AIMC Topic: Support Vector Machine
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Machine learning based classification of excessive smartphone users via neuronal cue reactivity.
Psychiatry research. Neuroimaging
Oct 2, 2024
Excessive Smartphone Use (ESU) poses a significant challenge in contemporary society, yet its recognition as a distinct disorder remains ambiguous. This study aims to address this gap by leveraging functional magnetic resonance imaging (fMRI) data an...
Histopathology-driven prostate cancer identification: A VBIR approach with CLAHE and GLCM insights.
Computers in biology and medicine
Oct 2, 2024
Efficient extraction and analysis of histopathological images are crucial for accurate medical diagnoses, particularly for prostate cancer. This research enhances histopathological image reclamation by integrating Visual-Based Image Reclamation (VBIR...
Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging.
Diagnostic and interventional radiology (Ankara, Turkey)
Oct 1, 2024
PURPOSE: This study aims to demonstrate the performance of machine learning algorithms to distinguish clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa) in prostate bi-parametric magnetic resonance im...
Applying support vector machines to a diagnostic classification model for polytomous attributes in small-sample contexts.
The British journal of mathematical and statistical psychology
Oct 1, 2024
Over several years, the evaluation of polytomous attributes in small-sample settings has posed a challenge to the application of cognitive diagnosis models. To enhance classification precision, the support vector machine (SVM) was introduced for esti...
Multi-instance learning for identifying high-risk subregions associated with synchronous distant metastasis in clear cell renal cell carcinoma.
Medical physics
Oct 1, 2024
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal tumors.
A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.
PloS one
Oct 1, 2024
INTRODUCTION: Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a significant challenge in disease management due to its poor prognosis. Predicting refractory status could greatly inform treatment strategies, enabling ...
Detection of Low Resilience Using Data-Driven Effective Connectivity Measures.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 1, 2024
Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing human brain networks under different mental disorders, such as mental stress. However, these approa...
Automated analysis of a novel object recognition test in mice using image processing and machine learning.
Behavioural brain research
Sep 30, 2024
The novel object recognition test (NORT) is one of the most commonly employed behavioral tests in experimental animals designed to evaluate an animal's interest in and recognition of novelty. However, manual procedures, which rely on researchers' obs...
Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.
Neural networks : the official journal of the International Neural Network Society
Sep 30, 2024
This article considers the problem of classifying individuals in a dataset of diverse psychosis spectrum conditions, including persons with subsyndromal psychotic-like experiences (PLEs) and healthy controls. This task is more challenging than the tr...
Prediction of Crohn's disease based on deep feature recognition.
Computational biology and chemistry
Sep 30, 2024
BACKGROUND: Crohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic inf...
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