In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,th...In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,thoughts,stories,advertisements,and news,among many other content types.With the recent increase in online content,the importance of identifying fake and real news has increased.Although,there is a lot of work present to detect fake news,a study on Fuzzy CRNN was not explored into this direction.In this work,a system is designed to classify fake and real news using fuzzy logic.The initial feature extraction process is done using a convolutional recurrent neural network(CRNN).After the extraction of features,word indexing is done with high dimensionality.Then,based on the indexing measures,the ranking process identifies whether news is fake or real.The fuzzy CRNN model is trained to yield outstanding resultswith 99.99±0.01%accuracy.This work utilizes three different datasets(LIAR,LIAR-PLUS,and ISOT)to find the most accurate model.展开更多
A tremendous amount of vendor invoices is generated in the corporate sector.To automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is required.This paper proposes an e...A tremendous amount of vendor invoices is generated in the corporate sector.To automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is required.This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement.For text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an invoice.This chunk is later passed to the deep learning model,which performs text recognition.The deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the LSTM.The model integrates feature extraction,modeling sequence,and transcription into a unified network.It handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale normalization.Furthermore,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical applications.The overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed model.The model is thus generic and can be used for other similar recognition scenarios.展开更多
COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of coun...COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of countries or cities,quarantines,isolation,sanitization,and masks.Patients with symptoms of COVID-19 are tested using medical testing kits;these tests must be conducted by healthcare professionals.However,the testing process is expensive and time-consuming.There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken.This paper introduces a novel technique based on deep learning(DL)that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19.The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high;clinical tests should then be used to test and identify infected individuals.The system proposed here uses recurrent neural networks(RNN)and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus.The results demonstrate that RNN can conduct this analysis more accurately than other machine learning(ML)algorithms.展开更多
While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning me...While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.展开更多
现有文本分类模型对文本的全局信息和局部信息利用不足,导致文本分类性能较差。针对这一问题,提出一种将文本的全局和局部特征动态融合(global and local features dynamic fusion,GLFDF)的分类模型。所提模型首先设计动态融合增强模块...现有文本分类模型对文本的全局信息和局部信息利用不足,导致文本分类性能较差。针对这一问题,提出一种将文本的全局和局部特征动态融合(global and local features dynamic fusion,GLFDF)的分类模型。所提模型首先设计动态融合增强模块动态控制文本的全局时序特征与局部语义特征融入单词嵌入矩阵的每个特定位置;其次,将融合全局和局部特征的嵌入矩阵馈送到特征提取模块中进行特征提取;最后,在Ohsumed和THUCNews数据集上测试所提模型的效果。实验结果表明:GLFDF模型在2个数据集上的F1值分别达到63.24%和92.50%,优于其他文本分类模型,提高了文本分类的性能。由消融实验分析可知,动态融合增强模块可以充分融合文本的全局时序特征和局部语义特征,有效解决文本分类模型对全局信息和局部信息利用不足的问题。展开更多
针对中文文本分类准确率低、单一的卷积神经网络模型难以提取多方面特征的问题,本文提出一种基于CNN的并行门控机制的混合文本分类模型——CGGA(Convolutional Neural Network with parallel gating unit and attention mechanism).利...针对中文文本分类准确率低、单一的卷积神经网络模型难以提取多方面特征的问题,本文提出一种基于CNN的并行门控机制的混合文本分类模型——CGGA(Convolutional Neural Network with parallel gating unit and attention mechanism).利用卷积提取文本的局部特征,并加入双向门控循环单元对数据进行上下文数据建模,提取关系特征,同时,引入门控Tanh-ReLU单元进行进一步的特征筛选,从而控制信息向下层流动的力度,并且减轻梯度弥散,提高模型分类准确率.最后,使用多头注意力机制进行权重更新计算,以提高在相应文本类别上的输出,进而优化模型分类性能.实验结果显示,本文提出的文本分类模型和分类算法,在THUCNews数据集和搜狐数据集上,比基线模型的宏平均精确率分别提高了2.24%、6.78%.展开更多
文摘In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,thoughts,stories,advertisements,and news,among many other content types.With the recent increase in online content,the importance of identifying fake and real news has increased.Although,there is a lot of work present to detect fake news,a study on Fuzzy CRNN was not explored into this direction.In this work,a system is designed to classify fake and real news using fuzzy logic.The initial feature extraction process is done using a convolutional recurrent neural network(CRNN).After the extraction of features,word indexing is done with high dimensionality.Then,based on the indexing measures,the ranking process identifies whether news is fake or real.The fuzzy CRNN model is trained to yield outstanding resultswith 99.99±0.01%accuracy.This work utilizes three different datasets(LIAR,LIAR-PLUS,and ISOT)to find the most accurate model.
