Smart Machine Learning-based IoT Health and Cough Monitoring System

Abstract

Coronavirus 2019, more commonly known as COVID-19, was declared a global pandemic by the World Health Organization (WHO) on March 11, 2020. The β coronavirus culpable for the disease, SARS CoV-2, is known to be highly contagious with a relatively long incubation period of up to 14 days and is transmittable through small droplets, especially among people who are in close face-to-face contact. The Ministry of Health of Malaysia has recommended five days of quarantine for people who are positive for COVID-19 to avoid further disease transmission. Many resources are used to monitor patients throughout the quarantine period. Therefore, this project would like to present an IoT-enabled wearable device capable of monitoring COVID-19 quarantine patients by utilizing sensors to monitor the necessary health parameters and facilitate home quarantine. The low-cost ESP32 and Arduino Nano 33 BLE Sense microcontrollers are used in this device. They are connected to various IoT sensors to collect temperature, humidity, and sound data. The data obtained will then be uploaded to an IoT platform for doctors to analyze and monitor remotely via the health log throughout the 5-day quarantine period. An alert system is also devised to inform the medical staff if the patient is experiencing abnormal symptoms. The medical staff can then bring their attention to the patient and take the necessary actions to combat COVID-19.

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

“…Artificial intelligence (AI) including machine learning (ML) and combined algorithms can be utilized in many fields such as energy and fuels (Drzewiecki and Guziński, 2023;Goyal et al, 2023;Sharma et al, 2023), education (Haque et al, 2024;Kim, 2024;Kim et al, 2023), communication (Hu and Qin, 2017;Melinda et al, 2024;Rumapea et al, 2024), chemical engineering (Aniza et al, 2023;Dobbelaere et al, 2021), industry manufacturing (Chau et al, 2021;Lee et al, 2018), transportation and logistics (Hu, 2018;Radonjić et al, 2020;Witkowska and Rynkiewicz, 2018;Zaki, 2024), medical (Haleem et al, 2019;Pang et al, 2023;Yunidar and Melinda, 2023), social study (Liu et al, 2023;Triandi et al, 2023), environment (Biswas et al, 2023;Chaoraingern et al, 2023;Domachowski, 2021;Vo et al, 2021), and economy (Furman and Seamans, 2019;Suvon et al, 2023) aiming to enhance management effectiveness. For energy area, AI could be used for forecasting energy production and demand prediction (Aguilar et al, 2021;Ahmad et al, 2021;Mosavi et al, 2019), energy theft detection (Ahmad et al, 2021), demand side management (Antonopoulos et al, 2020), predictive maintenance and monitoring (H. P. Wedashwara et al, 2023), optimized energy operation (El-Shafay et al, 2023;Goswami ...…”