There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …

Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Suicide is a critical issue in modern society. Early detection and prevention of suicide
attempts should be addressed to save people's life. Current suicidal ideation detection (SID) …

Federated learning (FL) has recently emerged as a promising distributed machine learning
(ML) paradigm. Practical needs of the" right to be forgotten" and countering data poisoning …

Stealing attack against controlled information, along with the increasing number of
information leakage incidents, has become an emerging cyber security threat in recent …

The aim of this paper is to review machine learning (ML) algorithms and techniques for hate
speech detection in social media (SM). Hate speech problem is normally model as a text …

Graph Neural Networks (GNNs), which generalize traditional deep neural networks on
graph data, have achieved state-of-the-art performance on several graph analytical tasks …

The significant rise in suicides is a major cause of concern in public health domain.
Depression plays a major role in increasing suicide ideation among the individuals …

Privacy concerns often arise as the key bottleneck for the sharing of data between
consumers and data holders, particularly for sensitive data such as Electronic Health …

Abstract Adversarial Machine Learning (AML) discusses the act of attacking and defending
Machine Learning (ML) Models, an essential building block of Artificial Intelligence (AI). ML …