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
Establishing and Evaluating Trustworthy AI: Overview and Research Challenges
Dominik Kowald, Sebastian Scher, Viktoria Pammer-Schindler, Peter Müllner, Kerstin Waxnegger, Lea Demelius, Angela Fessl, Maximilian Toller, Inti Gabriel Mendoza Estrada, Ilija Simic, Vedran Sabol, Andreas Truegler, Eduardo Veas, Roman Kern, Tomislav Nad, Simone Kopeinik
Research on Domain-Specific Chinese Spelling Correction Method Based on Plugin Extension Modules
Xiaowu Zhang, Hongfei Zhao, Xuan Chang
Research on Travel Route Planing Problems Based on Greedy Algorithm
Yiquan Wang
Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents
Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Deli Zhao, Yu Rong, Tian Feng, Lidong Bing
Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs
Tianqi Shang, Shu Yang, Weiqing He, Tianhua Zhai, Dawei Li, Bojian Hou, Tianlong Chen, Jason H. Moore, Marylyn D. Ritchie, Li Shen
Research on short-term load forecasting model based on VMD and IPSO-ELM
Qiang Xie