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Support Vector Machine

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Clustering and classification for dry bean feature imbalanced data.

Scientific reports
The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that c...

Fluorescence excitation-emission matrix spectroscopy combined with machine learning for the classification of viruses for respiratory infections.

Talanta
Significant efforts were currently being made worldwide to develop a tool capable of distinguishing between various harmful viruses through simple analysis. In this study, we utilized fluorescence excitation-emission matrix (EEM) spectroscopy as a ra...

Determination and visualization of moisture content in Camellia oleifera seeds rapidly based on hyperspectral imaging combined with deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Moisture content (MC) is crucial for the storage, transportation, and processing of Camellia oleifera seeds. The purpose of this study was to investigate the feasibility for detecting MC in Camellia oleifera seeds using visible near-infrared hyperspe...

Identification of Phosphodiesterase type 5 inhibitors (PDE5is) analogues using surface-enhanced Raman scattering and machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Phosphodiesterase type 5 inhibitors (PDE5is), primarily used for the treatment of erectile dysfunction, have been severely misused in recent years, posing a threat to public health and safety. This study developed a method that combines Surface-enhan...

Machine learning-based prediction of duodenal stump leakage following laparoscopic gastrectomy for gastric cancer.

Surgery
BACKGROUND: Duodenal stump leakage is one of the most critical complications following gastrectomy surgery, with a high mortality rate. The present study aimed to establish a predictive model based on machine learning for forecasting the occurrence o...

Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment.

JCO clinical cancer informatics
PURPOSE: Toxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk of experiencing treatment-re...

Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.

Sensors (Basel, Switzerland)
Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to...

Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classificatio...

Do machine learning methods solve the main pitfall of linear regression in dental age estimation?

Forensic science international
INTRODUCTION: Age estimation is crucial in forensic and anthropological fields. Teeth, are valued for their resilience to environmental factors and their preservation over time, making them essential for age estimation when other skeletal remains det...