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
Investigating the changes in BOLD responses during viewing of images with varied complexity: An fMRI time-series based analysis on human vision
Naveen Kanigiri, Manohar Suggula, Debanjali Bhattacharya, Neelam Sinha
Analysis on Multi-robot Relative 6-DOF Pose Estimation Error Based on UWB Range
Xinran Li, Shuaikang Zheng, Pengcheng Zheng, Haifeng Zhang, Zhitian Li, Xudong Zou
CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis
Di Guo, Sijin Li, Jun Liu, Zhangren Tu, Tianyu Qiu, Jingjing Xu, Liubin Feng, Donghai Lin, Qing Hong, Meijin Lin, Yanqin Lin, Xiaobo Qu
AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours
Sergio Romero-Tapiador, Ruben Tolosana, Aythami Morales, Isabel Espinosa-Salinas, Gala Freixer, Julian Fierrez, Ruben Vera-Rodriguez, Enrique Carrillo de Santa Pau, Ana Ramírez de Molina, Javier Ortega-Garcia