With the rise in traffic congestion in urban centers, predicting accidents has become
paramount for city planning and public safety. This work comprehensively studied the …

Hyperspectral image (HSI) spectral-spatial joint feature (FE) extraction methods generally
suffer from low feature retention and weak spatial–spectral dependence, which will lead to …

Federated learning (FL) represents a distributed machine learning approach that leverages
a centralized server to train models while keeping the data on edge devices isolated. FL has …

The prevalence of cybercrime has emerged as a critical issue in contemporary society
because of its far-reaching financial, social, and psychological implications. The negative …

Discriminative marginalized least-squares regression (DMLSR) is unable to extract the
spectral-spatial joint features, the proportion of learned interfering pixels is high. To solve …

With satellite quantity and quality development in recent years, remote sensing products in
vast areas are becoming widely used in more and more fields. The acquisition of large …

Facial emotion recognition (FER) stands as a pivotal artificial intelligence (AI)-driven
technology that exploits the capabilities of computer-vision techniques for decoding and …

We present an innovative hyperspectral image (HSI) classification method addressing
challenges posed by closely spaced wavelength bands. Our approach combines 3D-2D …

The growing adoption of renewable energy sources and the need for more efficient and
secure energy grids are revolutionizing the energy sector. Electricity monitoring becomes an …

This paper proposes a spectral clustering method using k-means and weighted
Mahalanobis distance (Referred to as MDLSC) to enhance the degree of correlation …