced by combining adaptive search mechanisms batch parallel strategies in space. Secondly, method designed based supervised contrastive learning establish consistency. Then, incorporates Cross-modal Attention Promotion mechanism global–local interaction pattern. Finally, multi-task framework built classification objectives. empirical analysis shows proposed achieves accuracy 97.41% our self-constructed dataset. Compared average existing six baseline models, 4.09%. Additionally, it demonstrates robustness handling complex scenarios.

Language: Английский

A black-winged kite optimization algorithm enhanced by osprey optimization and vertical and horizontal crossover improvement

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 25, 2025

This paper addresses issues of inadequate accuracy and inconsistency between global search efficacy local development capability in the black-winged kite algorithm for practical problem-solving by proposing a optimization that integrates Osprey Crossbar enhancement (DKCBKA). Firstly, adaptive index factor fusion Optimization Algorithm approach are incorporated to enhance algorithm's convergence rate, probability distribution is updated throughout attack stage. Second, stochastic difference variant method implemented prevent from entering optima. Lastly, longitudinal transversal crossover technique dynamically alter population's individual optimal solutions. Fifteen benchmark functions chosen test effectiveness enhanced compare efficiency each technique. Simulation experiments performed on CEC2017 CEC2019 sets, revealing DKCBKA surpasses five standard swarm intelligence methods six improved algorithms regarding solution speed. The superiority meeting real challenges further demonstrated three engineering problems DKCBKA, with capabilities 18.222%, 99.885% 0.561% higher than BKA, respectively.

Language: Английский