基金Researchers would like to thank the Deanship of Scientific Research,Qassim University,for funding publication of this project.
文摘A tremendous amount of vendor invoices is generated in the corporate sector.To automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is required.This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement.For text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an invoice.This chunk is later passed to the deep learning model,which performs text recognition.The deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the LSTM.The model integrates feature extraction,modeling sequence,and transcription into a unified network.It handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale normalization.Furthermore,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical applications.The overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed model.The model is thus generic and can be used for other similar recognition scenarios.
基金support from Taif university through Researchers Supporting Project number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘COVID-19 disease is spreading exponentially due to the rapid transmission of the virus between humans.Different countries have tried different solutions to control the spread of the disease,including lockdowns of countries or cities,quarantines,isolation,sanitization,and masks.Patients with symptoms of COVID-19 are tested using medical testing kits;these tests must be conducted by healthcare professionals.However,the testing process is expensive and time-consuming.There is no surveillance system that can be used as surveillance framework to identify regions of infected individuals and determine the rate of spread so that precautions can be taken.This paper introduces a novel technique based on deep learning(DL)that can be used as a surveillance system to identify infected individuals by analyzing tweets related to COVID-19.The system is used only for surveillance purposes to identify regions where the spread of COVID-19 is high;clinical tests should then be used to test and identify infected individuals.The system proposed here uses recurrent neural networks(RNN)and word-embedding techniques to analyze tweets and determine whether a tweet provides information about COVID-19 or refers to individuals who have been infected with the virus.The results demonstrate that RNN can conduct this analysis more accurately than other machine learning(ML)algorithms.
基金This research was funded by National Natural Science Foundation of China under Grant No.61806171Sichuan University of Science&Engineering Talent Project under Grant No.2021RC15+2 种基金Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computer of Sichuan Province Universities on Bridge Inspection and Engineering under Grant No.2022QYJ06Sichuan University of Science&Engineering Graduate Student Innovation Fund under Grant No.Y2023115The Scientific Research and Innovation Team Program of Sichuan University of Science and Technology under Grant No.SUSE652A006.
文摘While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.
文摘现有文本分类模型对文本的全局信息和局部信息利用不足,导致文本分类性能较差。针对这一问题,提出一种将文本的全局和局部特征动态融合(global and local features dynamic fusion,GLFDF)的分类模型。所提模型首先设计动态融合增强模块动态控制文本的全局时序特征与局部语义特征融入单词嵌入矩阵的每个特定位置;其次,将融合全局和局部特征的嵌入矩阵馈送到特征提取模块中进行特征提取;最后,在Ohsumed和THUCNews数据集上测试所提模型的效果。实验结果表明:GLFDF模型在2个数据集上的F1值分别达到63.24%和92.50%,优于其他文本分类模型,提高了文本分类的性能。由消融实验分析可知,动态融合增强模块可以充分融合文本的全局时序特征和局部语义特征,有效解决文本分类模型对全局信息和局部信息利用不足的问题。
文摘针对中文文本分类准确率低、单一的卷积神经网络模型难以提取多方面特征的问题,本文提出一种基于CNN的并行门控机制的混合文本分类模型——CGGA(Convolutional Neural Network with parallel gating unit and attention mechanism).利用卷积提取文本的局部特征,并加入双向门控循环单元对数据进行上下文数据建模,提取关系特征,同时,引入门控Tanh-ReLU单元进行进一步的特征筛选,从而控制信息向下层流动的力度,并且减轻梯度弥散,提高模型分类准确率.最后,使用多头注意力机制进行权重更新计算,以提高在相应文本类别上的输出,进而优化模型分类性能.实验结果显示,本文提出的文本分类模型和分类算法,在THUCNews数据集和搜狐数据集上,比基线模型的宏平均精确率分别提高了2.24%、6.78%.
