3 research outputs found
Alzheimer Disease Detection Techniques and Methods: A Review
Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper
A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions
As the name suggests, affective computing aims to recognize human emotions,
sentiments, and feelings. There is a wide range of fields that study affective
computing, including languages, sociology, psychology, computer science, and
physiology. However, no research has ever been done to determine how machine
learning (ML) and mixed reality (XR) interact together. This paper discusses
the significance of affective computing, as well as its ideas, conceptions,
methods, and outcomes. By using approaches of ML and XR, we survey and discuss
recent methodologies in affective computing. We survey the state-of-the-art
approaches along with current affective data resources. Further, we discuss
various applications where affective computing has a significant impact, which
will aid future scholars in gaining a better understanding of its significance
and practical relevance