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
Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation
Syed Samiul Alam, Samiul Based Shuvo, Shams Nafisa Ali, Fardeen Ahmed, Arbil Chakma, Yeong Min Jang
Classification of Orbits in Poincar\'e Maps using Machine Learning
Chandrika Kamath
Evolving Tsukamoto Neuro Fuzzy Model for Multiclass Covid 19 Classification with Chest X Ray Images
Marziyeh Rezaei, Sevda Molani, Negar Firoozeh, Hossein Abbasi, Farzan Vahedifard, Maysam Orouskhani
Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars
Guiyu Zhao, Bo Qiu, A-Li Luo, Xiaoyu Guo, Lin Yao, Kun Wang, Yuanbo Liu
A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems
Nicolò Pagan, Joachim Baumann, Ezzat Elokda, Giulia De Pasquale, Saverio Bolognani, Anikó Hannák
Novel deep learning methods for 3D flow field segmentation and classification
Xiaorui Bai, Wenyong Wang, Jun Zhang, Yueqing Wang, Yu Xiang