General Analysis
General analysis encompasses a broad range of methodologies applied across diverse scientific domains to extract meaningful insights from data. Current research focuses on developing robust and efficient analytical techniques, including the application of machine learning models like convolutional neural networks, graph neural networks, and transformer architectures, as well as statistical methods for data modeling and hypothesis testing. These advancements are improving the accuracy and efficiency of analyses in fields ranging from medical image processing and materials science to social media analysis and autonomous systems, ultimately leading to more reliable scientific findings and improved decision-making in various applications.
Papers
BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training
Yiming Yan, Tao Wang, Chengqi Zhao, Shujian Huang, Jiajun Chen, Mingxuan Wang
Lyapunov function search method for analysis of nonlinear systems stability using genetic algorithm
A. M. Zenkin, A. A. Peregudin, A. A. Bobtsov
How word semantics and phonology affect handwriting of Alzheimer's patients: a machine learning based analysis
Nicole Dalia Cilia, Claudio De Stefano, Francesco Fontanella, Sabato Marco Siniscalchi
An Uncertainty Aided Framework for Learning based Liver $T_1\rho$ Mapping and Analysis
Chaoxing Huang, Vincent Wai Sun Wong, Queenie Chan, Winnie Chiu Wing Chu, Weitian Chen
Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques
Pijush Kanti Kumar, DeepKiran Munjal, Sunita Rani, Anurag Dutta, Liton Chandra Voumik, A. Ramamoorthy
A negation detection assessment of GPTs: analysis with the xNot360 dataset
Ha Thanh Nguyen, Randy Goebel, Francesca Toni, Kostas Stathis, Ken Satoh