AIMC Topic: Neural Networks, Computer

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Optimizing intervertebral disc cell metabolic phenotyping with machine learning and artificial neural networks.

Scientific reports
Biological phenotyping of cellular metabolism is essential for deciphering health and disease states. The Seahorse XF analyzer enables direct measurement of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), providing insight ...

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.

eLife
Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing the processing ...

Indoor location perception model based on Resnet50 and Elman network.

PloS one
The visible light indoor position perception method not only solves the limitations of traditional positioning technology indoors, but also promotes innovation in fields such as smart retail and healthcare with its advantages of high accuracy and low...

Improved ADME Prediction by Multitask Pretraining on Predicted Data: Insights from the ASAP-Polaris-OpenADMET Blind Challenge.

Journal of chemical information and modeling
Absorption, distribution, metabolism, and excretion (ADME) properties are among the key factors in determining the success of lead discovery and optimization campaigns. Fast and accurate prediction of molecular ADME profiles is hence of particular in...

Fast and accurate visual acuity prediction based on optical aberrations and machine learning.

Scientific reports
In this work, we propose three machine learning-based methods for predicting visual acuity (VA). Two methods utilize regression trees (LSBoost and XGBoost), and the third employs a neural network that classifies simulated aberrated optotypes as "reco...

CABNas-nir: A near-infrared classification for urban pipe network sludge on the fusion algorithm of NAS framework and active learning.

PloS one
Pipe network sludge is a complex pollutant aggregate deposited during long-term operation of urban sewage pipelines, and a key target for pollution control in environmental monitoring systems. Accurate source classification is critical for treatment ...

SynSeg: A synthetic data-driven approach for robust subcellular structure segmentation.

The Journal of cell biology
Accurate subcellular segmentation is crucial for understanding cellular processes, but traditional methods struggle with noise and complex structures. Convolutional neural networks improve accuracy but require large, time-consuming, and biased manual...

Flexible state space modelling for accurate and efficient 3D lung nodule detection.

Biomedical physics & engineering express
Early and accurate detection of pulmonary nodules in computed tomography (CT) scans is critical for reducing lung cancer mortality. While convolutional neural networks (CNNs) and Transformer-based architectures have been widely used for this task, th...

Dual-channel TRCA-net based on cross-subject positive transfer for SSVEP-BCI.

Biomedical physics & engineering express
. To enhance the decoding accuracy and information transfer rate of steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) systems and to reduce inter-subject variability for broader SSVEP-BCI applications, a dual-channel TRC...

Incorporating multi-modal prompt learning into foundation models enhances predictability of visual fMRI responses to dynamic natural stimuli.

Journal of neural engineering
. Modeling neural encoding of visual stimuli often uses deep neural networks (DNNs) to predict human brain response to external stimuli. However, each DNN depends on networks tailored for computer vision tasks, resulting in suboptimal brain correspon...