AIMC Topic: Machine Learning

Clear Filters Showing 27551 to 27560 of 34417 articles

Prediction of activity type in preschool children using machine learning techniques.

Journal of science and medicine in sport
OBJECTIVES: Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting ...

Machine learning-based augmented reality for improved surgical scene understanding.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In orthopedic and trauma surgery, AR technology can support surgeons in the challenging task of understanding the spatial relationships between the anatomy, the implants and their tools. In this context, we propose a novel augmented visualization of ...

A general framework for wireless capsule endoscopy study synopsis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We present a general framework for analysis of wireless capsule endoscopy (CE) studies. The current available workstations provide a time-consuming and labor-intense work-flow for clinicians which requires the inspection of the full-length video. The...

A framework for optimal kernel-based manifold embedding of medical image data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, ...

Joint sparse coding based spatial pyramid matching for classification of color medical image.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Although color medical images are important in clinical practice, they are usually converted to grayscale for further processing in pattern recognition, resulting in loss of rich color information. The sparse coding based linear spatial pyramid match...

A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Tissue texture is known to exhibit a heterogeneous or non-stationary nature; therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subbands' textural fractal ch...

A lifelong learning hyper-heuristic method for bin packing.

Evolutionary computation
We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and h...

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.

Medical decision making : an international journal of the Society for Medical Decision Making
BACKGROUND: Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, ...

Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques.

Cerebral cortex (New York, N.Y. : 1991)
Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associate...

Aggregate features in multisample classification problems.

IEEE journal of biomedical and health informatics
This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditiona...