IoT and AI Based Android App for Safety Assistance and Recommendation

Research suggests that a person may apply and use a smart phone to expedite daily chores. Nowadays, everyone everywhere needs safety assistance because of the increasing number of traffic-related deaths and injuries brought on by reckless driving, delayed medical attention, sexual harassment of women, child abduction, adventurous travel, crime, terrorism, health, natural disasters, and other related incidents. The primary cause of mortality is a lack of access to high-quality medical care. Appropriate communication and guidance mechanisms are needed to minimize these issues. The main objective of this work is to create a system that may effectively assist in averting any type of safety issue. In such cases, this proposed system provides an effective solution and alert the appropriate authorities and persons so that the issue may be resolved immediately. This initiative aims to combines Artificial Intelligence (AI) and Internet of Things (IOT) to produce the most accurate recommendations for user's safety precautions and also to prevent and detect accidents. Utilizing machine learning methods, this study analyzes datasets created especially for the victim healthcare system. Support Vector Machine (SVM), Neural Network and Naïve Bayes are some of the methods used in this study to forecast the user's critical or non-critical information. And also using proposed stacked ensemble methods provide better accuracy compare with other machine learning models. When the training and testing datasets are split 70:30, the algorithm performs at its most accurate level. Based on experimental study, the suggested stacked ensemble approach outperforms previous methods in terms of prediction accuracy, with a maximum of 0.992.