Pervasive healthcare computing

TL;DR: A three-phase air pollution monitoring system analogous to Google traffic or the navigation application of Google Maps is proposed, and air quality data can be used to predict future air quality index (AQI) levels.

TL;DR: The experimental results show that the proposed approach can significantly enhance the conventional mobile-based medical monitoring in terms of diagnostic accuracy, execution efficiency, and energy efficient, and holds the potential in addressing future large-scale data analysis in personalized healthcare.

TL;DR: The system is an application of IoT deploying LoRa wireless communication under fog computing, benefitting the system with both the qualities of LoRa and fog computing and can be promising for changing the clinic-centrichealth system to smart patient-centric health system and for providing seamless health services to all.

TL;DR: This paper analyzes and compares different machine learning algorithms to identify a best predicting algorithm based on various metrics such as accuracy, kappa, precision, recall, sensitivity and specificity and shows that RF is giving more accurate predictions with compared to other algorithms.