SIMULATION OF BUCK CONVERTER USING SIMULINK AND SPICE TOOL

Abstract

Strong predictive capabilities are introduced by integrating the ACO-LSTM model with cloud- based healthcare systems, enabling patient health status predictions and ongoing monitoring. The concept provides real-time feedback to patients and healthcare practitioners based on data gathered from wearable sensors and Internet of Things devices. By combining the sequential
data processing capabilities of LSTM with the optimisation efficiency of ACO, the model is well-suited to detect early indicators of disease progression, allowing for prompt intervention. Furthermore, by utilising the scalability and dependability of cloud computing, this strategy tackles typical problems with conventional healthcare IT systems, like constrained processing power and real-time data accessibility. Therefore, the ACO-LSTM model has the potential to revolutionise patient care by providing dynamic and responsive illness forecasting that improves patient outcomes through prompt, individualised therapy based on accurate, predictive insights.