AIMC Topic: Deep Learning

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Transparent RFID tag wall enabled by artificial intelligence for assisted living.

Scientific reports
Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new...

Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults: Study of Domain Adaptation and Deep Learning.

JMIR aging
BACKGROUND: Cognitive impairment and dementia pose a significant challenge to the aging population, impacting the well-being, quality of life, and autonomy of affected individuals. As the population ages, this will place enormous strain on health car...

Predicting structure-targeted food bioactive compounds for middle-aged and elderly Asians with myocardial infarction: insights from genetic variations and bioinformatics-integrated deep learning analysis.

Food & function
Myocardial infarction (MI) is a significant global health issue. Despite the advances in genome-wide association studies, a complete genetic and molecular understanding of MI is elusive and needs to be fully explored. This study aimed to elucidate th...

An enhanced Garter Snake Optimization-assisted deep learning model for lung cancer segmentation and classification using CT images.

Journal of medical engineering & technology
An early detection of lung tumors is critical for better treatment results, and CT scans can reveal lumps in the lungs which are too small to be picked up by conventional X-rays. CT imaging has advantages, but it also exposes a person to radiation fr...

A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredi...

A Novel Method to Identify Mild Cognitive Impairment Using Dynamic Spatio-Temporal Graph Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer's disease (AD). However, al...

AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI.

Sensors (Basel, Switzerland)
Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerou...

Boundary-aware convolutional attention network for liver segmentation in ultrasound images.

Scientific reports
Liver ultrasound is widely used in clinical practice due to its advantages of non-invasiveness, non-radiation, and real-time imaging. Accurate segmentation of the liver region in ultrasound images is essential for accelerating the auxiliary diagnosis...

Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety.

Scientific reports
Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and ...