IoT-based unified child monitoring and security system

In this fast-growing modern era, mothers are likely to work soon after childbirth, which makes it hard for them to render complete care to their child. Hiring a childminder is not just costly but also unsafe, especially during a global pandemic like Covid-19. Child abuse is also a major worry. This paper introduces an IoT-based Unified child monitoring and security system without any third-party involvement, thus addressing the parents' needs and concerns. The proposed system monitors the temperature and heart rate of theinfant, humidity of the room, detects motion and sound produced by the baby and adopts suitable measures to notify the parent such as sending alert messages, live video streaming of the infant or turning on a motor to swing the cradle. This system also monitors the movement of toddlers using GPS-and GSM-enabled wrist-bands and continuously sends their live locations. A buzzer is also interfaced with the band to alert if any stranger is in close proximity with the toddler. This system enables parents to keep a watch on their children remotely and thus ensures the safety of the child from any type of abuse. An added feature of this band is that it also prompts to maintain social distancing from the toddlers. Overall, a reliable, continuous and real-time baby monitoring is ensured by the proposed system. © 2023 Scrivener Publishing LLC. All rights reserved.

Ensemble Model to Forecast the End of the Covid-19 Pandemic

The coronavirus disease 2019 (Covid-19) epidemic has caused a worldwide health catastrophe that has had a profound influence on how we see our planet and our daily lives. In this pandemic circumstance, machine learning (ML) based prediction models demonstrate their value in predicting perioperative outcomes to enhance decision-making on future course of action. Ensemble learning is used in the majority of ML based forecasting approaches. The ML models anticipate the number of patients who will be affected by Covid-19, and use this information to forecast the end of the pandemic is to be leveraged. Three types of predictions are made: the number of newly infected cases, the number of deaths, and the number of recoveries in the next ‘x’ number of days. By combining one of the forecasting models with classifiers, we can predict the end of the pandemic. The proposed idea combines the SIRF model from epidemiology and a forecasting machine learning model named Prophet and a Naïve Bayes Classifier to predict the end of the pandemic. Using the theoretical equations of the SIRF model, we developed a formula for infectious growth rate. The classifier uses this infectious growth rate to check if the infection is fading. With confirmed, recovered and fatalities data, the infectious growth rate is calculated. Naïve Bayes classifier is used to check if the pandemic is about to end or not. If not then forecast the data for ‘x’ number of days and do the calculations again. The process continues until we get a time frame where the pandemic may reach its end. The results are discussed for 2 countries India and Israel. The forecasts done for Israel were very accurate to the actual data, whilst for India it was less comparatively as India was hit by 2 waves of Covid-19 pandemic. By leveraging the forecasting and classification capabilities of machine learning models like FBProphet, Naïve Bayes Classifier, and the mathematical equations of the SIRF model from epidemiology, the life span of the pandemic is determined. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.