AIMC Topic: Humans

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A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composi...

Artificial Intelligence Model of Drive-Through Vaccination Simulation.

International journal of environmental research and public health
Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and...

Computer-Aided Diagnosis of Alzheimer's Disease through Weak Supervision Deep Learning Framework with Attention Mechanism.

Sensors (Basel, Switzerland)
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. H...

Human-Robot Interaction and Sexbots: A Systematic Literature Review.

Sensors (Basel, Switzerland)
At present, sexual robots have become a new paradigm of social robots. In this paper, we developed a systematic literature review about sexual robots (sexbots). To do this, we used the Scopus and WoS databases to answer different research questions r...

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications.

IEEE transactions on biomedical circuits and systems
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can fa...

On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks.

IEEE transactions on biomedical circuits and systems
Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements...

Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics.

PloS one
BACKGROUND: The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.

Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.

PloS one
BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making rega...