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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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Lucas Lopes Oliveira, Xiaorui Jiang, Aryalakshmi Nellippillipathil Babu, Poonam Karajagi and Alireza Daneshkhah
Early identification of acute gout is crucial, enabling healthcare professionals to implement targeted interventions for rapid pain relief and preventing disease progression, ensuring improved long-term joint function. In this study, we comprehensively e...
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Sufyan Danish, Asfandyar Khan, L. Minh Dang, Mohammed Alonazi, Sultan Alanazi, Hyoung-Kyu Song and Hyeonjoon Moon
Bioinformatics and genomics are driving a healthcare revolution, particularly in the domain of drug discovery for anticancer peptides (ACPs). The integration of artificial intelligence (AI) has transformed healthcare, enabling personalized and immersive ...
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Nikola Andelic, Sandi Baressi ?egota, Matko Glucina and Ivan Lorencin
In autonomous manufacturing lines, it is very important to detect the faulty operation of robot manipulators to prevent potential damage. In this paper, the application of a genetic programming algorithm (symbolic classifier) with a random selection of h...
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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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Hala Al Nuaimi, Mohamed Abdelmagid, Ali Bouabid, Constantinos V. Chrysikopoulos and Maher Maalouf
A substantial portion of the water supply and sanitation (WatSan) infrastructure in the rural areas of developing countries is currently not operating. This failure is due to the inappropriate implementation of WatSan technologies and the lack of decisio...
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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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Aristeidis Karras, Christos Karras, Konstantinos C. Giotopoulos, Dimitrios Tsolis, Konstantinos Oikonomou and Spyros Sioutas
Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent chall...
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Nikola Andelic, Sandi Baressi ?egota and Zlatan Car
Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automat...
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Nikola Andelic, Ivan Lorencin, Sandi Baressi ?egota and Zlatan Car
Hepatitis C is an infectious disease which is caused by the Hepatitis C virus (HCV) and the virus primarily affects the liver. Based on the publicly available dataset used in this paper the idea is to develop a mathematical equation that could be used to...
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