Aggregate gradation plays a crucial role in determining the asphalt pavement performance,
necessitating assessment post-construction. Additionally, efficient gradation measurement is …

Autonomous indoor navigation of UAVs presents numerous challenges, primarily due to the
limited precision of GPS in enclosed environments. Additionally, UAVs' limited capacity to …

Traditional recurrent neural network architectures, such as long short-term memory neural
networks (LSTM), have historically held a prominent role in time series forecasting (TSF) …

Time series forecasting is a critical task that provides key information for decision-making
across various fields. Recently, various fundamental deep learning architectures such as …

In contemporary economic society, credit scores are crucial for every participant. A robust
credit evaluation system is essential for the profitability of core businesses such as credit …

Point cloud representation for real-world objects has seen a surge in interest recently,
finding widespread applications in augmented reality, virtual reality, and autonomous …

The progress of deep-learning-based forecasting architectures is evident through their
expanding parameter configurations. However, the need for rapid online decision making in …

Grain storage temperature prediction is crucial for silo safety and can effectively prevent
mold and mildew caused by increasing grain temperature and condensation due to …

Both Transformer and Graph Neural Networks (GNNs) have been employed in the domain
of learning to rank (LTR). However, these approaches adhere to two distinct yet …

Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages
from retrieved content based on input queries. However, traditional LTR models encounter …