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
Severity Prediction in Mental Health: LLM-based Creation, Analysis, Evaluation of a Novel Multilingual Dataset
Konstantinos Skianis, John Pavlopoulos, A. Seza Doğruöz
Application of AI-based Models for Online Fraud Detection and Analysis
Antonis Papasavva, Shane Johnson, Ed Lowther, Samantha Lundrigan, Enrico Mariconti, Anna Markovska, Nilufer Tuptuk
An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications
Rumeshika Pallewela, Eslam Eldeeb, Hirley Alves
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers
Biplov Paneru, Bishwash Paneru, Krishna Bikram Shah
Speech Recognition for Analysis of Police Radio Communication
Tejes Srivastava, Ju-Chieh Chou, Priyank Shroff, Karen Livescu, Christopher Graziul
Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram
Michael Achmann-Denkler, Jakob Fehle, Mario Haim, Christian Wolff
An Analysis of Linear Complexity Attention Substitutes with BEST-RQ
Ryan Whetten, Titouan Parcollet, Adel Moumen, Marco Dinarelli, Yannick Estève