Patients, hospitals, sensors, researchers, providers, phones, and healthcare organisations
are producing enormous amounts of data in both the healthcare and drug detection sectors …

Alzheimer Disease (AD) is the ordinary type of dementia which does not have any proper
and efficient medication. Accurate classification and detection of AD helps to diagnose AD in …

Abstract Training a Convolutional Neural Network (CNN) from scratch is time-consuming
and expensive. In this study, we propose implementing the DenseNet architecture for …

Machine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are
progressively gaining prevalence to manage the vast volume of processed information …

Semantics play a crucial role in organizing domain knowledge, schematizing it, and
modeling it into classes of objects and relationships between them. Knowledge graphs …

In the face of the increasing prevalence of neurodegenerative disorders and the lack of
effective treatments, there is growing interest in non-pharmacological approaches for …

Abstract Data Sources: Articles for this paper were sourced from Pub-Med and Google
Scholar databases. Data Extraction: Human studies published in English from 2015 to 2021 …

Deep visual data analysis from social network has become an increasingly important area of
research. In fact, this form of assessment makes it viable to recognize new information on …

This chapter focuses on the contributions of AI-powered robots for the early detection of AD
using human-robot communication. The objectives of this chapter are to 1) summarize and …

The prevalence of neurocognitive disorders has led to increased interest in mobile health
applications (mHealth apps) for detection and training. However, there'sa need for apps that …