svm classification models Search Results


svm classification model  (Cognivue Inc)
90
Cognivue Inc svm classification model
Svm Classification Model, supplied by Cognivue Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/svm classification model/product/Cognivue Inc
Average 90 stars, based on 1 article reviews
svm classification model - by Bioz Stars, 2026-04
90/100 stars
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svm classification model based on features of total head and body movement and general facial expressiveness  (Movex Inc)
90
Movex Inc svm classification model based on features of total head and body movement and general facial expressiveness
Svm Classification Model Based On Features Of Total Head And Body Movement And General Facial Expressiveness, supplied by Movex Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/svm classification model based on features of total head and body movement and general facial expressiveness/product/Movex Inc
Average 90 stars, based on 1 article reviews
svm classification model based on features of total head and body movement and general facial expressiveness - by Bioz Stars, 2026-04
90/100 stars
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svm classification models  (OpenSim Ltd)
90
OpenSim Ltd svm classification models
Svm Classification Models, supplied by OpenSim Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/svm classification models/product/OpenSim Ltd
Average 90 stars, based on 1 article reviews
svm classification models - by Bioz Stars, 2026-04
90/100 stars
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svm model for classification  (Hamad Medical Corporation)
90
Hamad Medical Corporation svm model for classification
Summary of the performance of AI models for assessment of stroke via retinal imaging.
Svm Model For Classification, supplied by Hamad Medical Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/svm model for classification/product/Hamad Medical Corporation
Average 90 stars, based on 1 article reviews
svm model for classification - by Bioz Stars, 2026-04
90/100 stars
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svm based second-tier predictive models using one-versus-one classification approach  (Molecular Biosystems Inc)
90
Molecular Biosystems Inc svm based second-tier predictive models using one-versus-one classification approach
Summary of the performance of AI models for assessment of stroke via retinal imaging.
Svm Based Second Tier Predictive Models Using One Versus One Classification Approach, supplied by Molecular Biosystems Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/svm based second-tier predictive models using one-versus-one classification approach/product/Molecular Biosystems Inc
Average 90 stars, based on 1 article reviews
svm based second-tier predictive models using one-versus-one classification approach - by Bioz Stars, 2026-04
90/100 stars
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Image Search Results


Summary of the performance of AI models for assessment of stroke via retinal imaging.

Journal: Frontiers in Computational Neuroscience

Article Title: Artificial intelligence in stroke risk assessment and management via retinal imaging

doi: 10.3389/fncom.2025.1490603

Figure Lengend Snippet: Summary of the performance of AI models for assessment of stroke via retinal imaging.

Article Snippet: , 2022 , 56 (21/35/0) , OCT , SVM (Classification of pial collateral status) , Hamad General Hospital , 1. Retinal Vessel Multifractal Dimensions: Significantly higher in patients with poor pial collaterals compared to good pial collaterals. 2. SVM Model for classification: Accuracy = 0.743 Sensitivity = 0.743 Specificity = 0.707.

Techniques: Imaging, Extraction, Biomarker Discovery, Isolation