Fuels, gases, and other such substances are widely used in domestic and
industrial settings daily. However, they frequently result in significant mishaps like
fires and gas leaks. If prompt notification is received, such accidents can be avoided.
Installing a gas leakage and fire incident detection system in strategic locations is one
approach to achieve this. Here, we demonstrate the construction of a straightforward
system that sends an SMS using a GSM module in the event of a fire or gas leak.
Additionally, a temperature sensor simultaneously detects the temperature of that
difficult circumstance and transmits information to a web server. This is achieved with
the use of the Internet of Things (IoT), neural computing, and machine learning. We
employ a system with multi-sensing and interaction with the current centralized M2M
(Machine-to-Machine) home network and external networks in place of discrete units
with basic functionality (such as the Internet). Then, using machine learning, we apply
a data mining technique to the sensed data and find anomalous changes for early risk
prediction. The system's goals are to increase security and safety and safeguard
properties.
Keywords: Alert system, IoT, Machine learning, Neural network, Risk prediction.
