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Yong Liu, Jialin Zhou, Dong Zhang, Shaoyu Wei, Mingshun Yang and Xinqin Gao
To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generativ...
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Sara Rajaram and Cassie S. Mitchell
The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework ...
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Mohammad Shokouhifar, Mohamad Hasanvand, Elaheh Moharamkhani and Frank Werner
Heart disease is a global health concern of paramount importance, causing a significant number of fatalities and disabilities. Precise and timely diagnosis of heart disease is pivotal in preventing adverse outcomes and improving patient well-being, there...
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SeyedehRoksana Mirzaei, Hua Mao, Raid Rafi Omar Al-Nima and Wai Lok Woo
Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI ...
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Gulsum Alicioglu and Bo Sun
Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitiv...
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Leon Kopitar, Iztok Fister, Jr. and Gregor Stiglic
Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to im...
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Samuel de Oliveira, Oguzhan Topsakal and Onur Toker
Automated Machine Learning (AutoML) is a subdomain of machine learning that seeks to expand the usability of traditional machine learning methods to non-expert users by automating various tasks which normally require manual configuration. Prior benchmark...
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Jinhong Wu, Konstantinos Plataniotis, Lucy Liu, Ehsan Amjadian and Yuri Lawryshyn
Synthetic data, artificially generated by computer programs, has become more widely used in the financial domain to mitigate privacy concerns. Variational Autoencoder (VAE) is one of the most popular deep-learning models for generating synthetic data. Ho...
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Bradley Walters, Sandra Ortega-Martorell, Ivan Olier and Paulo J. G. Lisboa
A lack of transparency in machine learning models can limit their application. We show that analysis of variance (ANOVA) methods extract interpretable predictive models from them. This is possible because ANOVA decompositions represent multivariate funct...
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Mohammad Alauthman, Amjad Aldweesh, Ahmad Al-qerem, Faisal Aburub, Yazan Al-Smadi, Awad M. Abaker, Omar Radhi Alzubi and Bilal Alzubi
Liver diseases are among the most common diseases worldwide. Because of the high incidence and high mortality rate, these diseases diagnoses are vital. Several elements harm the liver. For instance, obesity, undiagnosed hepatitis infection, and alcohol a...
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