AIMC Topic: Machine Learning

Clear Filters Showing 25721 to 25730 of 34417 articles

Self-learning robust optimal control for continuous-time nonlinear systems with mismatched disturbances.

Neural networks : the official journal of the International Neural Network Society
This paper presents a novel adaptive dynamic programming(ADP)-based self-learning robust optimal control scheme for input-affine continuous-time nonlinear systems with mismatched disturbances. First, the stabilizing feedback controller for original n...

Enhancement of force patterns classification based on Gaussian distributions.

Journal of biomechanics
Description of the patterns of ground reaction force is a standard method in areas such as medicine, biomechanics and robotics. The fundamental parameter is the time course of the force, which is classified visually in particular in the field of clin...

Vector representations of multi-word terms for semantic relatedness.

Journal of biomedical informatics
This paper presents a comparison between several multi-word term aggregation methods of distributional context vectors applied to the task of semantic similarity and relatedness in the biomedical domain. We compare the multi-word term aggregation met...

Differential Compound Prioritization via Bidirectional Selectivity Push with Power.

Journal of chemical information and modeling
Effective in silico compound prioritization is a critical step to identify promising drug candidates in the early stages of drug discovery. Current computational methods for compound prioritization usually focus on ranking the compounds based on one ...

Identification of human circadian genes based on time course gene expression profiles by using a deep learning method.

Biochimica et biophysica acta. Molecular basis of disease
Circadian genes express periodically in an approximate 24-h period and the identification and study of these genes can provide deep understanding of the circadian control which plays significant roles in human health. Although many circadian gene ide...

Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then...

Breast cancer data analysis for survivability studies and prediction.

Computer methods and programs in biomedicine
BACKGROUND: Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to asses...

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and ...

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

EBioMedicine
BACKGROUND: Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an...

A cryptography-based approach for movement decoding.

Nature biomedical engineering
Brain decoders use neural recordings to infer the activity or intent of a user. To train a decoder, one generally needs to infer the measured variables of interest (covariates) from simultaneously measured neural activity. However, there are cases fo...