AIMC Topic: Neural Networks, Computer

Clear Filters Showing 17321 to 17330 of 31376 articles

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees.

Sensors (Basel, Switzerland)
The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational...

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.

Nature communications
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits t...

LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.

BioMed research international
Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods wer...

Signal-piloted processing and machine learning based efficient power quality disturbances recognition.

PloS one
Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of m...

Sensitivity and generalized analytical sensitivity expressions for quantitative analysis using convolutional neural networks.

Analytica chimica acta
In recent years, convolutional neural networks and deep neural networks have been used extensively in various fields of analytical chemistry. The use of these models for calibration tasks has been highly effective; however, few reports have been publ...

Spectral embedding network for attributed graph clustering.

Neural networks : the official journal of the International Neural Network Society
Attributed graph clustering aims to discover node groups by utilizing both graph structure and node features. Recent studies mostly adopt graph neural networks to learn node embeddings, then apply traditional clustering methods to obtain clusters. Ho...

Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning.

Physical and engineering sciences in medicine
Early diagnosis of attention deficit and hyperactivity disorder (ADHD) by experts is difficult. Some solutions using electroencephalography (EEG) signals have been presented in the literature to solve this problem. However, few studies have aimed to ...

Geometric deep learning on brain shape predicts sex and age.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about t...

An artificial neural network-pharmacokinetic model and its interpretation using Shapley additive explanations.

CPT: pharmacometrics & systems pharmacology
We developed a method to apply artificial neural networks (ANNs) for predicting time-series pharmacokinetics (PKs), and an interpretable the ANN-PK model, which can explain the evidence of prediction by applying Shapley additive explanations (SHAP). ...

Deep Learning-Based Conformal Prediction of Toxicity.

Journal of chemical information and modeling
Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately qu...