TY - JOUR
T1 - An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network
AU - Safara, Fatemeh
AU - Mohammed, Amin Salih
AU - Yousif Potrus, Moayad
AU - Ali, Saqib
AU - Tho, Quan Thanh
AU - Souri, Alireza
AU - Janenia, Fereshteh
AU - Hosseinzadeh, Mehdi
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Author gender detection
KW - artificial neural network
KW - machine learning
KW - whale optimization algorithm
UR - http://www.scopus.com/inward/record.url?scp=85082690499&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082690499&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2973509
DO - 10.1109/ACCESS.2020.2973509
M3 - Article
AN - SCOPUS:85082690499
SN - 2169-3536
VL - 8
SP - 48428
EP - 48437
JO - IEEE Access
JF - IEEE Access
M1 - 8995513
ER -