AIMC Topic: Support Vector Machine

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A comparative study of machine learning classifiers for risk prediction of asthma disease.

Photodiagnosis and photodynamic therapy
Asthma is a chronic disease characterized by wheezing, chest tightening and difficulty in breathing due to inflammation of lung airways. Early risk prediction of asthma is crucial for proper and effective management. This study presents the use of ma...

Development of a Wearable Electrical Impedance Tomographic Sensor for Gesture Recognition With Machine Learning.

IEEE journal of biomedical and health informatics
A wearable electrical impedance tomographic (wEIT) sensor with 8 electrodes is developed to realize gesture recognition with machine learning algorithms. To optimize the wEIT sensor, gesture recognition rates are compared by using a series of electro...

Crash prediction based on traffic platoon characteristics using floating car trajectory data and the machine learning approach.

Accident; analysis and prevention
Predicting crash propensity helps study safety on urban expressways in order to implement countermeasures and make improvements. It also helps identify and prevent crashes before they happen. However, collecting real-time wide-coverage traffic inform...

Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic bleeding detection.

Computers in biology and medicine
Wireless capsule endoscopy (WCE) is a video technology to inspect abnormalities, like bleeding in the gastrointestinal tract. In order to avoid a complex and long duration manual review process, automatic bleeding detection schemes are developed that...

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
BACKGROUND AND AIM: It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer-aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection.

Extracting comprehensive clinical information for breast cancer using deep learning methods.

International journal of medical informatics
OBJECTIVE: Breast cancer is the most common malignant tumor among women. The diagnosis and treatment information of breast cancer patients is abundant in multiple types of clinical fields, including clinicopathological data, genotype and phenotype in...

Quantitative analysis of wheat maltose by combined terahertz spectroscopy and imaging based on Boosting ensemble learning.

Food chemistry
To improve the prediction accuracy of existing data modeling that is based on either spectral data or image data alone, we herein propose a method for the quantitative analysis of wheat maltose contents based on the fusion of terahertz spectroscopy a...

A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network.

Medical hypotheses
Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks to these images, some methods have so far been proposed in order to distinguish between benign and malignant brain tumors. Many systems attempting to define these tu...

Label Self-Advised Support Vector Machine (LSA-SVM)-Automated Classification of Foot Drop Rehabilitation Case Study.

Biosensors
Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus ...