A face recognition and intelligent home automation system.

Intelligent The way we interact with our living areas has been completely transformed by home automation systems. Through the integration of microcontrollers, sensors, and Internet of Things devices, these systems are able to reliably determine occupancy levels and guarantee house fire safety. This paper's main goal is to conduct a thorough analysis of an intelligent home automation system. Furthermore, for facial recognition and intelligent home automation systems, Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are introduced. This clever home automation setup employs IoT, microcontrollers, and an array of sensors to identify occupancy levels, oversee potential fire hazards, and bolster safety measures in a residential setting. Through the integration of cutting-edge technologies, the aim is to automate tasks, heighten convenience, and ensure the welfare of inhabitants. Within the findings, a thorough exploration of the architecture, constituents, and operations of the intelligent home automation system is presented. Special attention is given to the pivotal role played by IoT, microcontrollers, and sensors in detecting occupancy as well as monitoring fire risks and safety measures. This study concludes by emphasizing the potency of IoT, microcontrollers, and sensor technologies within home automation systems, particularly in ensuring safety. The experimental results prove that the CNN has better accuracy than SVM for face recognition and intelligent home automation system. [ABSTRACT FROM AUTHOR]