Revolutionizing Remote Patient Monitoring with AI and IoT (Published)
Amidst the growing trend of chronic disease and the need for continuous, longitudinal care focused on the patient, Remote Patient Monitoring (RPM) systems have been on the rise. This research aims to assess the effectiveness of Artificial Intelligence (AI) and the Internet of Things (IoT) in addressing the efficiency, sensitivity, and generalizability of RPM systems. This research is qualitative and quantitative in nature, utilizing biological real-time signals from publicly available datasets (MIT-BIH, MIMIC-III, Fitbit), employing AI methodologies (Random Forest and Convolutional Neural Network (CNN)) for classifying and predicting anomalies. The proposed edge-enabled Internet of Things architecture lowers latency by 35%; CNNs achieve 93.2% accuracy in electrocardiograms (ECG) classification. Qualitative subject-matter expert responsiveness from healthcare professionals noted a 40% increase in timely intervention for detected anomalies—with confidence in the usability of the systems. Findings advocate AI and IoT enhancements for smart real-time monitoring of health-related information.
Keywords: Convolutional Neural Networks (CNN), Edge Computing, Healthcare Informatics, Internet of Things (IoT), IoMT, Physiological Signal Analysis, Remote Patient Monitoring (RPM), Smart Wearables, artificial intelligence (AI), predictive analytics
AI-Driven Cloud Optimization for Cost Efficiency (Published)
AI-driven cloud optimization represents a transformative approach to addressing the significant challenges of cloud resource management and cost efficiency. As global cloud expenditure continues to grow at a rapid pace, organizations face increasing pressure to optimize their cloud investments while maintaining performance standards. This article examines how artificial intelligence technologies are revolutionizing cloud resource management through dynamic allocation, predictive analytics, and automated workload optimization. The integration of machine learning algorithms with cloud infrastructure enables unprecedented levels of accuracy in resource forecasting, automated scaling, and workload classification. These capabilities allow organizations to significantly reduce both over-provisioning and under-provisioning scenarios that plague traditional threshold-based management approaches. The economic benefits of these technologies are substantial and multifaceted, extending beyond direct cost reduction to include improved application performance, reduced downtime, and decreased operational overhead. As the complexity of cloud environments continues to increase, the strategic value of AI-driven optimization becomes increasingly apparent across diverse industry sectors, from financial services to healthcare and e-commerce.
Keywords: Artificial Intelligence, Cloud optimization, Cost Efficiency, Resource Allocation, predictive analytics