Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims a...Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.展开更多
This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the trans...This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.展开更多
文摘Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.
文摘This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.