Volume-11, Issue-1, January 2025

Abstract

In recent day modern power, system and industrial application witnessed great interest by engineering and scientist. Converters used with these applications also developed leading to improve efficiency, power management, cost, reliability and others. Bidirectional dc-dc converters are the base for all electric systems, which depends on dc power flow through networks or grids. In this work different kind of bidirectional converter are introduce, As well as the operation principle with limitation will propose. This review include several parts covered converter topology, operations, application, system storage units and control strategy.

Keywords: dc-dc converter, Bidirectional converter, renewable energy PV array, electric vehicle.

References

Keywords: dc-dc converter, Bidirectional converter, renewable energy PV array, electric vehicle.

Abstract

In response to the growing prevalence of multimodal false information on social media platforms, traditional single-modal models and basic feature concatenation approaches in multimodal models exhibit limitations in effectively detecting fake news. Therefore, this paper presents a multimodal approach for detecting fake news, integrating a multimodal attention mechanism known as MAMFND (Multimodal Attention Mechanism for Fake News Detection). Initially, we utilize pretrained BERT (Bidirectional Encoder Representations from Transformers) and Swin Transformer (Swin Transformer: Hierarchical Vision Transformer using Shifted Windows) models to extract features from text and images, respectively. Subsequently, we introduce a fusion strategy based on attention mechanisms to integrate textual and visual features. To better capture the intrinsic relationships between text and images, we also input the textual features into a BiLSTM (Bi-directional Long Short-Term Memory) model for temporal sequence modeling, followed by an additional attention-based fusion with visual features. Finally, we extract information from the two rounds of feature fusion and input it into a fake news detection model for classification. Experimental results demonstrate that, on the Weibo and CCF competition datasets, the MAMFND model achieved average accuracy improvements of approximately 9.4% and 5.6%, respectively, compared to baseline models.

Keywords: Fake News Detection, Multimodal Features Fusion, Multimodal Attention Mechanism, Deep learning

References

Keywords: Fake News Detection, Multimodal Features Fusion, Multimodal Attention Mechanism, Deep learning