Generic Artificial Intelligent Agent Using Iot And Deep Learning

Abstract

the rapid integration of the Internet of Things (IoT) with artificial intelligence has unlocked new opportunities to develop
adaptable, multi-domain artificial intelligence (AI) agents. However, the design of many AI agents for specific tasks limits their ability to generalize across different applications and environments. This paper introduces a generic artificial intelligent agent that utilizes IoT and deep learning, enabling autonomous adaptation to diverse domains such as smart homes, healthcare, industrial automation, and agriculture. The agent leverages IoT sensors for real-time data collection, while deep learning models process and analyze this data to make intelligent, context-aware decisions. Using a combination of initial training and domain adaptation techniques, the agent can learn to recognize patterns and perform tasks across diverse environments. This research proposes an intelligent facial identification and recognition system powered by deep learning. It automatically updates the identification records of individual personnel based on the results generated by the recognition process. Utilizing deep learning models, the system performs both facial recognition and object detection, ensuring high accuracy and
reliable performance.