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cnn + softmax  (SoftMax Inc)

 
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    SoftMax Inc cnn + softmax
    Cnn + 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/cnn+++softmax/cnn+softmax/pmc12060224-33-8-10
    Average 90 stars, based on 1 article reviews
    cnn + softmax - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Comparison:

    Article Title: Derived Amharic alphabet sign language recognition using machine learning methods
    Article Snippet: CNN (Softmax) was utilized as a classifier for normalized hybrid features in addition to SVM.

    Article Title: Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination.
    Article Snippet: Ref. Year FeatureExtractor Classifier Dataset Type Size Accuracy [17] 2021 CNN Softmax AHCD Proposed dataset Hijja MNIST Characters Characters Characters Digits 16,800 38,100 47,434 70,000 99% 95.4% 90% 99% [18] 2021 CNN Softmax SVM XGBoost Hijja Characters 47,434 89% 96.3% 95.7% [19] 2022 CNN Softmax AHCDHijja Characters Characters 16,800 47,434 98.48% 91.24% [20] 2022 CNN Softmax Hijja Characters 47,434 95%



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    Types of models used and their specifications.

    Journal: Frontiers in Big Data

    Article Title: Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis

    doi: 10.3389/fdata.2024.1402926

    Figure Lengend Snippet: Types of models used and their specifications.

    Article Snippet: Sakthiraj ( ) , Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) algorithm , HCNN-IASO , No , Softmax-CNN layer classifier (based on ResNet-34 and DenseNet-121) , IV , -Before augmentation: Healthy: 190 CML: 58 CLL: 30 AML: 56 ALL: 182 -After augmentation: Healthy: 1,291 CML: 1,244 CLL: 845 AML: 1,198 ALL: 1,082 , The proposed approach is used to generate results and to accurately identify and detect them. The data augmentation technique involved is utilized to practice big datasets and thus it processes large Leukemia images. The features from Leukemia datasets are extracted by using our proposed HCNN and further the attention layer in the HCNN is exploited to fuse the extracted features. The softmax layer of HCNN acts as a classifier and therefore it classifies the leukemia dataset into several subtypes. Furthermore, the accuracy of classification is optimized by utilizing Interactive autodidactic school optimization techniques. Finally, the optimized outcomes are sent to the medical institution/hospital via an IoMT platform for further processing. Based on the results retrieved, the physician/doctor provides a diagnosis to the patients..

    Techniques: Biomarker Discovery, Derivative Assay, Microscopy, Staining, Diagnostic Assay, Control, Generated