AIMC Topic: Neural Networks, Computer

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Alzheimer's disease prediction via an explainable CNN using genetic algorithm and SHAP values.

PloS one
Convolutional neural networks (CNNs) are widely recognized for their high precision in image classification. Nevertheless, the lack of transparency in these black-box models raises concerns in sensitive domains such as healthcare, where understanding...

Skin disease diagnostics through federated transfer learning on heterogeneous data.

Scientific reports
Skin diseases frequently cause mental and physical distress and are major global health concern. Because early detection is crucial to successful treatment, accurate diagnosis is challenge for dermatologists as well. Diagnostic accuracy could be sign...

Pediatric diabetes prediction using machine learning.

Scientific reports
Diabetes is a chronic condition that affects a substantial portion of the global population and is linked to elevated mortality rates and a range of severe health complications. Despite its clinical importance, progress in diabetes research is often ...

Explainable AI for pain perception: subject-independent EEG decoding using DeepSHAP and CNNs.

Biomedical physics & engineering express
Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learni...

ACFSENet: an adaptive cross-frequency global sparse encoding network for end-to-end EEG emotion recognition.

Biomedical physics & engineering express
End-to-end EEG-based emotion recognition is attracting increasing attention due to its potential in human-computer interaction, mental health, and affective brain-computer interfaces (aBCIs). However, most existing methods overlook cross-frequency in...

LMProtein: a protein language model based framework for protein structural property prediction.

Physical chemistry chemical physics : PCCP
Recent advances in machine learning and self-supervised deep language modeling have made it possible to accurately predict protein structural properties. Most existing models and pretraining methods leverage evolutionary information in multiple seque...

ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.

Genetics, selection, evolution : GSE
BACKGROUND: Various methods have been widely utilized to estimate the genomic breeding values (GEBVs) for genomic prediction. Traditional approaches often relied on the assumption of linear regression models, which struggle to effectively capture the...

CTA-based deep-learning integrated model for identifying irregular shape and aneurysm size of unruptured intracranial aneurysms.

Journal of neurointerventional surgery
BACKGROUND: Artificial intelligence can help to identify irregular shapes and sizes, crucial for managing unruptured intracranial aneurysms (UIAs). However, existing artificial intelligence tools lack reliable classification of UIA shape irregularity...

Research on missing value prediction of measured ERT data for coal mine based on a GRNN algorithm.

PloS one
In the process of long-term monitoring of the coal seam floor of a coal mining face using electrical resistivity tomography (ERT), the data loss caused by electrode disconnection adversely affects early warning of water inrush and prevents the identi...

RUL prediction method based on sequential health index evaluation with multidimensional coupled degradation data.

PloS one
Remaining Useful Life (RUL) prediction is crucial for implementing predictive maintenance strategies, however, RUL prediction is severely constrained by the lack of high-quality labeled life-cycle data. Moreover, complex coupling relationships exist ...