Graph Convolutional Network for Word Sense Disambiguation by Chun-Xiang Zhang, Rui Liu, Xue-Yao Gao, Bo Yu

“…GCN is used to fuse features of a node and its neighbors, and the softmax function is applied to determine the semantic category of the ambiguous word. …”
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Deep Learning-Based Dzongkha Handwritten Digit Classification by Yonten Jamtsho, Pema Yangden, Sonam Wangmo, Nima Dema

“…In the study, the 3 layer set of CONV → ReLU → POOL, followed by a fully connected layer, dropout layer, and softmax function were used to train the digit. In the dataset, each class (0-9) contains 1500 images which are split into train, validation, and test sets: 70:20:10. …”
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An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder by Fengtao Wang, Bosen Dun, Xiaofei Liu, Yuhang Xue, Hongkun Li, Qingkai Han

“…Subsequently, a deep neural network is constructed with one KAE and multiple AEs to extract inherent features layer by layer. Finally, softmax is adopted as the classifier to accurately identify different bearing faults, and error backpropagation algorithm is used to fine-tune the model parameters. …”
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Mass Laplacian Discriminant Analysis and Its Application in Gear Fault Diagnosis by Guangbin Wang, Ying Lv, Tengqiang Wang, Xiaohui Wang, Huanke Cheng

“…Finally, based on the mapping function, the eigenvalues of the training data and the test data are calculated, and the softmax algorithm is used to classify the test data. …”
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Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals by Hongmei Liu, Lianfeng Li, Jian Ma

“…After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. …”
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Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN by Lei Wang, Kun He, Haipeng Fu, Weixing Chen

“…A fully connected layer with a SoftMax activation function is then used to classify faults in the training set. …”
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Image Video Teaching Method in College Physical Education by Haibo Xu, Lun Wan

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Tempering Feydeau: Twisting and Guilty Pleasures on the London Stage (1893-1897) by Violaine Heyraud

“…London theatres surrendered to his infectious laughter, producing a number of his plays between 1892 and 1897, though admittedly, carefully selected: The Sportsman, adapted from Monsieur chasse! , The Other Man from Champignol malgré lui, His Little Dodge (Le Système Ribadier), A Night Out (L’Hôtel du Libre-Échange) and A Night Session (Séance de nuit). …”
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From fitness trackers to medical devices: wearable technologies by Zhi Zhou

“…A graphic user interface has been implemented to suggest a series of exercises to improve a sportsman/woman s condition, depending on the context and their profile. …”
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ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network by Jingming Xia, Dawei Xuan, Ling Tan, Luping Xing

“…Finally, the weather images are classified and recognized through the fully connected layer and Softmax classifier. In addition, we build a medium-scale dataset of weather images on traffic road, called “WeatherDataset-4,” which consists of 4 categories and contains 4983 weather images covering most of the severe weather. …”
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Intensive Cold-Air Invasion Detection and Classification with Deep Learning in Complicated Meteorological Systems by Ming Yang, Hao Ma, Bomin Chen, Guangtao Dong

“…Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine (SVM) is used for path classification to enhance the confidence of classification. …”
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OsteoNet—A Framework for Identifying Osteoporosis in Bone Radiograph Images Using Attention-Based VGG Network by Abdul Wahab Muzaffar, Farhan Riaz, Muhammad Tahir

“…Channel attention is applied by calculating the mean and variance across channels, followed by a softmax operation to highlight essential features in the channel. …”
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A lightweight power quality disturbance recognition model based on CNN and Transformer by ZHANG Bide, QIU Jie, LOU Guangxin, ZHOU Can, LUO Qingqing, LI Tianqian

“…Finally, pooling layers, fully connected layers, and Softmax are applied to complete the recognition PQDs. …”
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A Low-Power Streaming Speech Enhancement Accelerator for Edge Devices by Ci-Hao Wu, Tian-Sheuan Chang

“…Additionally, we employed softmax-free attention, complemented by an extra batch normalization, facilitating simpler hardware design. …”
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GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network by Mohammad Farukh Hashmi, B. Kiran Kumar Ashish, Prabhu Chaitanya, Avinash Keskar, Sinan Q. Salih, Neeraj Dhanraj Bokde

“…The end layer comprises of a Softmax classifier to classify the final prediction of the subject identity. …”
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A Novel Deep Sparse Filtering Method for Intelligent Fault Diagnosis by Acoustic Signal Processing by Guowei Zhang, Jinrui Wang, Baokun Han, Sixiang Jia, Xiaoyu Wang, Jingtao He

“…In the second stage, the learned features are obtained by training batch-normalized DSF with frequency-domain signals, and then the features are fine-tuned by backpropagation (BP) algorithm. In the third stage, softmax regression is used as a classifier for heath condition recognition based on the fine-tuned features. …”
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Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology by Haitao Chen

“…The convolutional layer, pooling layer, and fully connected layer in a convolutional neural network are responsible for feature extraction, reducing the number of parameters, integrating features into high-level features, and finally classifying them by SoftMax classifier in turn. However, the actual situation is intricate and often the result is not obtained as envisioned, and the use of migration learning can be a good way to improve the overfitting phenomenon. …”
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A Novel Model to Detect and Classify Fresh and Damaged Fruits to Reduce Food Waste Using a Deep Learning Technique by T. Bharath Kumar, Deepak Prashar, Gayatri Vaidya, Vipin Kumar, S. Deva Kumar, F. Sammy

“…For classifying photographs into fresh and decaying fruits, softmax is used, while CNN obtains fruit image properties. …”
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Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

“…Moreover, we propose an integrated end-to-end neural network learning framework based on one complete encoder-decoder architecture transformer model: Transfer Text-to-Text Transformer (T5), by learning the embedding vector representation space of conditional molecular properties to encode and guide the vector representation of SMILES sequences, resulting in the output of the final decoder block with a softmax output (maximum likelihood objective). Moreover, we evaluated the performance of these NLP-based generation models and another new model architecture based on a selective state space and selected the best approach jointing a transfer learning strategy for de novo drug discovery to target L858R/T790M/C797S-mutant EGFR in non-small cell lung cancer.…”
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Supporting Health and Social Care Students Stay and Stay Well: A Conceptual Framework for Implementing Integrated Care Into Higher Education by Louise Grant, Lisa Bostock, Caroline Reid, Nasreen Ali, Fiona Factor

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