In the last few years, the trend in health care of embracing artificial intelligence (AI) has
dramatically changed the medical landscape. Medical centres have adopted AI applications …

Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …

Abstract Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting
clinicians for real-time treatment of sepsis. While a value function quantifies the performance …

The purpose of this paper is to propose a novel hybrid framework for evaluating and
benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi …

Feature selection is widely used in various fields as a key means of data dimension
reduction. The existing feature selection algorithms only use one linear or nonlinear …

Attribute reductions rely on knowledge granulation and information measurement. Aiming at
incomplete interval-valued decision systems (IIVDSs), an attribute reduction (with the FSR …

Feature selections facilitate classification learning in various data environments. Aiming at
interval-valued decision systems (IVDSs), feature selections rely on information measures …

Abstract The use of Artificial Intelligence in medical decision support systems has been
widely studied. Since a medical decision is frequently the result of a multi-objective …

In medical imaging, the effective detection and classification of Breast Cancer (BC) is a
current research important task because of the still existing difficulty to distinguish …

In the era of explosive data growth, data sources and volumes are rapidly increasing. A multi-
source data refers to information from multi-sources. However, not every source of …