AIMC Topic: Support Vector Machine

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From Nontargeted to Targeted Analysis: Feature Selection in the Differentiation of Truffle Species ( spp.) Using H NMR Spectroscopy and Support Vector Machine.

Journal of agricultural and food chemistry
The price of different truffle types varies according to their culinary value, sometimes by more than a factor of 10. Nonprofessionals can hardly distinguish visually the species within the white or black truffles, making the possibility of food frau...

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data.

Scientific reports
Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need...

Mechanomyography signals pattern recognition in hand movements using swarm intelligence algorithm optimized support vector machine based on acceleration sensors.

Medical engineering & physics
On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human-machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements bas...

Maximum margin and global criterion based-recursive feature selection.

Neural networks : the official journal of the International Neural Network Society
In this research paper, we aim to investigate and address the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. We identify two main challenges associated with these methods. Firstly, the...

A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.

PloS one
Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms ...

Early pigment spot segmentation and classification from iris cellular image analysis with explainable deep learning and multiclass support vector machine.

Biochemistry and cell biology = Biochimie et biologie cellulaire
Globally, retinal disorders impact thousands of individuals. Early diagnosis and treatment of these anomalies might halt their development and prevent many people from developing preventable blindness. Iris spot segmentation is critical due to acquir...

Decoding fMRI data with support vector machines and deep neural networks.

Journal of neuroscience methods
BACKGROUND: Multivoxel pattern analysis (MVPA) examines fMRI activation patterns associated with different cognitive conditions. Support vector machines (SVMs) are the predominant method in MVPA. While SVM is intuitive and easy to apply, it is mainly...

Multi-Cat Monitoring System Based on Concept Drift Adaptive Machine Learning Architecture.

Sensors (Basel, Switzerland)
In multi-cat households, monitoring individual cats' various behaviors is essential for diagnosing their health and ensuring their well-being. This study focuses on the defecation and urination activities of cats, and introduces an adaptive cat ident...

Classification of oolong tea varieties based on computer vision and convolutional neural networks.

Journal of the science of food and agriculture
BACKGROUND: In the contemporary food industry, accurate and rapid differentiation of oolong tea varieties holds paramount importance for traceability and quality control. However, achieving this remains a formidable challenge. This study addresses th...

Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review.

The Annals of otology, rhinology, and laryngology
BACKGROUND: Machine Learning models have been applied in various healthcare fields, including Audiology, to predict disease outcomes. The prognosis of sudden sensorineural hearing loss is difficult to predict due to the variable course of the disease...