AIMC Topic: Algorithms

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A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues.

Journal of biomedical informatics
In the last few years, the application of Machine Learning approaches like Deep Neural Network (DNN) models have become more attractive in the healthcare system given the rising complexity of the healthcare data. Machine Learning (ML) algorithms prov...

Extraction of the molecular level biomedical event trigger based on gene ontology using radial belief neural network techniques.

Bio Systems
Detection of molecular level biomedical event extraction plays a vital role in creating and visualizing the applications related to natural language processing. Cystic Fibrosis is an inherited genetic and debilitating pathology involving the respirat...

Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER.

CNS neuroscience & therapeutics
AIMS: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate...

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.

International journal of molecular sciences
Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. ...

Mitigation of ocular artifacts for EEG signal using improved earth worm optimization-based neural network and lifting wavelet transform.

Computer methods in biomechanics and biomedical engineering
An Electroencephalogram (EEG) is often tarnished by various categories of artifacts. Numerous efforts have been taken to improve its quality by eliminating the artifacts. The EEG involves the biological artifacts (ocular artifacts, ECG and EMG artifa...

Approximation rates for neural networks with encodable weights in smoothness spaces.

Neural networks : the official journal of the International Neural Network Society
We examine the necessary and sufficient complexity of neural networks to approximate functions from different smoothness spaces under the restriction of encodable network weights. Based on an entropy argument, we start by proving lower bounds for the...

Deep-learned spike representations and sorting via an ensemble of auto-encoders.

Neural networks : the official journal of the International Neural Network Society
Spike sorting refers to the technique of detecting signals generated by single neurons from multi-neuron recordings and is a valuable tool for analyzing the relationships between individual neuronal activity patterns and specific behaviors. Since the...

Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network.

Neural networks : the official journal of the International Neural Network Society
This work is aimed to study experimental and theoretical approaches for searching effective local training rules for unsupervised pattern recognition by high-performance memristor-based Spiking Neural Networks (SNNs). First, the possibility of weight...

Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks.

Medical physics
BACKGROUND AND PURPOSE: Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric mag...

Bidirectional Attention for Text-Dependent Speaker Verification.

Sensors (Basel, Switzerland)
Automatic speaker verification provides a flexible and effective way for biometric authentication. Previous deep learning-based methods have demonstrated promising results, whereas a few problems still require better solutions. In prior works examini...