Abstract: Large-scale industrial processes are characterized by complex reaction mechanisms and strong correlation among operational units, which makes it more difficult to extract the temporal ...

Experiments were executed on NVIDIA A40 of 46068MiB memory in linux with torch==2.1.0+cu121, torch_geometric==2.3.1, torch-sparse==0.6.18+pt21cu121, and torchvision==0.16.0+cu121. The stkan is an ...

ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...

Anomaly detection is a typical binary classification problem under the condition of unbalanced samples, which has been widely used in various fields of data mining. For example, it can help detect ...

Thanks for your great work. Recently I'm trying to train the parameter autoencoder (ODEncoder2Decoder class in your module/modules/encoder.py) with my own parameter ...

This image provides a complete visual guide to the autoencoder neural network, featuring five illustrations that detail each stage of the data encoding and decoding process. It serves as an ...

This image provides a visual representation of an autoencoder neural network, focusing on the three main components the encoder, hidden layer, and decoder. It depicts the process of data compression ...

Jeff Tompkins is a writer and teacher of English as a Second Language living in New York City. He was born in Hartford, Connecticut, in 1967 and was educated at Brown University and University College ...

ABSTRACT: In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices ...