Abstract: This paper proposes a self-supervised framework based on a contrastive auto-encoding and convolutional information exchange for multi-modal medical fusion tasks. It is well known that ...

ABSTRACT: Anomaly detection in complex crowd scenes is a challenging task due to the inherent variability in crowd behaviors, interactions, and scales. This paper proposes a novel hybrid model that ...

Visual data(such as images, video) are everywhere. Rougly, millions of images and videos are generated everyday. For instance, everyday, 95 million photos and 720.000 ...

Multiple studies have attempted to use a single type of data to predict various stages of Alzheimer’s disease (AD). However, combining multiple data modalities can improve prediction accuracy. In this ...

This important work presents a novel approach to infer causal relations in non-stationary time series data. To do so, the authors introduce a novel machine-learning model of Temporal Autoencoders for ...

Alzheimer’s disease (AD) is a progressive neurological disorder that presents a significant public health concern. Early detection of Alzheimer’s has the potential to greatly improve patient care and ...

The rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results ...

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the ...