AIMC Topic: Machine Learning

Clear Filters Showing 25101 to 25110 of 34417 articles

Natural Language Processing for EHR-Based Computational Phenotyping.

IEEE/ACM transactions on computational biology and bioinformatics
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, no...

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

ACS chemical neuroscience
Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein...

Transductive Joint-Knowledge-Transfer TSK FS for Recognition of Epileptic EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic EEG signals are usually based on two assumptions: 1) adequate training examples are available for mod...

An Extreme Learning Machine Based on Artificial Immune System.

Computational intelligence and neuroscience
Extreme learning machine algorithm proposed in recent years has been widely used in many fields due to its fast training speed and good generalization performance. Unlike the traditional neural network, the ELM algorithm greatly improves the training...

Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine.

Environmental pollution (Barking, Essex : 1987)
In order to improve the prediction accuracy and real-time of the air pollutant concentration prediction, this paper proposes self-adaptive neuro-fuzzy weighted extreme learning machine (ANFIS-WELM) based on the weighted extreme learning machine (WELM...

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

Journal of neuroscience methods
BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation.

Abdominal-Waving Control of Tethered Bumblebees Based on Sarsa With Transformed Reward.

IEEE transactions on cybernetics
Cyborg insects have attracted great attention as the flight performance they have is incomparable by micro aerial vehicles and play a critical role in supporting extensive applications. Approaches to construct cyborg insects consist of two major issu...

A novel integrated action crossing method for drug-drug interaction prediction in non-communicable diseases.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Drug-drug interaction (DDI) is one of the main causes of toxicity and treatment inefficacy. This work focuses on non-communicable diseases (NCDs), the non-transmissible and long-lasting diseases since they are the leading ca...

On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines.

Neural networks : the official journal of the International Neural Network Society
In this paper, we analyze the role of hidden bias in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines (GBPRBMs), which are similar to the widely used Gaussian-Bernoulli RBMs. Our experiments show that hidden bias play...