Simplified multilayer graph convolutional networks with dropout

Yang, Fei; Zhang, Huyin; Tao, Shiming

doi:10.1007/s10489-021-02617-7

Cited by 17 publications

(6 citation statements)

References 31 publications

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“…Yang et al. ( 2021b ) employed a simplified multilayer GCN where redundant computation was handled with the removal of nonlinearities and merging weight matrices between graph conventional layers. The method matched the running speed of simple graph convolution (SGC) and outperformed GCN and SGC in five downstream tasks.…”

Section: Results Analysis Per Datasetsmentioning

confidence: 99%

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A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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Section: Results Analysis Per Datasetsmentioning

confidence: 99%

“…Yang et al. ( 2021b ) explored multilayer GCNs to handle complexity and redundant calculations, and the overfitting problem of GCNs. A simplified multilayer GCN with dropout which extends shallow GCNs was applied in scientific texts.…”

Section: Semi-supervised Learning For Text Classificationmentioning

confidence: 99%

A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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“…During each step of model training, this layer randomly sets input units to zero value with a set frequency. Those inputs not set to zero are scaled up by 1/(1-rate), keeping the total sum of all inputs constant [14]. In our CNN architecture, we have used two dropout layers with dropout rates of 25%.…”

Section: Dropout Layermentioning

confidence: 99%

CNN-based Clinical Diagnosis and Decision Support System for Chest X-ray

Magar,

Shrestha,

Rahman

2024

IOP Conf. Ser.: Mater. Sci. Eng.

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Chest radiographs are popularly used as a first-line diagnostic tool for various chest diseases. However, the correct interpretation of information while reading a radiograph is a major challenge. We aim to develop a suitable convolutional neural network (CNN) framework for the automatic detection of disease in chest radiographs. We have designed a 15-layered CNN architecture with 3 output classes: covid, normal, and pneumonia. This architecture was trained with 10,011 images. To avoid the risk of over-fitting, data augmentation techniques were implemented on training samples while dropout layers were implemented in CNN architecture. During training, we were able to obtain 98.24% accuracy on the training dataset. On the other hand, the experimental results of the test dataset indicate that the classification accuracy is 95.8%, precision is 95.83%, recall is 95.77% and the f1-score is 95.79% on average which is comparatively superior to several existing research works.

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“…With the dropout layer, it is aimed that some neurons have less information about each other. Thanks to the dropout layer, neurons are less affected by each other's weight changes [19].…”

Section: Cnn Architectures Layers and Lstm Networkmentioning

confidence: 99%

Detection of COVID-19 Fake News in Online Social Networks with the Developed CNN-LSTM Based Hybrid Model

Yıldırım¹

2022

RCES

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Technological developments have led to the emergence of different platforms. Social media platforms are one of the most used platforms recently. In this study, a text-based study was conducted on fake news sharing about COVID-19 in online social networks with Shallow Learning (SL) and Deep Learning (DL) methods. In order to classify the news in the dataset, the news in the dataset is converted into a format that can be understood by the machines in the preprocessing step. In the study, the glove method was used for word representation. The document matrix obtained using the glove method was classified with the proposed hybrid model. In the proposed hybrid model, LSTM and CNN structures are used together. In addition, different Shallow Learning methods accepted in the literature were used to compare the performances of the proposed model, and the results were obtained and these results were compared with the proposed model. Among these models, the most successful results were obtained in the proposed hybrid model. When the performance evaluation metrics obtained are examined, it is obvious that the proposed model can be used to solve many other social media and network problems related to COVID-19.

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Simplified multilayer graph convolutional networks with dropout

Cited by 17 publications

References 31 publications

A review of semi-supervised learning for text classification

A review of semi-supervised learning for text classification

CNN-based Clinical Diagnosis and Decision Support System for Chest X-ray

Detection of COVID-19 Fake News in Online Social Networks with the Developed CNN-LSTM Based Hybrid Model

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