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Sikha Bagui, Dustin Mink, Subhash Bagui, Sakthivel Subramaniam and Daniel Wallace
This study, focusing on identifying rare attacks in imbalanced network intrusion datasets, explored the effect of using different ratios of oversampled to undersampled data for binary classification. Two designs were compared: random undersampling before...
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Yingze Song, Degang Yang, Weicheng Wu, Xin Zhang, Jie Zhou, Zhaoxu Tian, Chencan Wang and Yingxu Song
Landslide susceptibility assessment (LSA) based on machine learning methods has been widely used in landslide geological hazard management and research. However, the problem of sample imbalance in landslide susceptibility assessment, where landslide samp...
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Haoxiang Shi, Jun Ai, Jingyu Liu and Jiaxi Xu
Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by genera...
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Sikha S. Bagui, Dustin Mink, Subhash C. Bagui and Sakthivel Subramaniam
Machine Learning is widely used in cybersecurity for detecting network intrusions. Though network attacks are increasing steadily, the percentage of such attacks to actual network traffic is significantly less. And here lies the problem in training Machi...
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Maria Nefeli Nikiforos, Konstantina Deliveri, Katia Lida Kermanidis and Adamantia Pateli
Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational backgrounds, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communit...
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Javad Hassannataj Joloudari, Abdolreza Marefat, Mohammad Ali Nematollahi, Solomon Sunday Oyelere and Sadiq Hussain
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, ma...
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Amani Alqarni and Hamoud Aljamaan
Software defect prediction is an active research area. Researchers have proposed many approaches to overcome the imbalanced defect problem and build highly effective machine learning models that are not biased towards the majority class. Generative adver...
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Nikola Andelic, Sandi Baressi ?egota, Ivan Lorencin and Matko Glucina
Malicious websites are web locations that attempt to install malware, which is the general term for anything that will cause problems in computer operation, gather confidential information, or gain total control over the computer. In this paper, a novel ...
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Md. Maniruzzaman, Jungpil Shin and Md. Al Mehedi Hasan
Attention deficit hyperactivity disorder (ADHD) is one of childhood?s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-b...
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Maya Hilda Lestari Louk and Bayu Adhi Tama
Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability for intrusion detection systems remain questionable in current research, owing to the particularly imbalanced data issu...
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