AIMC Topic: Neural Networks, Computer

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[Methodology and progress in adjusting time-dependent covariates in clinical prediction models].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
Adjusting time-dependent covariates into prediction models may help improve model performance and expand clinical applications. The methodology of handling time-dependent covariates is limited in traditional regression strategies (i.e., landmark mode...

Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.

Neural computation
Understanding the effect of spike-timing-dependent plasticity (STDP) is key to elucidating how neural networks change over long timescales and to design interventions aimed at modulating such networks in neurological disorders. However, progress is r...

Deep Learning for Multi-Tissue Segmentation and Fully Automatic Personalized Biomechanical Models from BACPAC Clinical Lumbar Spine MRI.

Pain medicine (Malden, Mass.)
STUDY DESIGN: In vivo retrospective study of fully automatic quantitative imaging feature extraction from clinically acquired lumbar spine magnetic resonance imaging (MRI).

Small-correlation expansion to quantify information in noisy sensory systems.

Physical review. E
Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with a complex interplay between correlations induced by the stimulus, and correl...

Prediction of the roughness coefficient for drainage pipelines with sediments using GA-BPNN.

Water science and technology : a journal of the International Association on Water Pollution Research
Accurate prediction of the roughness coefficient of sediment-containing drainage pipes can help engineers optimize urban drainage systems. In this paper, the variation of the roughness coefficient of circular drainage pipes containing different thick...

A novel approach to precipitation prediction using a coupled CEEMDAN-GRU-Transformer model with permutation entropy algorithm.

Water science and technology : a journal of the International Association on Water Pollution Research
The accurate forecasting of precipitation in the upper reaches of the Yellow River is imperative for enhancing water resources in both the local and broader Yellow River basin in the present and future. While many models exist for predicting precipit...

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.

Water science and technology : a journal of the International Association on Water Pollution Research
Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle t...

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.

Bioinformatics (Oxford, England)
MOTIVATION: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as...

A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model.

Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES
BACKGROUND: The article was planned to make the first evaluation in terms of acute subdural hemorrhages, thinking that it can help in appropriate pathologies by tomography interpretation with the artificial intelligence (AI) method, at least in a way...

MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.

Bioinformatics (Oxford, England)
MOTIVATION: CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area...