DH Research
DH research, encompassing diverse applications of data-driven methods, primarily aims to improve prediction accuracy and efficiency across various domains. Current research focuses heavily on leveraging machine learning algorithms, including convolutional neural networks, recurrent neural networks (like LSTMs), and large language models (like GPT-4), often combined with techniques like knowledge graph embedding and attention mechanisms, to analyze complex datasets and improve model performance. This work holds significant implications for numerous fields, from enhancing financial risk management and improving healthcare diagnostics to optimizing autonomous systems and advancing water resource management.
Papers
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning
Yuxiang Hu, Jinxin Hu, Ting Xu, Bo Zhang, Jiajie Yuan, Haozhang Deng
Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning
Dan Sun, Yaxin Liang, Yining Yang, Yuhan Ma, Qishi Zhan, Erdi Gao
Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network
Houze Liu, Iris Li, Yaxin Liang, Dan Sun, Yining Yang, Haowei Yang
A Toolbox for Supporting Research on AI in Water Distribution Networks
André Artelt, Marios S. Kyriakou, Stelios G. Vrachimis, Demetrios G. Eliades, Barbara Hammer, Marios M. Polycarpou
Research on Driver Facial Fatigue Detection Based on Yolov8 Model
Chang Zhou, Yang Zhao, Shaobo Liu, Yi Zhao, Xingchen Li, Chiyu Cheng