AIMC Topic: Support Vector Machine

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Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data.

Journal of animal science
This paper presents the application of machine learning algorithms to identify pigs' behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig's back senses the acceleration and angular velocity ...

Light field quality assessment based on aggregation learning of multiple visual features.

Optics express
Light field imaging is a way to represent human vision from a computational perspective. It contains more visual information than traditional imaging systems. As a basic problem of light field imaging, light field quality assessment has received exte...

Speckle classification of a multimode fiber based on Inception V3.

Applied optics
Multimode optical fiber plays an important role in endoscope miniaturization. With the development of deep learning and machine learning, neural networks can be used to identify and classify speckle patterns obtained at the fiber output. Based on the...

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging.

Journal of biomedical optics
SIGNIFICANCE: Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the developme...

PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection.

Briefings in bioinformatics
A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular me...

[Rapid identification of geographic origins of Zingiberis Rhizoma by NIRS combined with chemometrics and machine learning algorithms].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
In this study, 280 batches of Zingiberis Rhizoma samples from nine producing areas were analyzed to obtain infrared spectral information based on near-infrared spectroscopy(NIRS). Pluralistic chemometrics such as principal component analysis(PCA), pa...

[ST segment morphological classification based on support vector machine multi feature fusion].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and inter...

Machine fault detection methods based on machine learning algorithms: A review.

Mathematical biosciences and engineering : MBE
Preventive identification of mechanical parts failures has always played a crucial role in machine maintenance. Over time, as the processing cycles are repeated, the machinery in the production system is subject to wear with a consequent loss of tech...

[Comparison of the predictive performance of Logistic regression, BP neural network and support vector machine model for the risk of acute exacerbation of readmission in elderly patients with chronic obstructive pulmonary disease within 30 days].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To compare the effectiveness of Logistic regression, BP neural network and support vector machine models in the prediction of 30-day risk of readmission in elderly patients with an exacerbation of chronic obstructive pulmonary disease (COP...