AIMC Topic: Support Vector Machine

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Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors.

The veterinary quarterly
This study introduces Raman imaging technique for diagnosing skin cancer in veterinary oncology patients (dogs and cats). Initially, Raman spectral bands (with specificity to certain molecular structures and functional groups) were identified in form...

Safe and accelerated screening framework for support tensor machines.

Neural networks : the official journal of the International Neural Network Society
Support Tensor Machines (STMs) constitute an effective supervised learning method for classifying high-dimensional tensor data. However, traditional iterative solving methods are often time-consuming. To effectively address the issue of lengthy train...

Accelerometer-derived classifiers for early detection of degenerative joint disease in cats.

Veterinary journal (London, England : 1997)
Decreased mobility is a clinical sign of degenerative joint disease (DJD) in cats, which is highly prevalent, with 61 % of cats aged six years or older showing radiographic evidence of DJD. Radiographs can reveal morphological changes and assess join...

Alterations in static and dynamic functional network connectivity in chronic low back pain: a resting-state network functional connectivity and machine learning study.

Neuroreport
Low back pain (LBP) is a prevalent pain condition whose persistence can lead to changes in the brain regions responsible for sensory, cognitive, attentional, and emotional processing. Previous neuroimaging studies have identified various structural a...

Voice biomarkers as prognostic indicators for Parkinson's disease using machine learning techniques.

Scientific reports
Many people suffer from Parkinson's disease globally, a complicated neurological condition caused by the deficiency of dopamine, an organic chemical responsible for regulating movement in individuals. Patients with Parkinson face muscle stiffness or ...

One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics.

Scientific reports
Machine learning (ML) models are increasingly being applied to diagnose and predict disease, but face technical challenges such as population drift, where the training and real-world deployed data distributions differ. This phenomenon can degrade mod...

The potential of evaluating shape drawing using machine learning for predicting high autistic traits.

PloS one
BACKGROUND: Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product...

Diagnostic performance of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for detecting osteoarthritis and rheumatoid arthritis from blood serum.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Osteoarthritis (OA) and rheumatoid arthritis (RA) are the two most common rheumatic diseases worldwide, causing pain and disability. Both conditions are highly heterogeneous, and their onset occurs insidiously with non-specific symptoms, so they are ...

Sub-diffuse Reflectance Spectroscopy Combined With Machine Learning Method for Oral Mucosal Disease Identification.

Lasers in surgery and medicine
OBJECTIVES: Oral squamous cell carcinoma (OSCC) is the sixth-highest incidence of malignant tumors worldwide. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Here, we prese...

CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM.

PloS one
Human emotions are not necessarily tends to produce right facial expressions as there is no well defined connection between them. Although, human emotions are spontaneous, their facial expressions depend a lot on their mental and psychological capaci...