An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network
Author gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email,...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8995513/ |
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| author | Fatemeh Safara Amin Salih Mohammed Moayad Yousif Potrus Saqib Ali Quan Thanh Tho Alireza Souri Fereshteh Janenia Mehdi Hosseinzadeh |
| author_facet | Fatemeh Safara Amin Salih Mohammed Moayad Yousif Potrus Saqib Ali Quan Thanh Tho Alireza Souri Fereshteh Janenia Mehdi Hosseinzadeh |
| author_sort | Fatemeh Safara |
| collection | DOAJ |
| description | Author gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email, blogs, forums. Text and writings of people on the Internet have valuable information that can be used to identify the gender of an author. Machine learning and meta-heuristic algorithms are valuable techniques to extract hidden patterns useful for detecting gender of a text. In this paper, an artificial neural network (ANN) is employed as a classifier to detect the gender of an email author and the whale optimization algorithm (WOA) is used to find optimal weights and biases for improving the accuracy of the ANN classification. Through this combination of ANN and WOA an accuracy of 98%, precision of 97.16%, and recall of 99.67% were achieved, which indicates the superiority of the proposed method on Bayesian networks, regression, decision tree, support vector machine, and ANN examined. |
| format | Article |
| id | doaj-art-340aba7a350c4e1aa5fdbbec9d359e00 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-340aba7a350c4e1aa5fdbbec9d359e002025-08-20T02:06:50ZengIEEEIEEE Access2169-35362020-01-018484284843710.1109/ACCESS.2020.29735098995513An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural NetworkFatemeh Safara0Amin Salih Mohammed1Moayad Yousif Potrus2Saqib Ali3Quan Thanh Tho4Alireza Souri5https://orcid.org/0000-0001-8314-9051Fereshteh Janenia6Mehdi Hosseinzadeh7https://orcid.org/0000-0003-1088-4551Department of Computer Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, IranDepartment of Computer Engineering, Lebanese French University, Erbil, IraqDepartment of Software and Informatics Engineering, Salahaddin University-Erbil, Erbil, IraqDepartment of Information Systems, College of Economics and Political Science, Sultan Qaboos University, Muscat, OmanDepartment of Software Engineering, Ho Chi Minh City University of Technology–Vietnam National University, Ho Chi Minh City, VietnamDepartment of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Computer Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, IranInstitute of Research and Development, Duy Tan University, Da Nang, VietnamAuthor gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email, blogs, forums. Text and writings of people on the Internet have valuable information that can be used to identify the gender of an author. Machine learning and meta-heuristic algorithms are valuable techniques to extract hidden patterns useful for detecting gender of a text. In this paper, an artificial neural network (ANN) is employed as a classifier to detect the gender of an email author and the whale optimization algorithm (WOA) is used to find optimal weights and biases for improving the accuracy of the ANN classification. Through this combination of ANN and WOA an accuracy of 98%, precision of 97.16%, and recall of 99.67% were achieved, which indicates the superiority of the proposed method on Bayesian networks, regression, decision tree, support vector machine, and ANN examined.https://ieeexplore.ieee.org/document/8995513/Author gender detectionmachine learningartificial neural networkwhale optimization algorithm |
| spellingShingle | Fatemeh Safara Amin Salih Mohammed Moayad Yousif Potrus Saqib Ali Quan Thanh Tho Alireza Souri Fereshteh Janenia Mehdi Hosseinzadeh An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network IEEE Access Author gender detection machine learning artificial neural network whale optimization algorithm |
| title | An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network |
| title_full | An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network |
| title_fullStr | An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network |
| title_full_unstemmed | An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network |
| title_short | An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network |
| title_sort | author gender detection method using whale optimization algorithm and artificial neural network |
| topic | Author gender detection machine learning artificial neural network whale optimization algorithm |
| url | https://ieeexplore.ieee.org/document/8995513/ |
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