Latest News

AAAI 2025 (CCF-A, Top Conference in Artificial Intelligence)

Paper Title: Deconfound Semantic Shift and Incompleteness in Incremental Few-shot Semantic Segmentation

Computational Intelligence is a journal in the field of artificial intelligence that publishes original research on a wide range of experimental and theoretical topics in AI and computer science. The journal has a broad scope, covering areas such as machine learning, knowledge mining, network intelligence, artificial intelligence languages, and the philosophical impacts of AI. It is read by both researchers in the academic AI community and professionals in the industry.

Professor Yirui Wu from our lab was awarded the title of IEEE Senior Member

Qiran Kong’s paper “CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images,” published in TCBB, has been recognized as an ESI Highly Cited Paper.

The Jiangsu Province Young Science and Technology Talent Support Project (Youth Support Project) is an important talent program aiming to cultivate and select outstanding young science and technology talents.

At the “3rd Jiangsu Information Technology Application Conference” held at Nanjing University on November 7, 2024, Professor Wu Yirui was awarded the Youth Science and Technology Award of Jiangsu Computer Society in recognition of his innovative achievements in the field of few-shot learning.

The Digital Twin Watershed refers to the digital mapping and intelligent simulation of all elements within a physical watershed and the entire process of water governance and management activities. Centering on the construction of digital twin watersheds, the focus lies on how intelligent simulation technology, through big data analysis and artificial intelligence algorithms, enables real-time monitoring and prediction of multidimensional data such as hydrology, water quality, and the environment within the watershed. Specific case studies highlight the remarkable achievements of intelligent technology in areas like flood forecasting and ecological protection. In light of the characteristics of current pre-trained large models, the future development trends of digital twin and intelligent simulation technologies in watershed management are also explored.