Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.

The line between human and artificial intelligence is growing ever more blurry. Since 2021, AI has deciphered ancient texts ...

Current continual learning methods can utilize labeled data to alleviate catastrophic forgetting effectively. However, ...

Abstract: Network traffic classification (NTC) plays an essential role in managing, securing, and optimizing networks. Supervised learning methods face challenges such as label scarcity. Given that ...

A machine learning project to predict loan default risk using financial and credit history data. Built as part of a team capstone project in master degree at Deakin University. BayesCOOP is a scalable ...

1 Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States 2 Department of Applied Mathematics, University of Washington, Seattle, WA, United States ...

Automatic classification of interior decoration styles has great potential to guide and streamline the design process. Despite recent advancements, it remains challenging to construct an accurate ...

Abstract: Objective: Epileptic seizure classification using EEG signals remains a significant challenge due to complex spatial-temporal dependencies, limited labeled data, and severe class imbalance.