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
BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation
Juan Borrego-Carazo, Carles Sánchez, David Castells-Rufas, Jordi Carrabina, Débora Gil
Aspect-based Analysis of Advertising Appeals for Search Engine Advertising
Soichiro Murakami, Peinan Zhang, Sho Hoshino, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
ChildCI Framework: Analysis of Motor and Cognitive Development in Children-Computer Interaction for Age Detection
Juan Carlos Ruiz-Garcia, Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Jaime Herreros-Rodriguez
Analysis and transformations of voice level in singing voice
Frederik Bous, Axel Roebel
LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks
Amin Shafiee, Sanmitra Banerjee, Krishnendu Chakrabarty, Sudeep Pasricha, Mahdi Nikdast
Analysis of Different Losses for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo, Michaël Clément, Rémi Giraud, Lara Raad, Patricia Vitoria
Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision
Duygu Ider, Stefan Lessmann