AIMC Topic: Neural Networks, Computer

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Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization.

Scientific reports
Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing the amount of toxic...

Advanced MRI based Alzheimer's diagnosis through ensemble learning techniques.

Scientific reports
Alzheimer's Disease is a condition that affects the brain and causes changes in behavior and memory loss while making it hard to carry out tasks properly. It's vital to spot the illness early, for effective treatment. MRI technology has advanced in d...

Efficient and robust temporal processing with neural oscillations modulated spiking neural networks.

Nature communications
The brain exhibits rich dynamical properties that underpin its remarkable temporal processing capabilities. However, spiking neural networks (SNNs) inspired by the brain have not yet matched their biological counterparts in temporal processing and re...

A Pretraining Approach for Small-sample Training Employing Radiographs (PASTER): a Multimodal Transformer Trained by Chest Radiography and Free-text Reports.

Journal of medical systems
While deep convolutional neural networks (DCNNs) have achieved remarkable performance in chest X-ray interpretation, their success typically depends on access to large-scale, expertly annotated datasets. However, collecting such data in real-world cl...

TFDISNet: Temporal-frequency domain-invariant and domain-specific feature learning network for enhanced auditory attention decoding from EEG signals.

Biomedical physics & engineering express
Auditory Attention Decoding (AAD) from Electroencephalogram (EEG) signals presents a significant challenge in brain-computer interface (BCI) research due to the intricate nature of neural patterns. Existing approaches often fail to effectively integr...

Hybrid-MedNet: a hybrid CNN-transformer network with multi-dimensional feature fusion for medical image segmentation.

Physics in medicine and biology
Twin-to-twin transfusion syndrome (TTTS) is a complex prenatal condition in which monochorionic twins experience an imbalance in blood flow due to abnormal vascular connections in the shared placenta. Fetoscopic laser photocoagulation is the first-li...

DiabetesXpertNet: An innovative attention-based CNN for accurate type 2 diabetes prediction.

PloS one
Type 2 diabetes mellitus remains a critical global health challenge, with rising incidence rates placing immense pressure on healthcare systems worldwide. This chronic metabolic disorder affects diverse populations, including the elderly and children...

PSCG-Net: A Multiscale Crystal Graph Neural Network for Accelerated Materials Discovery.

Journal of chemical information and modeling
The discovery of new materials is crucial for progress in energy, electronics, and sustainable technology. Traditional machine learning approaches, including graph neural networks (GNNs), often fall short because they cannot capture long-range intera...

Molecular Dynamics and Neural Network Analysis Reveal Sequential Gating and Allosteric Communication in FMRFamide-Activated Sodium Channels.

Journal of chemical information and modeling
FMRFamide-activated sodium channels (FaNaCs) represent a unique class of neuropeptide-gated ion channels within the degenerin/epithelial sodium channel (DEG/ENaC) superfamily. While cryo-electron microscopy has revealed static binding architectures, ...

A deep learning model for epidermal growth factor receptor prediction using ensemble residual convolutional neural network.

Scientific reports
Epidermal growth factor receptor (EGFR) overexpression is a key oncogenic driver in breast cancer, making it an important therapeutic target. Conventional approaches for EGFR identification, including motif- and homology-based methods, often lack acc...