The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Recent years have witnessed promising artificial intelligence (AI) applications in many
disciplines, including optics, engineering, medicine, economics, and education. In particular …

The impressive growth rate of the Internet of Things (IoT) has drawn the attention of
cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and …

Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …

Mainstream object detectors based on the fully convolutional network has achieved
impressive performance. While most of them still need a hand-designed non-maximum …

The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …

Recent advancements in neural network design have given rise to the development of
Kolmogorov-Arnold Networks (KANs), which enhance interpretability and precision of these …

Since it can effectively address the problem of sparsity and cold start of collaborative
filtering, knowledge graph (KG) is widely studied and employed as side information in the …

The increasing awareness of climate change and human capital issues is shifting
companies towards aspects other than traditional financial earnings. In particular, the …