Toward Identifying Features for Automatic Gender Detection: A Corpus Creation and Analysis

Alanazi, Saad

doi:10.1109/access.2019.2932026

Cited by 16 publications

(3 citation statements)

References 45 publications

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“…Additionally, applications using physiological cues are primarily effectively developed and employed. Following this, independent deep learning-oriented frameworks limit preprocessing approaches, such as noise filtration, feature extraction [ 59 ], and others. Different organizations have established these frameworks for EC in different domains for diverse purposes.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Alanazi

Alruwaili

Ahmad

et al. 2021

Sensors

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The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).

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Section: Discussionmentioning

confidence: 99%

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Alanazi

Alruwaili

Ahmad

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…For single-label classification tasks, each instance in the training data is associated with one class label ''l'' from a disjoint label set L [38], [39]. When |L| = 2, a learning problem is known as binary classification problem (e.g., the gender identification problem, where the task is to assign an anonymous text to one of two classes, i.e., male or female) [40], [41]. When |L| >2, a learning problem is known as a multi-class classification problem (e.g., authorship identification of single-author documents) [12].…”

Section: Multi-label Classification Techniques and Existing Aimd Tmentioning

confidence: 99%

$CAG$ : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph

et al. 2020

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Stylometry has been successfully applied to perform authorship identification of single-author documents (AISD). The AISD task is concerned with identifying the original author of an anonymous document from a group of candidate authors. However, AISD techniques are not applicable to the authorship identification of multi-author documents (AIMD). Unlike AISD, where each document is written by one single author, AIMD focuses on handling multi-author documents. Due to the combinatoric nature of documents, AIMD lacks the ground truth information-that is, information on writing and non-writing authors in a multi-author document-which makes this problem more challenging to solve. Previous AIMD solutions have a number of limitations: (i) the best stylometry-based AIMD solution has a low accuracy, less than 30%; (ii) increasing the number of co-authors of papers adversely affects the performance of AIMD solutions; and (iii) AIMD solutions were not designed to handle the non-writing authors (NWAs). However, NWAs exist in real-world cases-that is, there are papers for which not every co-author listed has contributed as a writer. This paper proposes an AIMD framework called the Co-Authorship Graph that can be used to (i) capture the stylistic information of each author in a corpus of multi-author documents and (ii) make a multi-label prediction for a multi-author query document. We conducted extensive experimental studies on one synthetic and three real-world corpora. Experimental results show that our proposed framework (i) significantly outperformed competitive techniques; (ii) can effectively handle a larger number of co-authors in comparison with competitive techniques; and (iii) can effectively handle NWAs in multi-author documents.

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“…ese output features are then fed to different classifiers (Adaboost decision tree, Na¨ives Bayes, and SVM) for gender prediction. Different languages are considered for gender determination based on full name by machine learning methods, such as Russian [18,19], Indonesian [14], Chinese [12], Arabic [20], English [21][22][23], Kannada [24], Brazilian [25], ai [26], and Bengali [27].…”

Section: Introductionmentioning

confidence: 99%

A Computational Linguistic Approach for Gender Prediction Based on Vietnamese Names

Huong

Tran-Trung

Hoang

2022

Mobile Information Systems

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Gender prediction is extensively studied in recent years since it is widely applied in many fields. Several factors have been investigated to determine a gender of male or female through facial images, voice, gait, finger print, etc. In this study, we present a machine learning approach for gender determination based on Vietnamese names. A model based on N-gram for the full name, combining its own middle name feature based on the specificity of Vietnamese language, is proposed. The experimental evaluation of gender prediction tasks is applied on GenderVN1.0 dataset (with 3 million Vietnamese names) that achieves 90.9% of accuracy.

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Toward Identifying Features for Automatic Gender Detection: A Corpus Creation and Analysis

Cited by 16 publications

References 45 publications

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

$CAG$ : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph

A Computational Linguistic Approach for Gender Prediction Based on Vietnamese Names

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