Abstract Background and Objective: Artificial Intelligence (AI) has shown significant
advancements across several industries, including healthcare, using better fusion …

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by
enhancing the trust of end-users in machines. As the number of connected devices keeps on …

In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has
become pervasive across a spectrum of real-world applications, often in safety-critical …

Objective Disease comorbidity is a major challenge in healthcare affecting the patient's
quality of life and costs. AI-based prediction of comorbidities can overcome this issue by …

Machine intelligence models are robust in classifying the datasets for data analytics and for
predicting the insights that would assist in making clinical decisions. The models would …

Online social networks can be used for mental healthcare monitoring using Artificial
Intelligence and Machine Learning techniques for detecting various mental health disorders …

Background Artificial intelligence (AI) and machine learning (ML) models continue to evolve
the clinical decision support systems (CDSS). However, challenges arise when it comes to …

This paper engages in a comprehensive investigation concerning the application of
Explainable Artificial Intelligence (xAI) within the context of deep learning and Artificial …

Artificial intelligence (AI) and machine learning (ML) models have become essential tools
used in many critical systems to make significant decisions; the decisions taken by these …

Despite the numerous benefits of general artificial intelligence applications, there are
challenges in its introduction and implementation. This paper examines the limits of artificial …