Classification Code
Classification code research focuses on developing and improving algorithms and models to accurately assign data points to predefined categories. Current efforts concentrate on addressing challenges like imbalanced datasets, noisy data, and limited labeled data through techniques such as self-supervised pre-training, robust loss functions, and the application of diverse architectures including convolutional neural networks (CNNs), transformers, and novel approaches like Mamba. These advancements have significant implications across various fields, improving accuracy and efficiency in applications ranging from medical image analysis and bioacoustic monitoring to cybersecurity threat detection and scientific literature organization.
Papers
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network
Yash Sharma, Nick Coronato, Donald E. Brown
Supervised machine learning classification for short straddles on the S&P500
Alexander Brunhuemer, Lukas Larcher, Philipp Seidl, Sascha Desmettre, Johannes Kofler, Gerhard Larcher
Brain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural Network
Amin Abdollahi Dehkordi, Mina Hashemi, Mehdi Neshat, Seyedali Mirjalili, Ali Safaa Sadiq
Classification of Buildings' Potential for Seismic Damage by Means of Artificial Intelligence Techniques
Konstantinos Kostinakis, Konstantinos Morfidis, Konstantinos Demertzis, Lazaros Iliadis
Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network
Shimin Chen, Chen Chen, Wei Li, Xunqiang Tao, Yandong Guo
RF Signal Transformation and Classification using Deep Neural Networks
Umar Khalid, Nazmul Karim, Nazanin Rahnavard