To address the challenge of traditional models in simultaneously capturing local fluctuations and global trends for air pollutant concentration prediction, this paper proposes a multimodal deep learning model named MLP-BiLSTM- MHAT. The model integrates static features via MLP, extracts temporal dependencies through bidirectional LSTM (BiLSTM), and employs a Multi-head Attention mechanism (MHAT) to fuse local and global features while enhancing interactions between static and temporal characteristics. An improved Adam algorithm dynamically optimizes learning rates to balance the influence of heterogenous features. Validated on multi-site air quality data from Beijing, experimental results de... More >

Fall detection (FD) systems are an important part of healthcare monitoring, especially for elderly populations, where quick intervention can prevent serious injuries. This paper introduces an optimized YOLOV5-based framework that combines dual attention mechanisms for improved FD in real-time edge deployment situations. The proposed design integrates the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation (SE) blocks within the YOLOv5 backbone, along with an improved Focus module that uses slice-based feature extraction. These enhancements allow the model to effectively capture both spatial and channel-wise dependencies, which are essential for distinguishing fall events f... More >