AIMC Topic: Support Vector Machine

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Enhancing and improving the performance of imbalanced class data using novel GBO and SSG: A comparative analysis.

Neural networks : the official journal of the International Neural Network Society
Class imbalance problem (CIP) in a dataset is a major challenge that significantly affects the performance of Machine Learning (ML) models resulting in biased predictions. Numerous techniques have been proposed to address CIP, including, but not limi...

Protocol to train a support vector machine for the automatic curation of bacterial cell detections in microscopy images.

STAR protocols
Manual curation of bacterial cell detections in microscopy images remains a time-consuming and laborious task. This work offers a comprehensive, step-by-step tutorial on training a support vector machine to autonomously distinguish between good and b...

Cancer detection and classification using a simplified binary state vector machine.

Medical & biological engineering & computing
Cancer is an invasive and malignant growth of cells and is known to be one of the most fatal diseases. Its early detection is essential for decreasing the mortality rate and increasing the probability of survival. This study presents an efficient mac...

Diagnosis of multiple sclerosis using optical coherence tomography supported by explainable artificial intelligence.

Eye (London, England)
BACKGROUND/OBJECTIVES: Study of retinal structure based on optical coherence tomography (OCT) data can facilitate early diagnosis of relapsing-remitting multiple sclerosis (RRMS). Although artificial intelligence can provide highly reliable diagnoses...

A quantum-based oversampling method for classification of highly imbalanced and overlapped data.

Experimental biology and medicine (Maywood, N.J.)
Data imbalance is a challenging problem in classification tasks, and when combined with class overlapping, it further deteriorates classification performance. However, existing studies have rarely addressed both issues simultaneously. In this article...

Machine learning can predict anterior elevation after reverse total shoulder arthroplasty: A new tool for daily outpatient clinic?

Musculoskeletal surgery
The aim of the present study was to individuate and compare specific machine learning algorithms that could predict postoperative anterior elevation score after reverse shoulder arthroplasty surgery at different time points. Data from 105 patients wh...

Decoding Single and Paired Phonemes Using 7T Functional MRI.

Brain topography
Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speec...

Application of radiomics model based on lumbar computed tomography in diagnosis of elderly osteoporosis.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
A metabolic bone disease characterized by decreased bone formation and increased bone resorption is osteoporosis. It can cause pain and fracture of patients. The elderly are prone to osteoporosis and are more vulnerable to osteoporosis. In this study...

DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction.

Methods (San Diego, Calif.)
DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-bindi...

Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIM: The regular uptake of a high-fat diet (HFD) with changing lifestyle causes atherosclerosis leading to cardiovascular diseases and autonomic dysfunction. Therefore, the current study aimed to investigate the correlation of autonomi...