Stock index price prediction is prevalent in both academic and economic fields. The index
price is hard to forecast due to its uncertain noise. With the development of computer …

Faithfully representing chemical environments is essential for describing materials and
molecules with machine learning approaches. Here, we present a systematic classification …

In recent years, research on image generation has been developing very fast. The
generative adversarial network (GAN) emerges as a promising framework, which uses …

Skin lesion border irregularity, which represents the B feature in the ABCD rule, is
considered one of the most significant factors in melanoma diagnosis. Since signs that …

Abstract Generative Adversarial Networks (GAN) receive great attention recently due to its
excellent performance in image generation, transformation, and super-resolution. However …

Any optimization of gradient descent methods involves selecting a learning rate. Tuning the
learning rate can quickly become repetitive with deeper models of image classification, does …

With the increasing amounts of existing unorganized images on the internet today and the
necessity to use them efficiently in various types of applications. There is a critical need to …

Abstract Machine learning stems from a discipline with a long history, namely, artificial
intelligence, which uses statistical techniques to equip computer systems with the ability to" …

Deep neural network with multi-scale feature fusion has achieved great success in human
pose estimation. However, drawbacks still exist in these methods: 1) they consider multi …

To protect against attacks by malicious computer programs, many websites apply the
CAPTCHA (short for completely automated public turing test to tell computers and humans …