Abstract Background and Objective The accurate diagnosis of temporomandibular disorders
continues to be a challenge, despite the existence of internationally agreed‐upon diagnostic …

We study structure-preserving low-dimensional representation of a data manifold embedded
in a high-dimensional observation space based on variational auto-encoders (VAEs). We …

Backdoor attacks in Deep Neural Networks (DNNs) involve an attacker inserting a backdoor
into the network by manipulating the training dataset, which causes misclassification of …

Computational finance combines machine learning with financial needs to provide more
efficient solutions for investment analysis and automated trading. In previous studies …

There is a gap between current methods that explain deep learning models that work on
imbalanced image data and the needs of the imbalanced learning community. Existing …

We combine standard persistent homology with image persistent homology to define a novel
way of characterizing shapes and interactions between them. In particular, we introduce:(1) …

Deep Neural Networks have been shown to perform poorly or even fail altogether when
deployed in real-world settings, despite exhibiting excellent performance on initial …

Machine learning methods have been recognized as rapid methods for satellite-based
aerosol retrievals but have not been widely applied in geostationary satellites. In this study …

In recent years, an increasing number of researchers have applied machine learning
techniques to online portfolio selection (OLPS), aiming to improve the efficiency and …

Deep neural networks (DNNs) are powerful tools for approximating the distribution of
complex data. It is known that data passing through a trained DNN classifier undergoes a …