AIMC Topic: Humans

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Synaptic Scaling-An Artificial Neural Network Regularization Inspired by Nature.

IEEE transactions on neural networks and learning systems
Nature has always inspired the human spirit and scientists frequently developed new methods based on observations from nature. Recent advances in imaging and sensing technology allow fascinating insights into biological neural processes. With the obj...

Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems
Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space ...

Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network.

IEEE transactions on neural networks and learning systems
Real-time in situ image analytics impose stringent latency requirements on intelligent neural network inference operations. While conventional software-based implementations on the graphic processing unit (GPU)-accelerated platforms are flexible and ...

BiCoSS: Toward Large-Scale Cognition Brain With Multigranular Neuromorphic Architecture.

IEEE transactions on neural networks and learning systems
The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the correspond...

A structural characterization of shortcut features for prediction.

European journal of epidemiology
With the rising use of machine learning for healthcare applications, practitioners are increasingly confronted with the limitations of prediction models that are trained in one setting but meant to be deployed in several others. One recently identifi...

Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network.

Computational intelligence and neuroscience
Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performan...

Deep Learning-Based Mental Health Model on Primary and Secondary School Students' Quality Cultivation.

Computational intelligence and neuroscience
The purpose was to timely identify the mental disorders (MDs) of students receiving primary and secondary education (PSE) (PSE students) and improve their mental quality. Firstly, this work analyzes the research status of the mental health model (MHM...

Robot-assisted groin hernia repair is primarily performed by specialized surgeons: a scoping review.

Journal of robotic surgery
Surgical residents routinely participate in open and laparoscopic groin hernia repairs. The increasing popularity of robot-assisted groin hernia repair could lead to an educational loss for residents. We aimed to explore the involvement of surgical s...

Comparison of robot-assisted versus fluoroscopy-assisted minimally invasive transforaminal lumbar interbody fusion for degenerative lumbar spinal diseases: 2-year follow-up.

Journal of robotic surgery
This study was performed to prospectively compare the clinical and radiographic outcomes between robot-assisted minimally invasive transforaminal lumbar interbody fusion (RA MIS-TLIF) and fluoroscopy-assisted minimally invasive transforaminal lumbar ...

Automatic measurement of the patellofemoral joint parameters in the Laurin view: a deep learning-based approach.

European radiology
OBJECTIVES: To explore the performance of a deep learning-based algorithm for automatic patellofemoral joint (PFJ) parameter measurements from the Laurin view.