Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregul...Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment phrases.The identification of fake news and rumors and their dissemination on social media has become a critical requirement.They have adverse effects on users,businesses,enterprises,and even political regimes and governments.State of the art has tackled the English language for news and used feature-based algorithms.This paper proposes a model architecture to detect fake news in the Arabic language by using only textual features.Machine learning and deep learning algorithms were used.The deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+BiLSTM.Three datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife data.The results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.展开更多
The outbreak of Covid-19 has taken the lives of many patients so far.The symptoms of COVID-19 include muscle pains,loss of taste and smell,coughs,fever,and sore throat,which can lead to severe cases of breathing diffi...The outbreak of Covid-19 has taken the lives of many patients so far.The symptoms of COVID-19 include muscle pains,loss of taste and smell,coughs,fever,and sore throat,which can lead to severe cases of breathing difficulties,organ failure,and death.Thus,the early detection of the virus is very crucial.COVID-19 can be detected using clinical tests,making us need to know the most important symptoms/features that can enhance the decision process.In this work,we propose a modified multilayer perceptron(MLP)with feature selection(MLPFS)to predict the positive COVID-19 cases based on symptoms and features from patients’electronic medical records(EMR).MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance.Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy.Experiments were conducted using three different COVID-19 datasets and eight different models,including the proposed MLPFS.Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models.Additionally,it outperforms the other models in classification results as well as time.展开更多
文摘Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment phrases.The identification of fake news and rumors and their dissemination on social media has become a critical requirement.They have adverse effects on users,businesses,enterprises,and even political regimes and governments.State of the art has tackled the English language for news and used feature-based algorithms.This paper proposes a model architecture to detect fake news in the Arabic language by using only textual features.Machine learning and deep learning algorithms were used.The deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+BiLSTM.Three datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife data.The results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.
文摘The outbreak of Covid-19 has taken the lives of many patients so far.The symptoms of COVID-19 include muscle pains,loss of taste and smell,coughs,fever,and sore throat,which can lead to severe cases of breathing difficulties,organ failure,and death.Thus,the early detection of the virus is very crucial.COVID-19 can be detected using clinical tests,making us need to know the most important symptoms/features that can enhance the decision process.In this work,we propose a modified multilayer perceptron(MLP)with feature selection(MLPFS)to predict the positive COVID-19 cases based on symptoms and features from patients’electronic medical records(EMR).MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance.Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy.Experiments were conducted using three different COVID-19 datasets and eight different models,including the proposed MLPFS.Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models.Additionally,it outperforms the other models in classification results as well as time.
