AIMC Topic: Algorithms

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Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

Computational and mathematical methods in medicine
The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and ar...

Supervised Filter Learning for Representation Based Face Recognition.

PloS one
Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original ...

Gene-Disease Interaction Retrieval from Multiple Sources: A Network Based Method.

BioMed research international
The number of gene-related databases has been growing largely along with the research on genes of bioinformatics. Those databases are filled with various gene functions, pathways, interactions, and so forth, while much biomedical knowledge about huma...

Multiclass imbalance learning: Improving classification of pediatric brain tumors from magnetic resonance spectroscopy.

Magnetic resonance in medicine
PURPOSE: Classification of pediatric brain tumors from H-magnetic resonance spectroscopy (MRS) can aid diagnosis and management of brain tumors. However, varied incidence of the different tumor types leads to imbalanced class sizes and introduces di...

Event-triggered H∞ filter design for delayed neural network with quantization.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with H∞ filter design for a class of neural network systems with event-triggered communication scheme and quantization. Firstly, a new event-triggered communication scheme is introduced to determine whether or not the current ...

Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.

Clinical EEG and neuroscience
The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction...

Complex Neural Network Models for Time-Varying Drazin Inverse.

Neural computation
Two complex Zhang neural network (ZNN) models for computing the Drazin inverse of arbitrary time-varying complex square matrix are presented. The design of these neural networks is based on corresponding matrix-valued error functions arising from the...

Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG sign...

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

Environmental science and pollution research international
In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained bas...

Dissipativity analysis of stochastic memristor-based recurrent neural networks with discrete and distributed time-varying delays.

Network (Bristol, England)
In this paper, based on the knowledge of memristor-based recurrent neural networks (MRNNs), the model of the stochastic MRNNs with discrete and distributed delays is established. In real nervous systems and in the implementation of very large-scale i...