Novel Approach
This research explores novel approaches across diverse fields, aiming to improve existing methods and address limitations in various machine learning and AI applications. Current efforts focus on enhancing model performance and robustness through techniques like active learning, diffusion models, and transformer architectures, often incorporating advanced concepts such as graph isomorphism networks and attention mechanisms. These advancements have significant implications for various domains, including robotics, personalized recommendations, medical image analysis, and cybersecurity, by improving accuracy, efficiency, and interpretability. The overall goal is to create more powerful, reliable, and explainable AI systems.
Papers
pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning
Zebin Ma, Yonglin Zou, Xiaobin Huang, Wenjin Yan, Hao Xu, Jiexin Yang, Ying Zhang, Jinqi Huang
Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classification
Suvadeep Maiti, Shivam Kumar Sharma, Raju S. Bapi
A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective
Chenhang Cui, Yazhou Ren, Jingyu Pu, Jiawei Li, Xiaorong Pu, Tianyi Wu, Yutao Shi, Lifang He