This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve ...

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication ...

Hyperspectral images (HSIs) with high spatial resolution are challenging to obtain directly due to sensor limitations. Deep learning is able to provide an end-to-end reconstruction solution from low ...

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image ...

With the development of e-commerce, the types of logistics services have become diverse. In response to the logistics requirements in urban environments, this paper introduces a logistics system that ...

A general formulation of the feeder reconfiguration problem for loss reduction and load balancing is given, and a novel solution method is presented. The solution uses a search over different radial ...

This review examines the HTRB and HTGB reliability of SiC MOSFETs, focusing on their performance under high-temperature and high-voltage conditions.

Electrochemical impedance spectroscopy (EIS), serving as an invasive yet insightful diagnostic method, unveils a trove of status information and has been utilized in data-driven state classification.

Multisource remote sensing images (RSIs) can capture the complementary information of ground objects for use in semantic segmentation. However, there can be inconsistency and interference noise among ...

In this letter, we present SemGuarder, a novel deep learning-based semantic communication (DLSC) system that simultaneously incorporates physical-layer semantic encryption and adversarial ...

Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This ...

This paper introduces a novel optimized hybrid model combining Long Short-Term Memory (LSTM) and Transformer deep learning architectures designed for power load forecasting. It leverages the strengths ...