AIMC Topic: Humans

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[Applications of artificial intelligence to new drug development].

Annales pharmaceutiques francaises
Artificial intelligence (AI) encompasses technologies recapitulating four dimensions of human intelligence, i.e. sensing, thinking, acting and learning. The convergence of technological advances in those fields allows to integrate massive data and bu...

Deep Learning for EEG Seizure Detection in Preterm Infants.

International journal of neural systems
EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are re...

Deep Learning for Imaging and Detection of Microorganisms.

Trends in microbiology
Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic...

Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases.

American journal of ophthalmology
PURPOSE: To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images...

Stochastic configuration network ensembles with selective base models.

Neural networks : the official journal of the International Neural Network Society
Studies have demonstrated that stochastic configuration networks (SCNs) have good potential for rapid data modeling because of their sufficient adequate learning power, which is theoretically guaranteed. Empirical studies have verified that the learn...

Exploring the differential effects of trust violations in human-human and human-robot interactions.

Applied ergonomics
There is sparse research directly investigating the effects of trust manipulations in human-human and human-robot interactions. Moreover, studies on human-human versus human-robot trust have leveraged unusual or low vulnerability contexts to investig...

A survey on incorporating domain knowledge into deep learning for medical image analysis.

Medical image analysis
Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To address this problem, researchers have started looking for external informat...

Deep learning for the radiographic diagnosis of proximal femur fractures: Limitations and programming issues.

Orthopaedics & traumatology, surgery & research : OTSR
INTRODUCTION: Radiology is one of the domains where artificial intelligence (AI) yields encouraging results, with diagnostic accuracy that approaches that of experienced radiologists and physicians. Diagnostic errors in traumatology are rare but can ...

Automatic detection of brain metastases on contrast-enhanced CT with deep-learning feature-fused single-shot detectors.

European journal of radiology
PURPOSE: Despite the potential usefulness, no automatic detector is available for brain metastases on contrast-enhanced CT (CECT). The study aims to develop and investigate deep learning-based detectors for brain metastases detection on CECT.

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.

Sensors (Basel, Switzerland)
Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) usi...