AIMC Topic: Machine Learning

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[The Identification of Lettuce Varieties by Using Unsupervised Possibilistic Fuzzy Learning Vector Quantization and Near Infrared Spectroscopy].

Guang pu xue yu guang pu fen xi = Guang pu
To solve the noisy sensitivity problem of fuzzy learning vector quantization (FLVQ), unsupervised possibilistic fuzzy learning vector quantization (UPFLVQ) was proposed based on unsupervised possibilistic fuzzy clustering (UPFC). UPFLVQ aimed to use ...

Reducing streak artifacts in computed tomography via sparse representation in coupled dictionaries.

Medical physics
PURPOSE: Reducing the number of acquired projections is a simple and efficient way to reduce the radiation dose in computed tomography (CT). Unfortunately, this results in streak artifacts in the reconstructed images that can significantly reduce the...

Automatic labeling of MR brain images by hierarchical learning of atlas forests.

Medical physics
PURPOSE: Automatic brain image labeling is highly demanded in the field of medical image analysis. Multiatlas-based approaches are widely used due to their simplicity and robustness in applications. Also, random forest technique is recognized as an e...

BIT: Biologically Inspired Tracker.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change, and occlusion. Given the superior tracking performance of human visual system (HVS), an ideal design of b...

Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein-protein interactions using machine learning methods.

Molecular bioSystems
Protein-protein interactions (PPIs) play a vital role in most biological processes. Hence their comprehension can promote a better understanding of the mechanisms underlying living systems. However, besides the cost and the time limitation involved i...

Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus.

Journal of cataract and refractive surgery
PURPOSE: To describe the topographic and tomographic characteristics of normal fellow eyes of unilateral keratoconus cases and to evaluate the accuracy of machine learning classifiers in discriminating healthy corneas from the normal fellow corneas.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling.

Medical physics
PURPOSE: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical ...

Sparse electrocardiogram signals recovery based on solving a row echelon-like form of system.

IET systems biology
The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine...