Artificial intelligence (AI) and machine learning (ML) techniques are transforming building
engineering. This work goes through the critical role of architectural parameters in …

Most real-world datasets usually contain imbalanced data. Learning from datasets where the
number of samples in one class (minority) is much smaller than in another class (majority) …

Featured Application This research provides a cutting-edge application in the field of digital
marketing, focusing on understanding and enhancing user engagement on e-commerce …

This research paper focuses on the challenges associated with learning classifiers from
large-scale, highly imbalanced datasets prevalent in many real-world applications …

In recent years, image and video super-resolution have gained attention outside the
computer vision community due to the outstanding results produced by applying deep …

Classification is among the core tasks in machine learning. Existing classification algorithms
are typically based on the assumption of at least roughly balanced data classes. When …

We employ machine learning (ML) to predict the yield stress and plastic strain of body-
centered cubic (BCC) high-entropy alloys (HEAs) in the compression test. Our machine …

Learning from imbalanced data has been a focus of studies for more than two decades of
continuous development. Training data is considered imbalanced when the size of the …

Machine learning and deep learning play vital roles in predicting diseases in the medical
field. Machine learning algorithms are widely classified as supervised, unsupervised, and …

We compare several machine learning (ML) models that predict the yield strength and
plasticity of high-entropy alloys (HEAs) for achieving high-accuracy with notably low root …