AIMC Topic: Support Vector Machine

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Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review.

Journal of chromatography. A
In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional ...

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning.

Scientific reports
White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medic...

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.

PloS one
Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains ...

Characterisation of Cognitive Load Using Machine Learning Classifiers of Electroencephalogram Data.

Sensors (Basel, Switzerland)
A high cognitive load can overload a person, potentially resulting in catastrophic accidents. It is therefore important to ensure the level of cognitive load associated with safety-critical tasks (such as driving a vehicle) remains manageable for dri...

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia.

Environmental monitoring and assessment
Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines...

Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

PloS one
Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endosc...

Classification of patients with chronic disease by activation level using machine learning methods.

Health care management science
Patient Activation Measure (PAM) measures the activation level of patients with chronic conditions and correlates well with patient adherence behavior, health outcomes, and healthcare costs. PAM is increasingly used in practice to identify patients n...

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

International journal of molecular sciences
Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adj...

A quality detection method of corn based on spectral technology and deep learning model.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Corn is an important food crop in the world. With economic development and population growth, the nutritional quality of corn is of great significance to high-quality breeding, scientific cultivation and fine management. Aiming at the problems of cum...

Discrimination of human and animal bloodstains using hyperspectral imaging.

Forensic science, medicine, and pathology
Blood is the most encountered type of biological evidence in violent crimes and contains pertinent information to a forensic investigation. The false presumption that blood encountered at a crime scene is human may not be realised until after costly ...