AIMC Topic: Support Vector Machine

Clear Filters Showing 111 to 120 of 4975 articles

Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.

PloS one
Alzheimer's disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various typ...

A novel approach to porcine abnormal sounds recognition based on improved Multi-SVDD.

PloS one
During the real-time recognition of porcine abnormal sounds, the accuracy and stability of the recognition method are crucial to guarantee a good performance. For this purpose, an improved Multiple-Support Vector Data Description (Multi-SVDD) is prop...

Machine learning comparison for biomarker level estimation in wastewater dynamics monitoring.

Scientific reports
Wastewater surveillance is an emerging strategy that enables monitoring of the presence and dynamic changes of targeted substances, facilitating improved allocation of preventive actions and public health interventions. This paper investigates the ap...

A predictive model developed to classify Leishmania promastigotes at two distinct life stages using MALDI-TOF mass spectrometry.

Archives of microbiology
Investigating the molecular differences between procyclic (non-infective) and metacyclic (infective) promastigotes is essential for understanding the Leishmania life cycle in the sandfly vector and may aid in identifying molecular markers specific to...

From CBC to clarity: Interpretable detection of beta-thalassemia carriers in imbalanced datasets.

PloS one
Thalassemia is an inherited blood disorder and is among the five most prevalent birth-related complications, especially in Southeast Asia. Thalassemia is classified into two main types-alpha-thalassemia and beta-thalassemia-based on the reduced or ab...

Metabolomic profiling and machine learning-based biomarker identification for oligoasthenozoospermia.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION AND OBJECTIVES: Oligoasthenozoospermia, characterized by a low sperm count and impaired progressive motility, significantly contributes to male infertility. This study examines the metabolic disparities between individuals with oligoasth...

Comparative evaluation of deep learning and traditional models for predicting traffic accident severity in Saudi Arabia.

Scientific reports
Road traffic accidents are one of the leading death causes around the globe, claiming millions of lives every year. Predicting traffic accident severity is essential for road users' safety and accident prevention. Artificial neural network (ANN), Boo...

Label-Free SERS Platform Assisted by Machine Learning for Multi-Target Detection and Physiological State Classification in Sweat.

Analytical chemistry
The detection of sweat metabolites is crucial for health monitoring, disease screening, and personalized medicine. Traditional methods encounter challenges like low metabolite concentrations, complex biological matrices, and difficulty in achieving m...

Tile-Based Fisher Ratio Analysis with Support Vector Machine Regression Modeling of GC×GC-TOFMS Data of VOCs Produced by Grown at Variable pHs.

Analytical chemistry
yeasts are commensal microorganisms found in human and animal skin. Species of have been connected to skin and opportunistic infections, where certain microenvironmental conditions are required in the host for the pathogenic processes to occur. We ...

Comparative machine learning strategies for improving antioxidant properties and aroma quality in fermented mung bean milkby Lactobacillus plantarum PC4.

International journal of food microbiology
This study compares least squares support vector machine (LSSVM) and artificial neural network (ANN) models, integrated with the NSGA-II algorithm, to optimize the fermentation of mung bean milk by Lactobacillus plantarum PC4. Given its superior pred...