AIMC Topic: Neural Networks, Computer

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E-BabyNet: Enhanced Action Recognition of Infant Reaching in Unconstrained Environments.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Machine vision and artificial intelligence hold promise across healthcare applications. In this paper, we focus on the emerging research direction of infant action recognition, and we specifically consider the task of reaching which is an important d...

Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning.

Molecular psychiatry
With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques ...

TGIN: Document-level event extraction with two-phase graph inference network.

Neural networks : the official journal of the International Neural Network Society
Document-level event extraction aims to extract event records from a whole document that contain numerous entities scattered across multiple sentences. Efficiently modeling the interactions among these entities is crucial. However, previous methods s...

Towards complex dynamic physics system simulation with graph neural ordinary equations.

Neural networks : the official journal of the International Neural Network Society
The great learning ability of deep learning facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour both in academia and industry. However, the complex laws of the physical ...

Salience Interest Option: Temporal abstraction with salience interest functions.

Neural networks : the official journal of the International Neural Network Society
Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions,...

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Bone
Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bla...

Diabetic retinopathy prediction based on vision transformer and modified capsule network.

Computers in biology and medicine
Diabetic retinopathy is considered one of the most common diseases that can lead to blindness in the working age, and the chance of developing it increases as long as a person suffers from diabetes. Protecting the sight of the patient or decelerating...

End-to-End Ultrasonic Hand Gesture Recognition.

Sensors (Basel, Switzerland)
As the number of electronic gadgets in our daily lives is increasing and most of them require some kind of human interaction, this demands innovative, convenient input methods. There are limitations to state-of-the-art (SotA) ultrasound-based hand ge...

A Comparative Study of Physically Accurate Synthetic Shadow Datasets in Agricultural Settings with Human Activity.

Sensors (Basel, Switzerland)
Shadow, a natural phenomenon resulting from the absence of light, plays a pivotal role in agriculture, particularly in processes such as photosynthesis in plants. Despite the availability of generic shadow datasets, many suffer from annotation errors...

Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method.

BMC pulmonary medicine
BACKGROUND: Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces ch...