AIMC Topic: Algorithms

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Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble.

BMC bioinformatics
BACKGROUND: Antifreeze proteins (AFPs) are a group of proteins that inhibit body fluids from growing to ice crystals and thus improve biological antifreeze ability. It is vital to the survival of living organisms in extremely cold environments. Howev...

Adaptive Fuzzy Sliding Mode Control of a Pressure-Controlled Artificial Ventilator.

Journal of healthcare engineering
This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient's lung mod...

A Fuzzy Radial Basis Adaptive Inference Network and Its Application to Time-Varying Signal Classification.

Computational intelligence and neuroscience
A fuzzy radial basis adaptive inference network (FRBAIN) is proposed for multichannel time-varying signal fusion analysis and feature knowledge embedding. The model which combines the prior signal feature embedding mechanism of the radial basis kerne...

Impact of chart image characteristics on stock price prediction with a convolutional neural network.

PloS one
Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as AN...

COVID-19 deep classification network based on convolution and deconvolution local enhancement.

Computers in biology and medicine
Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection ...

MEG Source Localization via Deep Learning.

Sensors (Basel, Switzerland)
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole s...

A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks.

Sensors (Basel, Switzerland)
Cyber-physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troub...

scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.

Nature communications
Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will e...

Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.

PloS one
Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficu...

Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example.

PloS one
The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system throu...