AIMC Topic: Neural Networks, Computer

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Machine and deep learning applied to medical microwave imaging: a scoping review from reconstruction to classification.

Progress in biomedical engineering (Bristol, England)
Microwave imaging (MWI) is a promising modality due to its non-invasive nature and lower cost compared to other medical imaging techniques. These characteristics make it a potential alternative to traditional imaging techniques. It has various medica...

Deep memory for deep threats: A novel architecture combining GRUs and deep learning models for IDS.

PloS one
The increasing volumes and sophistication of cyber threats, particularly Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks, pose significant dangers to contemporary network structures, particularly the Internet of Things (IoT) ...

Orthogonal position representations for transformer in neural machine translation.

PloS one
In recent years, the Transformer architecture has solidified its position as the dominant model in neural machine translation (NMT), thanks to its exceptional effectiveness in capturing long-range dependencies and remarkable scalability across divers...

Discrimination of urine infrared spectral biomarkers for early-stage chronic kidney disease patients using attenuated total reflectance fourier transform infrared spectrometry.

Clinica chimica acta; international journal of clinical chemistry
Chronic Kidney Disease (CKD) is a highly prevalent non-communicable disorder lacking a gold standard method for diagnosis. Early-stage CKD remains undiagnosed and untreated leading to the disease progression. The study aimed to discriminate urine sam...

A deep learning approach to artifact removal in Transcranial Electrical Stimulation: From shallow methods to deep neural networks and state space models.

Neuroscience
Transcranial Electrical Stimulation (tES) is a non-invasive neuromodulation technique that generates artifacts in simultaneous EEG recordings, hindering brain activity analysis. This study analyzes Machine Learning (ML) methods for tES noise artifact...

Influencing factors for childbirth readiness among pregnant women based on the reciprocal determinism theory and backpropagation neural network: a cross-sectional study in China.

BMC pregnancy and childbirth
BACKGROUND: Childbirth readiness is essential for improving maternal health outcomes and reducing mortality, yet preparedness remains low among pregnant women globally. This study aims to identify key factors influencing childbirth readiness among Ch...

Quantitative inversion of soil heavy metal pollution using a GA-BP neural network model.

Environmental monitoring and assessment
With the rapid development of industrialization in China, significant economic benefits have been accompanied by varying degrees of threat to the soil environment, particularly from heavy metal pollution. The rapid quantitative inversion of heavy met...

Automated OSAHS detection from ECG using temporal convolutional network.

Scientific reports
Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a prevalent systemic disorder affecting approximately 1 billion people worldwide, associated with severe outcomes such as sudden death and traffic accidents. Despite its significant impact, OSAHS i...

A novel approach hybrid of ensemble learning and 3-D CNN mechanism: early-stage diagnosis of Alzheimer's disease using EEG signals.

Scientific reports
Alzheimer's disease (AD) is a progressive neurological disorder that causes brain cell degeneration and leads to dementia. Early and accurate detection of AD is crucial, as it allows timely treatment before the brain suffers permanent damage. In rece...

An incremental adversarial training method enables timeliness and rapid new knowledge acquisition.

Scientific reports
Adversarial training is an effective defense method for deep models against adversarial attacks. However, current adversarial training methods require retraining the entire neural network, which consumes a significant amount of computational resource...