Abstract Machine learning methods promote the sustainable development of wise
information technology of medicine (WITMED), and a variety of medical data brings high …

Data transparency is beneficial to data participants' awareness, users' fairness, and
research work's reproducibility. However, when addressing transparency requirements, we …

Integrating Internet technologies with traditional healthcare systems has enabled the
emergence of cloud healthcare systems. These systems aim to optimize the balance …

In spite of multiple advantages of the adoption of blockchain (BC), it still faces some
integration challenges in modern applications, such as the Internet of Things. These …

The privacy-preserving data publishing (PPDP) problem has gained substantial attention
from research communities, industries, and governments due to the increasing requirements …

Distributed database system (DDBS) design is still an open challenge even after decades of
research, especially in a dynamic network setting. Hence, to meet the demands of high …

Population-based optimization algorithms, such as genetic algorithm and particle swarm
optimization, have become a class of important algorithms for solving global optimization …

Feature selection is a crucial process in data science that involves selecting the most
effective subset of features. Evolutionary computation (EC) is one of the most commonly …

Purpose Cognitive diagnostic tests (CDTs) assess cognitive skills at a more granular level,
providing detailed insights into the mastery profile of test-takers. Traditional algorithms for …

Virtual Machine (VM) allocation are the crucial problems because cloud computing enables
the rapid growth of data centres and compute centres. Power consumption and network …