AIMC Topic: Neural Networks, Computer

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EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.

Nature methods
Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mec...

Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy.

Radiation oncology (London, England)
BACKGROUND: This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict radiation induced hyposalivation from dose-volume histograms of the parotid glands.

Saliency Map and Deep Learning in Binary Classification of Brain Tumours.

Sensors (Basel, Switzerland)
The paper was devoted to the application of saliency analysis methods in the performance analysis of deep neural networks used for the binary classification of brain tumours. We have presented the basic issues related to deep learning techniques. A s...

An artificial neural network approach for rational decision-making in borderline orthodontic cases: A preliminary analytical observational in silico study.

Journal of orthodontics
INTRODUCTION: Artificial intelligence (AI) technology has transformed the way healthcare functions in the present scenario. In orthodontics, expert systems and machine learning have aided clinicians in making complex, multifactorial decisions. One su...

Event-triggered fault-tolerant control for input-constrained nonlinear systems with mismatched disturbances via adaptive dynamic programming.

Neural networks : the official journal of the International Neural Network Society
In this paper, the issue of event-triggered optimal fault-tolerant control is investigated for input-constrained nonlinear systems with mismatched disturbances. To eliminate the effect of abrupt faults and ensure the optimal performance of general no...

An adaptive reinforcement learning-based multimodal data fusion framework for human-robot confrontation gaming.

Neural networks : the official journal of the International Neural Network Society
Playing games between humans and robots have become a widespread human-robot confrontation (HRC) application. Although many approaches were proposed to enhance the tracking accuracy by combining different information, the problems of the intelligence...

Exploring Tactile Temporal Features for Object Pose Estimation during Robotic Manipulation.

Sensors (Basel, Switzerland)
Dexterous robotic manipulation tasks depend on estimating the state of in-hand objects, particularly their orientation. Although cameras have been traditionally used to estimate the object's pose, tactile sensors have recently been studied due to the...

Integrating Spatial and Temporal Information for Violent Activity Detection from Video Using Deep Spiking Neural Networks.

Sensors (Basel, Switzerland)
Increasing violence in workplaces such as hospitals seriously challenges public safety. However, it is time- and labor-consuming to visually monitor masses of video data in real time. Therefore, automatic and timely violent activity detection from vi...

An Unsupervised Classification Algorithm for Heterogeneous Cryo-EM Projection Images Based on Autoencoders.

International journal of molecular sciences
Heterogeneous three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is an important but very challenging technique for recovering the conformational heterogeneity of flexible biological macromolecules such as pro...

Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands.

Scientific reports
The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, including dementia. In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized cl...