Review



svm classifier  (Keerthana Industries)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    Keerthana Industries svm classifier
    Svm Classifier, supplied by Keerthana Industries, 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/product/svm+classifier/hybrid+cnn+models/pm40578097-206-1-27
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-09
    90/100 stars

    Images

    Related Articles

    other:

    Article Title: MobileYOLO-Cyano: An enhanced deep learning approach for precise classification of cyanobacterial genera in water quality monitoring.
    Article Snippet: Cyanobacteria pose a critical challenge for freshwater management due to their ability to rapidly proliferate and produce toxins that can jeopardize human health, even at low concentrations.. Therefore, effective methods to accurately classify cyanobacterial genera are essential for water quality assessment.. However, existing automated classification methods often suffer from low accuracy or limitations in identifying cyanobacterial genera.

    Article Title: DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images
    Article Snippet: Keerthana et al. [ ] introduced two innovative CNN hybrid approaches using an SVM classifier at the final layer for categorizing dermoscopy images into benign or melanoma tumors.



    Similar Products

    svm classifier  (Cortical Dynamics)
    86
    Cortical Dynamics svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by Cortical Dynamics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm+classifier/classifier+svm/pmc13068037-49-13-4
    Average 86 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-09
    86/100 stars
    		  Buy from Supplier
    linear svm classifiers  (MathWorks Inc)
    96
    MathWorks Inc linear svm classifiers
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Linear Svm Classifiers, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm+classifier/Statistics+and+Machine+Learning+Toolbox/bio_rxiv__64898__2026__03__20__712287-168-13-21
    Average 96 stars, based on 1 article reviews
    linear svm classifiers - by Bioz Stars, 2026-09
    96/100 stars
    		  Buy from Supplier
    svm classifier  (Keerthana Industries)
    90
    Keerthana Industries svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by Keerthana Industries, 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/product/svm+classifier/hybrid+cnn+models/pm40578097-206-1-27
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    sr-svm classifier and all the analyses  (MathWorks Inc)
    90
    MathWorks Inc sr-svm classifier and all the analyses
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Sr Svm Classifier And All The Analyses, supplied by MathWorks 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/product/svm+classifier/us12333397-256-1-10
    Average 90 stars, based on 1 article reviews
    sr-svm classifier and all the analyses - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    svm classifier  (MathWorks Inc)
    90
    MathWorks Inc svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by MathWorks 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/product/svm+classifier/pm40474125-271-5-19
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    svm classifier  (CH Instruments)
    90
    CH Instruments svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by CH Instruments, 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/product/svm+classifier/svm+classifier/pmc12092891-88-22-25
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    hybridized densenet201 and mobilenet with svm classifier  (Keerthana Industries)
    90
    Keerthana Industries hybridized densenet201 and mobilenet with svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Hybridized Densenet201 And Mobilenet With Svm Classifier, supplied by Keerthana Industries, 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/product/svm+classifier/hybrid+cnn+models/pmc12092923-16-22-0
    Average 90 stars, based on 1 article reviews
    hybridized densenet201 and mobilenet with svm classifier - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    linear svm classifier  (MathWorks Inc)
    90
    MathWorks Inc linear svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Linear Svm Classifier, supplied by MathWorks 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/product/svm+classifier/pm40446048-326-6-12
    Average 90 stars, based on 1 article reviews
    linear svm classifier - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier
    svm classifier  (Kaggle Inc)
    90
    Kaggle Inc svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by Kaggle 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/product/svm+classifier/svm/pmc12127516-757-8-22
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

    Journal: CNS Neuroscience & Therapeutics

    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Biomarker Discovery, Functional Assay

    Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Journal: CNS Neuroscience & Therapeutics

    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Diagnostic Assay