This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature.
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory.
We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space.
Thus, accurate survival prognosis is an important step in treatment planning.
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists.
Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology.
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing. The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities. In this study, long-term stress is classified using baseline EEGsignal recordings.
The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals.
In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals.
Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features.
A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images.
The learned features and the classification results are used to retrieve medical images.