As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and
threatening. Network intrusion detection has been widely accepted as an effective method to …

Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …

In-context learning is a recent paradigm in natural language understanding, where a large
pre-trained language model (LM) observes a test instance and a few training examples as …

ChatGPT, an artificial intelligence model, has garnered significant interest within education.
This study examined public sentiment regarding ChatGPT's influence on education by …

This study presents a thorough examination of various Generative Pretrained Transformer
(GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the …

Smart homes have grown in popularity not only as a luxury but also because of the
numerous benefits they provide. In this research, a home automation system is developed …

Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMap have
demonstrated impressive visualization performance on many real-world datasets. One …

In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D
skeleton-based action representation (CrosSCLR), by leveraging multi-view complementary …

Automated bioacoustic analysis aids understanding and protection of both marine and
terrestrial animals and their habitats across extensive spatiotemporal scales, and typically …