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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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Article Title: Hybridized grasshopper optimization and cuckoo search algorithm for the classification of malware
Article Snippet: The comparative table of proposed LSTMSoftsign with existing classifiers is represented in Table 8. performance while compared to existing classifiers such as ML-Droid [21], DBN-GRU [22], CNN-Softmax [23], and CFG-4-gram [25].

Article Title: Hybridized grasshopper optimization and cuckoo search algorithm for the classification of malware
Article Snippet: From the evaluation the proposed attained the high accuracy of 98.95% which is comparatively higher than other existing methods such as ML-Droid, DBN-GRU, CNN-Softmax, and CFG-4-gram.

Article Title: Recognition of Drill String Vibration State Based on WGAN-div and CNN-IWPSO-SVM
Article Snippet: During drilling operations, complex and variable dynamic nonlinear loads result in intricate vibrations in the drill string, severely impacting drilling safety and efficiency.. The vibration data collected on-site contains rich information about vibration conditions.. Addressing the limitations of existing signal processing methods in vibration state monitoring, this study proposes a hybrid method combining deep learning and machine learning for vibration state classification.

Article Title: Multimodal Biometric System Fusion Using Fingerprint and Iris with Convolutional Neural Network
Article Snippet: Neural networks and Hamming distance are used to compare and determine confirmation.This part explains the detail on presented fingerprint identification system applying CNN-Softmax.

Article Title: Detection and mitigation of DDoS attacks based on multi-dimensional characteristics in SDN
Article Snippet: The detection experiment results of MDDCC on the InSDN, CIC-IDS2017, and CIC-DDoS2019 datasets demonstrate that MDDCC not only has good detection ability on simulated traffic data but also shows excellent detection performance on open network data sets, which shows that the MDDCC model has strong generalization ability. (d) Performance comparison with other detection methods To objectively evaluate the performance of the MDDCC model, it was compared with other similar detection models, specifically including CNN-Softmax and CNN-LSTM , DNN-LSTM , GAN , and 1D-CNN & 2D-CNN , which are traditional classic detection models.

Article Title: Detection and mitigation of DDoS attacks based on multi-dimensional characteristics in SDN.
Article Snippet: MDDCC achieved an accuracy of 99.24% on the InSDN dataset, which is an improvement of 0.75% and 3.03% over the accuracies of the CNN-Softmax and CNN-LSTM detection methods, respectively.

Article Title: Collective Decision of One-vs-Rest Networks for Open-Set Recognition
Article Snippet: 1) CNN-Softmax: The first baseline model is a conventional softmax CNN.

Article Title: Detection and mitigation of DDoS attacks based on multi-dimensional characteristics in SDN.
Article Snippet: Additionally, the F1 score for MDDCC is 99.53%, showing an improvement of 1.28% and 2.17% when compared to CNN-Softmax and CNN-LSTM.



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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