AIMC Topic: Neural Networks, Computer

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PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

Journal of chemical information and modeling
Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented K, a convolutional neural network that predicted the ...

Explainable Deep Learning-Assisted Fluorescence Discrimination for Aminoglycoside Antibiotic Identification.

Analytical chemistry
The complexity and multivariate analysis of biological systems and environment are the drawbacks of the current high-throughput sensing method and multianalyte identification. Deep learning (DL) algorithms contribute a big advantage in analyzing the ...

A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.

Biosensors
Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography ...

E-Commerce Picture Text Recognition Information System Based on Deep Learning.

Computational intelligence and neuroscience
For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network. At the same time, in view of the narrowing of ...

Pruning Growing Self-Organizing Map Network for Human Physical Activity Identification.

Journal of healthcare engineering
Human physical activity identification based on wearable sensors is of great significance to human health analysis. A large number of machine learning models have been applied to human physical activity identification and achieved remarkable results....

A multilayer perceptron neural network approach for the solution of hyperbolic telegraph equations.

Network (Bristol, England)
Neural networks have been extensively used for solving differential equations in the past, but they rely mostly on computationally expensive gradient-based numerical optimization procedure for solving differential equations. In this work, we are intr...

Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism.

Biomolecules
Many studies have clarified that microRNAs (miRNAs) are associated with many human diseases. Therefore, it is essential to predict potential miRNA-disease associations for disease pathogenesis and treatment. Numerous machine learning and deep learnin...

Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans.

International journal of environmental research and public health
The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it in...

Research on Multiple Spectral Ranges with Deep Learning for SpO Measurement.

Sensors (Basel, Switzerland)
Oxyhemoglobin saturation by pulse oximetry (SpO) has always played an important role in the diagnosis of symptoms. Considering that the traditional SpO measurement has a certain error due to the number of wavelengths and the algorithm and the wider a...

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.

Molecular diversity
In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this st...