AIMC Topic: Support Vector Machine

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High-speed identification system for fresh tea leaves based on phenotypic characteristics utilizing an improved genetic algorithm.

Journal of the science of food and agriculture
BACKGROUND: High-quality tea requires leaves of similar size and tenderness. The grade of the fresh leaves determines the quality of the tea. The automated classification of fresh tea leaves improves resource utilization and reduces manual picking co...

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Computational intelligence and neuroscience
Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches ha...

Research on Multi-Sensor Fusion Indoor Fire Perception Algorithm Based on Improved TCN.

Sensors (Basel, Switzerland)
Indoor fires cause huge casualties and economic losses worldwide. Thus, it is critical to quickly and accurately perceive the fire. In this work, an indoor fire perception algorithm based on multi-sensor fusion was proposed. Firstly, the sensor data ...

Semisupervised Learning via Axiomatic Fuzzy Set Theory and SVM.

IEEE transactions on cybernetics
In this article, we present a semantic semisupervised learning (Semantic SSL) approach targeted at unifying two machine-learning paradigms in a mutually beneficial way, where the classical support vector machine (SVM) learns to reveal primitive logic...

Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes.

Scientific reports
Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule ex...

Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data.

PLoS neglected tropical diseases
BACKGROUND: Previous epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements ...

Intelligent and novel multi-type cancer prediction model using optimized ensemble learning.

Computer methods in biomechanics and biomedical engineering
Cancer is known to be highly severe disease and gets incurable even when the treatment has started at the time of diagnosis owing to the occurrence of cancer cells. Diverse machine learning approaches are implemented for predicting the cancer recurre...

Prediction of black carbon in marine engines and correlation analysis of model characteristics based on multiple machine learning algorithms.

Environmental science and pollution research international
Ship black carbon emissions have caused great harm to ecological environment. In order to estimate the black carbon emissions, thereby reducing the cost of black carbon experiments, here, we introduced four machine learning algorithms which are lasso...

A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data.

Sensors (Basel, Switzerland)
Parkinson's disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have b...

Identifying molecular functional groups of organic compounds by deep learning of NMR data.

Magnetic resonance in chemistry : MRC
We preprocess the raw nuclear magnetic resonance (NMR) spectrum and extract key features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition. We also provide a strategy t...