In this research, the integration of meta-heuristic optimization into health monitoring systems is explored for its transformative potential. The study employs a comprehensive evaluation approach, focusing on Performance Metrics, Resource Utilization, and Scalability Testing. Results indicate a consistently high level of accuracy (90% to 97%) and swift response times (125 to 165 milliseconds), highlighting the reliability and efficiency of the enhanced system. Resource Utilization demonstrates optimal memory and CPU usage (110 to 130 MB and 30% to 47%, respectively), underscoring the sustainable and balanced operation of the system. Scalability Testing reveals the system's adaptability to changes in user numbers and data complexity, with response times ranging from 150 to 200 milliseconds. Meta-heuristic optimization emerges as a key enabler, fine-tuning predictive capabilities, optimizing resource usage, and ensuring seamless scalability.
Metaheuristic and Machine Learning Optimization Strategies for Complex Systems
Top1. Introduction
Health monitoring, through the use of sensors in cloud computing, is a huge stride in insuring the well-being of humans (M. Vijayakumar et al., 2023). Having a thorough method to monitor one's health has become more important than ever in our fast-paced, high-demanding environment (Siddiqui et al., 2023). Imagine having a personal health companion, always alert and ready to give insights about your well-being, contributing to a proactive approach to healthcare (Mehta et al., 2023). The integration of sensors into health monitoring technology has altered the way we observe and control our health (Loganathan et al., 2023). These sensors may detect a variety of critical indicators, including heart rate, sleep patterns, and physical activity (Josphineleela, Lekha, et al., 2023; Reddy et al., 2023). They are commonly used in wearable electronics like smartwatches and fitness trackers (Josphineleela, Kaliapp, et al., 2023; Suman et al., 2023). Their real-time snapshot of an individual's health status is provided by a continuous stream of data (Balamurugan et al., 2023; Kaliappan, Mothilal, et al., 2023). This ongoing flow of information enables early identification of potential health conditions, helping individuals to conduct preventive measures and make educated decisions regarding their well-being (Natrayan, Kaliappan, Saravanan, et al., 2023).
In parallel, cloud computing plays a vital role in improving the capabilities of health monitoring systems. Cloud computing permits for the storing and processing of massive volumes of health data collected by sensors (Lakshmaiya, 2023e, 2023j, 2023d, 2023a, 2023b). This allows seamless access to information from anywhere at any time, breaking down geographical boundaries in healthcare (Kaliappan, Natrayan, & Garg, 2023a; Natrayan & Kaliappan, 2023). A centralized hub made possible by the cloud allows authorized users—including medical professionals and the patients themselves—to securely store, analyze, and share health data (Kaliappan, Natrayan, & Rajput, 2023; Kaushal et al., 2023; Natrayan, Kaliappan, & Pundir, 2023; Selvi et al., 2023). It is possible to take health monitoring systems to new heights with the help of a technical approach known as meta-heuristic optimization (Natrayan & De Poures, 2023b, 2023a). In simple terms, meta-heuristic optimization uses advanced algorithms that can learn and change over time, much like the human brain (Lakshmaiya & Murugan, 2023d, 2023b, 2023e, 2023c, 2023a). These algorithms continuously adjust their strategies in response to new inputs, therefore optimizing the overall performance of the system. The process is comparable to having a clever assistant optimize a health monitoring system so it performs at peak efficiency. Meta-heuristic optimization holds enormous promise for health monitoring (Natrayan & Richard, 2023b, 2023a). These algorithms can enhance the accuracy of health projections, making the system more dependable in predicting future health hazards. Meta-heuristic optimization ensures that the health monitoring system runs smoothly and doesn't put undue strain on the device or the cloud infrastructure by strategically allocating resources like memory and processing power (Sukumaran et al., 2023).