AIMC Topic: Neural Networks, Computer

Clear Filters Showing 181 to 190 of 29777 articles

GCN-based unsupervised community detection with refined structure centers and expanded pseudo-labeled set.

PloS one
Community detection is a classical problem for analyzing the structures of various graph-structured data. An efficient approach is to expand the community structure from a few structure centers based on the graph topology. Considering them as pseudo-...

A fast (CNN + MCWS-transformer) based architecture for protein function prediction.

Statistical applications in genetics and molecular biology
The transformer model for sequence mining has brought a paradigmatic shift to many domains, including biological sequence mining. However, transformers suffer from quadratic complexity, i.e., O( ), where is the sequence length, which affects the tra...

AI-augmented differential diagnosis of granulomatous rosacea and lupus miliaris disseminatus faciei: A 23-year retrospective pilot study.

PloS one
Granulomatous rosacea (GR) and lupus miliaris disseminatus faciei (LMDF) exhibit overlapping clinical features, making their differentiation challenging. While histopathological examination remains the gold standard, it is invasive and time-consuming...

The singing style of female roles in ethnic opera under artificial intelligence and deep neural networks.

Scientific reports
With the rapid advancement of artificial intelligence technology, efficiently extracting and analyzing music performance style features has become an important topic in the field of music information processing. This work focuses on the classificatio...

Leveraging large language models for patient-ventilator asynchrony detection.

BMJ health & care informatics
OBJECTIVES: The objective of this study is to evaluate whether large language models (LLMs) can achieve performance comparable to expert-developed deep neural networks in detecting flow starvation (FS) asynchronies during mechanical ventilation.

.

International ophthalmology
UNLABELLED: Early detection of glaucoma represents a vital factor in securing vision while the disease retains its position as one of the central causes of blindness worldwide. The current glaucoma screening strategies with expert interpretation depe...

Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.

Journal of neural engineering
Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studi...

A comparative study of fully automatic and semi-automatic methods for oil spill detection using Sentinel-1 data.

Environmental monitoring and assessment
The oil spill detection and assessment study conducted in the Banten Province of Indonesia involves the application of Sentinel-1 satellite data and machine learning tools in the year 2024. Synthetic Aperture Radar (SAR) data were used with VV polari...

A novel dual-branch network for comprehensive spatiotemporal information integration for EEG-based epileptic seizure detection.

PloS one
Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal brain activity, which can severely affects people's normal lives. To improve the lives of these patients, it is necessary to develop accurate methods to predic...

A novel twin time series network for building energy consumption predicting.

PloS one
Energy consumption prediction in buildings is crucial for optimizing energy management. The latest research faces three critical challenges: (1) Insufficient temporal correlation extraction and prediction accuracy, hindering widespread adoption and a...