This paper provides a brief analytical review of the current state‐of‐the‐art in relation to the
explainability of artificial intelligence in the context of recent advances in machine learning …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

Evaluating binary classifications is a pivotal task in statistics and machine learning, because
it can influence decisions in multiple areas, including for example prognosis or therapies of …

Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …

Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …