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svm, knn  (MathWorks Inc)


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    MathWorks Inc svm, knn
    Summary of 3 radiomics studies (above) and 3 non-radiomic studies (below) included in the radiomics and predictive models section.
    Svm, Knn, 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/result/svm, knn/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    svm, knn - by Bioz Stars, 2026-06
    90/100 stars

    Images

    1) Product Images from "A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer"

    Article Title: A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer

    Journal: Physics and Imaging in Radiation Oncology

    doi: 10.1016/j.phro.2025.100731

    Summary of 3 radiomics studies (above) and 3 non-radiomic studies (below) included in the radiomics and predictive models section.
    Figure Legend Snippet: Summary of 3 radiomics studies (above) and 3 non-radiomic studies (below) included in the radiomics and predictive models section.

    Techniques Used: Biomarker Discovery, Imaging



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    Image Search Results


    Summary of 3 radiomics studies (above) and 3 non-radiomic studies (below) included in the radiomics and predictive models section.

    Journal: Physics and Imaging in Radiation Oncology

    Article Title: A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer

    doi: 10.1016/j.phro.2025.100731

    Figure Lengend Snippet: Summary of 3 radiomics studies (above) and 3 non-radiomic studies (below) included in the radiomics and predictive models section.

    Article Snippet: Iliadou V et al. , Cancers (2022) , Handcrafted (Pyradiomics) , SVM, KNN (MATLAB) , The need of ART , Clinical Target Volume (CTV) and Parotid Glands (PG) , 40 (n.d.) , Shape, first-order, GLCM, GLDM, GLRLM, GLSZM, NGTDM (104) , 24 (36.4 %).

    Techniques: Biomarker Discovery, Imaging

    Selected research articles.

    Journal: PeerJ Computer Science

    Article Title: A systematic review of literature on credit card cyber fraud detection using machine and deep learning

    doi: 10.7717/peerj-cs.1278

    Figure Lengend Snippet: Selected research articles.

    Article Snippet: A157 , SMOTE MLP, KNN, SVM OSE, NN, GAN , Accuracy F1-score , The results point out that the model using stacking classifier which combines GAN-improved MLP with SVM and KNN. OSE is preferred because of its ability to harness the abilities of MLP which works better in finding hidden patterns. The accuracy of OSE is 99.8% , Real dataset/Europeans cardholders/Kaggle , Apply weighted voting and boosting algorithms.

    Techniques: Selection, Comparison, Isolation, Plasmid Preparation, Sampling, Modification

    Usage frequency of ML and DL techniques in credit card fraud.

    Journal: PeerJ Computer Science

    Article Title: A systematic review of literature on credit card cyber fraud detection using machine and deep learning

    doi: 10.7717/peerj-cs.1278

    Figure Lengend Snippet: Usage frequency of ML and DL techniques in credit card fraud.

    Article Snippet: A157 , SMOTE MLP, KNN, SVM OSE, NN, GAN , Accuracy F1-score , The results point out that the model using stacking classifier which combines GAN-improved MLP with SVM and KNN. OSE is preferred because of its ability to harness the abilities of MLP which works better in finding hidden patterns. The accuracy of OSE is 99.8% , Real dataset/Europeans cardholders/Kaggle , Apply weighted voting and boosting algorithms.

    Techniques: Plasmid Preparation, Isolation, Sampling

    Comparisons of selected article on cyber fraud detection in credit card.

    Journal: PeerJ Computer Science

    Article Title: A systematic review of literature on credit card cyber fraud detection using machine and deep learning

    doi: 10.7717/peerj-cs.1278

    Figure Lengend Snippet: Comparisons of selected article on cyber fraud detection in credit card.

    Article Snippet: A157 , SMOTE MLP, KNN, SVM OSE, NN, GAN , Accuracy F1-score , The results point out that the model using stacking classifier which combines GAN-improved MLP with SVM and KNN. OSE is preferred because of its ability to harness the abilities of MLP which works better in finding hidden patterns. The accuracy of OSE is 99.8% , Real dataset/Europeans cardholders/Kaggle , Apply weighted voting and boosting algorithms.

    Techniques: Sampling, Isolation, Plasmid Preparation, Injection, Preserving, Selection, Produced, Construct, Comparison