AIMC Topic: Support Vector Machine

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Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification.

International journal of environmental research and public health
Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoietic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional ...

Computational learning of features for automated colonic polyp classification.

Scientific reports
Shape, texture, and color are critical features for assessing the degree of dysplasia in colonic polyps. A comprehensive analysis of these features is presented in this paper. Shape features are extracted using generic Fourier descriptor. The nonsubs...

A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition.

Journal of biophotonics
It is a practical necessity for non-professional users to interpret biologically derived Raman spectral information for obtaining accurate and reliable analytical results. An integrated Raman spectral analysis software (NWUSA) was developed for spect...

FOPR test: a virtual reality-based technique to assess field of perception and field of regard in hemispatial neglect.

Journal of neuroengineering and rehabilitation
BACKGROUND: We previously proposed a novel virtual reality-based method to assess human field of perception (FOP) and field of regard (FOR), termed the FOPR test. This study assessed the diagnostic validity of the FOPR test for hemispatial neglect (H...

Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
How to quickly and accurately select sugarbeet seeds with reliable germination is very important to sugarbeet planting. In this study, the hyperspectral images of 3072 sugarbeet seeds of the same variety were collected, and were successively processe...

A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine.

Physical and engineering sciences in medicine
Breast cancer continues to be a widespread health concern all over the world. Mammography is an important method in the early detection of breast abnormalities. In recent years, using an automatic Computer-Aided Detection (CAD) system based on image ...

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data.

Laboratory investigation; a journal of technical methods and pathology
Most biomedical datasets, including those of 'omics, population studies, and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand consider...

Machine Learning-Based Automatic Rating for Cardinal Symptoms of Parkinson Disease.

Neurology
OBJECTIVE: We developed and investigated the feasibility of a machine learning-based automated rating for the 2 cardinal symptoms of Parkinson disease (PD): resting tremor and bradykinesia.

Intelligent Neutrosophic Diagnostic System for Cardiotocography Data.

Computational intelligence and neuroscience
Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The ...

Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.

Sensors (Basel, Switzerland)
In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensit...