AIMC Topic: Support Vector Machine

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Advancing breast cancer prediction using blockchain-secured hybrid genetic algorithm.

Computers in biology and medicine
Feature selection using evolutionary algorithms-a well-liked technique for choosing pertinent characteristics in huge datasets is explored. In machine learning, feature selection (FS) is a key phase that helps to boost model efficiency, decrease over...

Optimizing machine learning methods for groundwater quality prediction: Case study in District Bagh, Azad Kashmir, Pakistan.

Ecotoxicology and environmental safety
Groundwater quality monitoring is crucial for protecting the environment and human health. Machine learning (ML) offers substantial potential for enhancing groundwater quality prediction, classification, and identification of pollution indicators. Th...

Two-stage ensemble learning framework for automated classification of keratoconus severity.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Accurate staging of keratoconus (KC) is crucial for timely intervention and improving patient quality of life. Unlike prior studies that relied on traditional base machine learning (ML) models, this paper proposes a more adv...

Methodology for contamination detection and reduction in fermentation processes using machine learning.

Bioprocess and biosystems engineering
This paper demonstrates an accurate and efficient methodology for fermentation contamination detection and reduction using two machine learning (ML) methods, including one-class support vector machine and autoencoders. We also optimize as many hyperp...

Development of an anomaly detection system for Gibbs artifact identification in amyloid PET imaging.

Radiological physics and technology
The PET Imaging Site Qualification Program for amyloid positron emission tomography (PET) in Japan includes visual evaluation of the cylinder phantom. This visual evaluation requires observation of the entire image of the phantom and confirmation of ...

Combining structural equation modeling analysis with machine learning for early malignancy detection in Bethesda Category III thyroid nodules.

Artificial intelligence in medicine
Atypia of Undetermined Significance (AUS), classified as Category III in the Bethesda Thyroid Cytopathology Reporting System, presents significant diagnostic challenges for clinicians. This study aims to develop a clinical decision support system tha...

Interpretable machine learning models based on body composition and inflammatory nutritional index (BCINI) to predict early postoperative recurrence of colorectal cancer: Multi-center study.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) ranks among the most prevalent cancers worldwide, with early postoperative recurrence remaining a major cause of mortality. Body composition and inflammatory-nutritional indices (BCINI) have demonstra...

Classification of epilepsy seizure types in pediatrics based on Turkish EEG reports.

Epilepsy research
This study focuses on the binary classification of pediatric epilepsy seizure types as focal or generalized using Turkish electroencephalography (EEG) reports, leveraging natural language processing (NLP) and machine learning methodologies. A novel d...

BindUP-Alpha: A Webserver for Predicting DNA-and RNA-binding Proteins based on Experimental and Computational Structural Models☆.

Journal of molecular biology
Structural data provides important information on the proteins' function. Recent development of advanced machine learning and artificial intelligence tools, such as AlphaFold, have led to an explosion of predicted protein structures. However, many of...

Estimation of postmortem interval under different ambient temperatures based on multi-organ metabolomics and machine learning algorithm.

International journal of legal medicine
In forensic practice, the estimation of postmortem interval has been a persistent challenge. Recently, there has been an increasing utilization of metabolomics techniques combined with machine learning methods for postmortem interval estimation. When...