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cnn–softmax model  (SoftMax Inc)

 
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    SoftMax Inc cnn–softmax model
    Cnn–Softmax Model, supplied by SoftMax 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/cnn-softmax+model/cnn+softmax/pmc11991339-112-26-26
    Average 90 stars, based on 1 article reviews
    cnn–softmax model - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: Therefore, CNN-Softmax model has a more accurate rating prediction of UBI auto insurance users, and the results are in line with the actual situation, which means a strong applicability and flexibility.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 6 CNN-Softmax model prediction accuracy when CNN activation function is sigmoid Data set Accuracy under Linear activation function Accuracy under rbf activation function 1 0.801 0.83 2 0.813 0.838 3 0.808 0.839 4 0.818 0.82 5 0.805 0.837 Data set Accuracy under Linear activation function Accuracy under rbf activation function 6 0.81 0.835 7 0.813 0.838 8 0.801 0.828 9 0.819 0.824 10 0.801 0.834 Average accuracy 0.809 0.832 Based on the above experiments, when CNN using reLU activation function and Softmax using the rbf activation function, it has the highest prediction accuracy rate of 86.4%.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: At the same time, it also verifies the rationality of CNN-Softmax model in processing the feature extraction of the raw data in insurance index system and the multi-classification of insurance samples.During rating the UBI auto insurance rate, CNN-Softmax algorithm has the following characteristics: the model accuracy rate is significantly improved compared with CNN-SVM algorithm, CNN algorithm and SVM algorithm; the model prediction efficiency is higher known from the number of iterations.

    Article Title: A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion
    Article Snippet: Firstly, the multichannel raw fault data of the temporal and spatial from multiple sensors is directly input into the improved CNN-Softmax model for the training of a CNN model by the back-propagation algorithm.

    Article Title: Deep learning based gasket fault detection: a CNN approach.
    Article Snippet: Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 5 CNN-Softmax model prediction accuracy when CNN activation function is reLU Data set Accuracy under Linear activation function Accuracy under rbf activation function 1 0.856 0.872 2 0.854 0.853 3 0.860 0.873 4 0.851 0.865 5 0.848 0.861 6 0.849 0.866 7 0.855 0.865 8 0.850 0.864 9 0.853 0.868 10 0.851 0.853 Average accuracy 0.853 0.864 When sigmoid activation function is uesd, the Softmax also respectively uses linear function and the reLU function.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 10 groups of data sets are input into the CNN-Softmax model using a circular algorithm.

    Construct:

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: Therefore, CNN-Softmax model has a more accurate rating prediction of UBI auto insurance users, and the results are in line with the actual situation, which means a strong applicability and flexibility.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 6 CNN-Softmax model prediction accuracy when CNN activation function is sigmoid Data set Accuracy under Linear activation function Accuracy under rbf activation function 1 0.801 0.83 2 0.813 0.838 3 0.808 0.839 4 0.818 0.82 5 0.805 0.837 Data set Accuracy under Linear activation function Accuracy under rbf activation function 6 0.81 0.835 7 0.813 0.838 8 0.801 0.828 9 0.819 0.824 10 0.801 0.834 Average accuracy 0.809 0.832 Based on the above experiments, when CNN using reLU activation function and Softmax using the rbf activation function, it has the highest prediction accuracy rate of 86.4%.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: At the same time, it also verifies the rationality of CNN-Softmax model in processing the feature extraction of the raw data in insurance index system and the multi-classification of insurance samples.During rating the UBI auto insurance rate, CNN-Softmax algorithm has the following characteristics: the model accuracy rate is significantly improved compared with CNN-SVM algorithm, CNN algorithm and SVM algorithm; the model prediction efficiency is higher known from the number of iterations.

    Article Title: A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion
    Article Snippet: Firstly, the multichannel raw fault data of the temporal and spatial from multiple sensors is directly input into the improved CNN-Softmax model for the training of a CNN model by the back-propagation algorithm.

    Article Title: Deep learning based gasket fault detection: a CNN approach.
    Article Snippet: Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 5 CNN-Softmax model prediction accuracy when CNN activation function is reLU Data set Accuracy under Linear activation function Accuracy under rbf activation function 1 0.856 0.872 2 0.854 0.853 3 0.860 0.873 4 0.851 0.865 5 0.848 0.861 6 0.849 0.866 7 0.855 0.865 8 0.850 0.864 9 0.853 0.868 10 0.851 0.853 Average accuracy 0.853 0.864 When sigmoid activation function is uesd, the Softmax also respectively uses linear function and the reLU function.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 10 groups of data sets are input into the CNN-Softmax model using a circular algorithm.

    Plasmid Preparation:

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: Therefore, CNN-Softmax model has a more accurate rating prediction of UBI auto insurance users, and the results are in line with the actual situation, which means a strong applicability and flexibility.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 6 CNN-Softmax model prediction accuracy when CNN activation function is sigmoid Data set Accuracy under Linear activation function Accuracy under rbf activation function 1 0.801 0.83 2 0.813 0.838 3 0.808 0.839 4 0.818 0.82 5 0.805 0.837 Data set Accuracy under Linear activation function Accuracy under rbf activation function 6 0.81 0.835 7 0.813 0.838 8 0.801 0.828 9 0.819 0.824 10 0.801 0.834 Average accuracy 0.809 0.832 Based on the above experiments, when CNN using reLU activation function and Softmax using the rbf activation function, it has the highest prediction accuracy rate of 86.4%.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: At the same time, it also verifies the rationality of CNN-Softmax model in processing the feature extraction of the raw data in insurance index system and the multi-classification of insurance samples.During rating the UBI auto insurance rate, CNN-Softmax algorithm has the following characteristics: the model accuracy rate is significantly improved compared with CNN-SVM algorithm, CNN algorithm and SVM algorithm; the model prediction efficiency is higher known from the number of iterations.

    Article Title: A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion
    Article Snippet: Firstly, the multichannel raw fault data of the temporal and spatial from multiple sensors is directly input into the improved CNN-Softmax model for the training of a CNN model by the back-propagation algorithm.

    Article Title: Deep learning based gasket fault detection: a CNN approach.
    Article Snippet: Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 5 CNN-Softmax model prediction accuracy when CNN activation function is reLU Data set Accuracy under Linear activation function Accuracy under rbf activation function 1 0.856 0.872 2 0.854 0.853 3 0.860 0.873 4 0.851 0.865 5 0.848 0.861 6 0.849 0.866 7 0.855 0.865 8 0.850 0.864 9 0.853 0.868 10 0.851 0.853 Average accuracy 0.853 0.864 When sigmoid activation function is uesd, the Softmax also respectively uses linear function and the reLU function.

    Article Title: Research on The Model of UBI Car Insurance Rates Rating Based on CNN-Softmax Algorithm
    Article Snippet: 10 groups of data sets are input into the CNN-Softmax model using a circular algorithm.



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    Image Search Results


    SVM and CNN (softmax) models comparison.

    Journal: Heliyon

    Article Title: Derived Amharic alphabet sign language recognition using machine learning methods

    doi: 10.1016/j.heliyon.2024.e38265

    Figure Lengend Snippet: SVM and CNN (softmax) models comparison.

    Article Snippet: As described in section , the accuracy results of the SVM model with HOG, CNN, Normalized and non-normalized features (HOG and CNN feature vectors) are 95.42 %, 89.02 %, 97.40 % and 93.61 % respectively, while a 93.55 % performance result was obtained using the CNN (softmax) model with normalized features.

    Techniques: Comparison