Leukemia refers to a type of blood malignancy that develops due to certain hematological
disorders. Identifying leukemia at its earlier stages through clinical operations are highly …

Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …

In the field of medical imaging, artificial intelligence solutions are used for diagnosis,
prediction and treatment processes. Such solutions are vulnerable to cyberattacks …

Background: The use of intelligent decision support systems (IDSS) is widespread in the
healthcare industry, particularly for real-time data, client and family history datasets, and …

The development of machine learning algorithms has revolutionized the medical data
categorization industry through the introduction of artificial intelligence. The development of …

Abstract Machine learning and deep learning are branches of artificial intelligence
consisting of statistical, probabilistic, and optimisation techniques that allow machines to …

The growing capabilities of AI raise questions about their trustworthiness in healthcare,
particularly due to opaque decision-making and limited data availability. This paper …

Sažetak Data used in big data applications are typically kept in decentralized computing
resources in the real world, which has an impact on the design of artificial intelligence …

This paper investigates the effectiveness of the logistic regression (LR) and random forest
(RF) algorithms for classifying breast cancer using the Breast Cancer Wisconsin Dataset …

Computation offloading in multi-access edge computing (MEC) is an effective paradigm for
enabling resource-intensive smart applications. However, when the wireless channel …