Hierarchical cross-modal attention and dual audio pathways for enhanced multimodal sentiment analysis.

This paper presents a new architecture for multimodal sentiment analysis exploiting hierarchical cross-modal attention mechanisms, as well as two parallel lanes for audio analysis. Traditional sentiment analysis approaches are mainly based on text data, which can be inefficient as valuable sentiment information may reside within images and audio. Aiming at solving this issue, the model provides a unified framework that integrates three modalities (text, image, audio) based on BERT text encoder, ResNet50 visual features extractor and hybrid CNN-Wav2Vec2.

Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction.

Effective treatment for brain tumors relies on accurate detection because this is a crucial health condition. Medical imaging plays a pivotal role in improving tumor detection and diagnosis in the early stage. This study presents two approaches to the tumor detection problem focusing on the healthcare domain.