AIMC Topic: Neural Networks, Computer

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A holistic framework for intradialytic hypotension prediction using generative adversarial networks-based data balancing.

BMC medical informatics and decision making
BACKGROUND: Intradialytic Hypotension (IDH) is a frequent complication in hemodialysis, yet predictive modeling is challenged by class imbalance. Traditional oversampling methods often struggle with complex clinical data. This study evaluates an enha...

Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training.

Scientific reports
Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fas...

A transformer-based network with second-order pooling for motor imagery EEG classification.

Journal of neural engineering
. Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning m...

Automated detection of air trapping from mechanical ventilation waveform through interpretable dual-channel 1D convolutional neural network.

Physiological measurement
. Air trapping is a major symptom of respiratory diseases like chronic obstructive pulmonary disease and asthma, and has always been a significant problem in treating patients using mechanical ventilation. If not handled timely, it can pose risk of s...

BIScreener: enhancing breast cancer ultrasound diagnosis through integrated deep learning with interpretability.

Analytical methods : advancing methods and applications
Breast cancer is the leading cause of death among women worldwide, and early detection through the standardized BI-RADS framework helps physicians assess the risk of malignancy and guide appropriate diagnostic and treatment decisions. In this study, ...

BN-SNN: Spiking neural networks with bistable neurons for object detection.

PloS one
Spiking neural networks (SNNs) are emerging as a promising evolution in neural network paradigms, offering an alternative to conventional convolutional neural networks (CNNs). One of the most effective methods for SNN development is the CNN-to-SNN co...

Multi-scale time series prediction model based on deep learning and its application.

PloS one
Time series prediction is a widely used key technology, and traffic flow prediction is its typical application scenario. Traditional time series prediction models such as LSTM (Long Short- Term Memory) and CNN (Convolution Neural Network)-based model...

Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms.

PloS one
Identifying mitosis is crucial for cancer diagnosis, but accurate detection remains difficult because of class imbalance and complex morphological variations in histopathological images. To overcome this challenge, we propose a Customized Deep Learni...

Hybrid deep learning framework for real-time DO prediction in aquaculture.

Scientific reports
Dissolved oxygen (DO) is a vital parameter in regulating water quality and sustaining the health of aquatic organisms in aquaculture environments. Therefore, estimation and control of DO levels are essential in aquaculture operations. However, tradit...

Diabetic retinopathy detection using adaptive deep convolutional neural networks on fundus images.

Scientific reports
Diabetic retinopathy (DR) is an age-related macular degeneration eye disease problem that causes pathological changes in the retinal neural and vascular system. Recently, fundus imaging is a popular technology and widely used for clinical diagnosis, ...