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svm classification algorithm based on ga  (MathWorks Inc)


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    MathWorks Inc svm classification algorithm based on ga
    Svm Classification Algorithm Based On Ga, 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-based+algorithms/10__1080_slash_22797254__2020__1747949-133-9-14
    Average 90 stars, based on 1 article reviews
    svm classification algorithm based on ga - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol.
    Article Snippet: The SVM-RFE algorithm is available online from Mathworks File Exchange [75].

    Article Title: Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine
    Article Snippet: The model is built and trained using the MATLAB software and the main part is based on the SVM algorithm using the LIBSVM toolbox , .

    Article Title: Computational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides.
    Article Snippet: The world is entering the third decade of the acquired immunodeficiency syndrome (AIDS) pandemic.. The primary cause of the disease has known to be human immunodeficiency virus type I (HIV-1).. Recently, peptides are shown to have high potency as drugs in the treatment of AIDS.

    Article Title: Cortical neurons multiplex reward-related signals along with sensory and motor information
    Article Snippet: /article/body/sec/ To investigate the strength of information about the time of reward provided by the entire population, a population-decoding analysis was performed based on an SVM algorithm using the MATLAB statistics toolbox with 10-fold cross-validation.

    Article Title: Promise of a Low Power Mobile CPU based Embedded System in Artificial Leg Control
    Article Snippet: Existing PC implementations of our SVM-based NMI algorithms, however, are mainly based on MATLAB giving rise to high overheads and poor real-time performance.

    Article Title: Accurate lateralization and classification of MRI-negative 18F-FDG-PET-positive temporal lobe epilepsy using double inversion recovery and machine-learning.
    Article Snippet: Objective: The main objective of this study was to determine the ability of double inversion recovery (DIR) data coupled with machine-learning algorithms to distinguish normal individuals from epileptic subjects and to identify the laterality of the focus side in MRI-negative, PET-positive temporal lobe epilepsy (TLE) patients.. Materials and methods: We used whole-brain DIR data as the input features with which to train a linear supportvector machine model in 63 participants who underwent high-resolution structural MRI and DIR scans.. The subjects included 20 left TLE patients, 19 right TLE patients, and 24 healthy controls (HCs).

    Produced:

    Article Title: PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons
    Article Snippet: .. The model produced by the SVM is trained offline in MATLAB, based on which, the online SVM algorithm can predict the locomotion mode according to the signals measured by sensors in real time. ..



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    Image Search Results


    Summary of related studies for AI application in periodontology.

    Journal: Healthcare

    Article Title: Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives

    doi: 10.3390/healthcare10112188

    Figure Lengend Snippet: Summary of related studies for AI application in periodontology.

    Article Snippet: Disease detection , Abdalla-Aslan et al., 2020 [ ] , Cubic SVM based algorithm , Panoramic radiographs , 83 images , Dental restorations , Experienced practitioners , Five-fold , For detection Sensitivity: 0.94, For classification Sensitivity: 0.98 , The model has the potential to detect and classify dental restorations to promote patient’s health.

    Techniques: Biomarker Discovery, Diagnostic Assay

    Summary of related studies for AI application in orthodontics.

    Journal: Healthcare

    Article Title: Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives

    doi: 10.3390/healthcare10112188

    Figure Lengend Snippet: Summary of related studies for AI application in orthodontics.

    Article Snippet: Disease detection , Abdalla-Aslan et al., 2020 [ ] , Cubic SVM based algorithm , Panoramic radiographs , 83 images , Dental restorations , Experienced practitioners , Five-fold , For detection Sensitivity: 0.94, For classification Sensitivity: 0.98 , The model has the potential to detect and classify dental restorations to promote patient’s health.

    Techniques: Biomarker Discovery, Plasmid Preparation, Extraction, Modification

    Summary of related studies for AI application in prosthetic/restorative dentistry.

    Journal: Healthcare

    Article Title: Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives

    doi: 10.3390/healthcare10112188

    Figure Lengend Snippet: Summary of related studies for AI application in prosthetic/restorative dentistry.

    Article Snippet: Disease detection , Abdalla-Aslan et al., 2020 [ ] , Cubic SVM based algorithm , Panoramic radiographs , 83 images , Dental restorations , Experienced practitioners , Five-fold , For detection Sensitivity: 0.94, For classification Sensitivity: 0.98 , The model has the potential to detect and classify dental restorations to promote patient’s health.

    Techniques: Biomarker Discovery, Extraction

    Difference in AUC between the ML  algorithm SVM  and median SWV was statistically significant for both groups.

    Journal: Ultrasound in medicine & biology

    Article Title: A New Multi-Model Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors

    doi: 10.1016/j.ultrasmedbio.2019.09.004

    Figure Lengend Snippet: Difference in AUC between the ML algorithm SVM and median SWV was statistically significant for both groups.

    Article Snippet: The ML-based algorithm SVM had excellent diagnostic performance in datasets acquired from Siemens and Philips systems; this result is despite the fact that these two vendors used different elastography techniques.

    Techniques: