scholarly journals Byte-Pair and N-Gram Convolutional Methods of Analysing Automatically Disseminated Content on Social Platforms

2020 ◽  
Author(s):  
Houjun Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Processing ◽  
User Data ◽  
N Gram ◽  
Linguistic Content

In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively — meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data — aiding the security and integrity of social platforms.

scholarly journals Prediction of MoRFs Based on n-gram Convolutional Neural Network

10.29007/5k4z ◽  
2019 ◽  
Author(s):  
Fang Chun ◽  
Yoshitaka Moriwaki ◽  
Caihong Li ◽  
Kentaro Shimizu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Intrinsically Disordered Proteins ◽  
Interaction Networks ◽  
Disordered Proteins ◽  
Single Model ◽  
One Dimensional ◽  
Intrinsically Disordered ◽  
Machine Learning Model ◽  
N Gram

MoRFs usually play as "hub" site in interaction networks of intrinsically disordered proteins. With more and more serious diseases being found to be associated with disordered proteins, identifying MoRFs has become increasingly important. In this study, we introduce a multichannel convolutional neural network (CNN) model for MoRFs prediction. This model is generated by expanding the standard one-dimensional CNN model using multiple parallel CNNs that read the sequence with different n-gram sizes (groups of residues). In addition, we add an averaging step to refine the output result of machine learning model. When compared with other methods on the same dataset, our approach achieved a balanced accuracy of 0.682 and an AUC of 0.723, which is the best performance among the single model-based approaches.

Adaptive convolutional neural network using N-gram for spatial object recognition

2019 ◽  
Vol 12 (4) ◽  
pp. 525-540 ◽  
Author(s):  
J. Joshua Bapu ◽  
D. Jemi Florinabel ◽  
Y. Harold Robinson ◽  
E. Golden Julie ◽  
Raghvendra Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Convolutional Neural Network ◽  
Spatial Object ◽  
N Gram ◽  
Adaptive Convolutional Neural Network

scholarly journals A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2964 ◽  
Author(s):  
Ashima Kukkar ◽  
Rajni Mohana ◽  
Anand Nayyar ◽  
Jeamin Kim ◽  
Byeong-Gwon Kang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Tracking System ◽  
Bug Reports ◽  
Severity Classification ◽  
Bug Report ◽  
N Gram

The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.

Evaluation of Impact of Neural Networks in Text Classification

2021 ◽  
Vol 23 (07) ◽  
pp. 1279-1292
Author(s):  
Meghana S ◽  
◽  
Jagadeesh Sai D ◽  
Dr. Krishna Raj P. M ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Confusion Matrix ◽  
British Broadcasting Corporation ◽  
Text Data ◽  
N Gram ◽  
The Impact

One of the most trending and major areas of research in Natural Language Processing (NLP) is the classification of text data. This necessarily means that the category that the text belongs to is determined by the content of the text. Various algorithms such as Recurrent Neural Network along with its variation which is Long Short-Term Memory, Hierarchical Attention Networks and also Convolutional Neural Network have been used to analyse how the context of the text can be determined from the text data which in available in terms of datasets. These algorithms each have a special characteristic of their own. While Recurrent Neural Network maintains the structural sequence of the contexts, the Convolutional Neural Network manages to obtain the n-gram feature and the Hierarchical Attention Network manages the hierarchy of the documents or data. The above said algorithms have been implemented on the British Broadcasting Corporation News datasets. Various parameters such as recall, precision, accuracy etc. have been considered along with standards such as F1-score, confusion matrix etc. to deduce the impact.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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