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convnets softmax  (SoftMax Inc)

 
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    SoftMax Inc convnets softmax
    Convnets Softmax, 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/convnets+softmax/convnets+softmax/pm40648284-122-12-18
    Average 90 stars, based on 1 article reviews
    convnets softmax - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: A Comprehensive Methodological Survey of Human Activity Recognition Across Diverse Data Modalities.
    Article Snippet: Author Year DatasetName Modality Method Classifier Accuracy [%] Akilan et al. [52] 2017 CIFAR100 Caltech101 CIFAR10 RGB ConvNets Softmax 75.87 95.54 91.83 Shi et al. [53] 2017 KTH UCF101 HMDB51 RGB 3-streamCNN Softmax 96.8 94.33 92.2 Ahsan et al. [54] 2018 UCF101HMDB51 RGB GAN Softmax 47.2 41.40 Tu et al. [55] 2018 JHMDB HMDB51 UCF Sports UCF101 RGB Multi-StreamCNN Softmax 71.17 69.8 58.12 94.5 Zhou et al. [56] 2018 HMDB51UCF101 RGB TMiCT-Net CNN 70.5 94.7 Jian et al. [57] 2019 Sport video RGB FCN Softmax 97.40 Ullah et al. [44] 2019 UCF50 UCF101 YouTube action HMDB51 RGB Deepautoencoder SVM 96.4 94.33 96.21 70.33 Gowda et al. [58] 2020 UCF101 HMDB51 FCVID ActivityNet RGB SMART Softmax 98.6 84.3 82.1 84.4 Khan et al. [59] 2020 HMDB51 UCF Sports YouTube IXMAS KTH RGB VGG19 CNN Naive Bayes 93.7 98.0 94.4 99.4 95.2 97.0 Ullah et al. [60] 2021 HMDB51 UCF101 UCF50 Hollywood2 YouTube Actions RGB DS-GRU Softmax 72.3 95.5 95.2 71.3 97.17 Wang et al. [61] 2021 SomethingV1 SomethingV2 Kinetics-400 RGB Temporal Difference Networks TDN 84.1 91.6 94.4 Wang et al. [62] 2022 UCF101 RGB HyRSM - 93.0 Wensel et al. [63] 2023 YouTube Action HMDB51 UCF50 UCF101 RGB ViT-ReT Softmax 92.4 78.4 97.1 94.7 Hassan et al. [64] 2024 UCF11 UCF Sports JHMDB RGB DeepBi-LSTM Softmax 99.2 93.3 76.3 Table 1.



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    Summary and comparison of the selected recent research.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

    doi: 10.3390/s22082911

    Figure Lengend Snippet: Summary and comparison of the selected recent research.

    Article Snippet: Bäckström et al. (2018) [ ] , 3D ConvNet + Softmax , 96% , , .

    Techniques: Comparison, Selection, T-Test, Generated

    Comparison of our test performance with eight existing state-of-the-art methods.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning

    doi: 10.3390/s22082911

    Figure Lengend Snippet: Comparison of our test performance with eight existing state-of-the-art methods.

    Article Snippet: Bäckström et al. (2018) [ ] , 3D ConvNet + Softmax , 96% , , .

    Techniques: Comparison

    Performance for all categories using the proposed method (ConvNet & LRBSF). Best and worst performance of individual participant is also mentioned.

    Journal: PLoS ONE

    Article Title: Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

    doi: 10.1371/journal.pone.0178410

    Figure Lengend Snippet: Performance for all categories using the proposed method (ConvNet & LRBSF). Best and worst performance of individual participant is also mentioned.

    Article Snippet: Because ConvNet is a complete framework, most of the studies have used the ConvNet classifier (softmax) for prediction/classification [ ].

    Techniques: Selection

    Significant difference (p-value) of accuracies between the proposed and other methods.

    Journal: PLoS ONE

    Article Title: Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

    doi: 10.1371/journal.pone.0178410

    Figure Lengend Snippet: Significant difference (p-value) of accuracies between the proposed and other methods.

    Article Snippet: Because ConvNet is a complete framework, most of the studies have used the ConvNet classifier (softmax) for prediction/classification [ ].

    Techniques: