Abstract Adversarial Machine Learning (AML) is a recently introduced technique, aiming to
deceive Machine Learning (ML) models by providing falsified inputs to render those models …

In recent years, significant progress has been achieved using deep neural networks (DNNs)
in obtaining human-level performance on various long-standing tasks. With the increased …

Artificial intelligence (AI) is a revolutionary technology that is finding wide application across
numerous sectors. Large language models (LLMs) are an emerging subset technology of AI …

Abstract Textual Emotion Analysis (TEA) seeks to extract and assess the emotional states of
users from the text. Various Deep Learning (DL) algorithms have emerged rapidly and …

Domain adaptation (DA) techniques are frequently utilized to enhance seizure prediction
accuracy by leveraging the labeled electroencephalogram data of existing patients on new …

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the
deployment and adoption of AI technologies remain limited in real-world clinical practice. In …

Deep Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …

Recently, deep networks have shown impressive performance for the segmentation of
cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

There is a considerable threat present in genres such as machine learning due to
adversarial attacks which include purposely feeding the system with data that will alter the …