AIMC Topic: Random Allocation
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Robotic finger perturbation training improves finger postural steadiness and hand dexterity.
Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Nov 22, 2017
The purpose of the study was to understand the effect of robotic finger perturbation training on steadiness in finger posture and hand dexterity in healthy young adults. A mobile robotic finger training system was designed to have the functions of hi...
Ant-inspired density estimation via random walks.
Proceedings of the National Academy of Sciences of the United States of America
Sep 19, 2017
Many ant species use distributed population density estimation in applications ranging from quorum sensing, to task allocation, to appraisal of enemy colony strength. It has been shown that ants estimate local population density by tracking encounter...
NIRCa: An artificial neural network-based insulin resistance calculator.
Pediatric diabetes
Jun 19, 2017
BACKGROUND: Direct measurement of insulin sensitivity in children with type 1 diabetes is cumbersome and time consuming.
Humans
Neural Networks, Computer
Machine Learning
Cross-Sectional Studies
Male
Adolescent
Child
Female
Reproducibility of Results
Young Adult
Adult
Random Allocation
Diabetes Mellitus, Type 1
Multivariate Analysis
Hypertension
Glucose Clamp Technique
Insulin Resistance
Predictive Value of Tests
Prehypertension
Diabetic Angiopathies
Puberty
Computational Biology
Robot education peers in a situated primary school study: Personalisation promotes child learning.
PloS one
May 23, 2017
The benefit of social robots to support child learning in an educational context over an extended period of time is evaluated. Specifically, the effect of personalisation and adaptation of robot social behaviour is assessed. Two autonomous robots wer...
Subject-based discriminative sparse representation model for detection of concealed information.
Computer methods and programs in biomedicine
Feb 20, 2017
BACKGROUND AND OBJECTIVES: The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each s...
H state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through Fading channels.
Neural networks : the official journal of the International Neural Network Society
Feb 13, 2017
In this paper, the H state estimation problem is investigated for a class of uncertain discrete-time neural networks subject to infinitely distributed delays and fading channels. Randomly occurring uncertainties (ROUs) are introduced to reflect the r...
A new neural network model for solving random interval linear programming problems.
Neural networks : the official journal of the International Neural Network Society
Feb 9, 2017
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming prob...
Metabolic changes in rat serum after administration of suberoylanilide hydroxamic acid and discriminated by SVM.
Human & experimental toxicology
Jan 13, 2017
Suberoylanilide hydroxamic acid (SAHA) exerts marked anticancer effects via promotion of apoptosis, cell cycle arrest, and prevention of oncogene expression. In this study, serum metabolomics and artificial intelligence recognition were used to inves...
Regional muscle tissue saturation is an indicator of global inadequate circulation during cardiopulmonary bypass: a randomized porcine study using muscle, intestinal and brain tissue metabolomics.
Perfusion
Oct 17, 2016
BACKGROUND: Muscle tissue saturation (StO) measured with near-infrared spectroscopy has generally been considered a measurement of the tissue microcirculatory condition. However, we hypothesized that StO could be more regarded as a fast and reliable ...
Emergence of low noise frustrated states in E/I balanced neural networks.
Neural networks : the official journal of the International Neural Network Society
Sep 8, 2016
We study emerging phenomena in binary neural networks where, with a probability c synaptic intensities are chosen according with a Hebbian prescription, and with probability (1-c) there is an extra random contribution to synaptic weights. This new te...
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