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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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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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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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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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Nicholas Fiorentini and Massimo Losa
Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that...
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Mauricio Barrios, Miguel Jimeno, Pedro Villalba and Edgar Navarro
Metabolic Syndrome (MetS) is a set of risk factors that increase the probability of heart disease or even diabetes mellitus. The diagnosis of the pathology implies compliance with at least three of five risk factors. Doctors obtain two of those factors i...
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Daniel Jerouschek, Ömer Tan, Ralph Kennel and Ahmet Taskiran
Voltage models of lithium-ion batteries (LIB) are used to estimate their future voltages, based on the assumption of a specific current profile, in order to ensure that the LIB remains in a safe operation mode. Data of measurable physical features?curren...
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Zhangren Tu, Huiting Liu, Jiaying Zhan and Di Guo
Multidimensional nuclear magnetic resonance (NMR) spectroscopy is one of the most crucial detection tools for molecular structure analysis and has been widely used in biomedicine and chemistry. However, the development of NMR spectroscopy is hampered by ...
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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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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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Moussa Diallo, Shengwu Xiong, Eshete Derb Emiru, Awet Fesseha, Aminu Onimisi Abdulsalami and Mohamed Abd Elaziz
Classification algorithms have shown exceptional prediction results in the supervised learning area. These classification algorithms are not always efficient when it comes to real-life datasets due to class distributions. As a result, datasets for real-l...
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