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
Simplification of Robotic System Model Analysis by Petri Net Meta-Model Property Transfer
Maksym Figat, Cezary Zieliński
Modeling and Analysis of Multi-Line Orders in Multi-Tote Storage and Retrieval Autonomous Mobile Robot Systems
Xiaotao Shan, Yichao Jin, Peizheng Li, Koichi Kondo
Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities
Chayan Tank, Sarthak Pol, Vinayak Katoch, Shaina Mehta, Avinash Anand, Rajiv Ratn Shah
Advancing Automated Deception Detection: A Multimodal Approach to Feature Extraction and Analysis
Mohamed Bahaa, Mena Hany, Ehab E. Zakaria