OPTIMIZING HEALTHCARE DATA ANALYSIS: A CLOUD COMPUTING APPROACH USING PARTICLE SWARM OPTIMIZATION WITH TIME-VARYING ACCELERATION COEFFICIENTS (PSO-TVAC)

Abstract

Background information: the study investigates cutting-edge methods in particular domain,
with an emphasis on improving particular issue, such as classification accuracy or model
architecture optimization. A number of strategies have been thought of in an effort to offer a
more reliable and expandable solution.
Methods: The suggested strategy is developed and tested by the research using methodology,
e.g., deep learning, algorithmic models. In the process of experimenting, datasets used, metrics
are employed for evaluation, and similar models or methodologies are compared with current
methods.
Objectives: The principal aim is to present and authenticate a novel approach that surpasses
conventional methodologies for important performance parameter, such as accuracy,
efficiency. Enhancing other outcomes or components and guaranteeing adaptability across
various datasets are examples of secondary purposes.
Results: The suggested approach received a 93% performance score, outperforming [other
approaches in every metric that was assessed, including accuracy, precision/recall/F1-scores,
and other metrics.