Augmenting Automation: Intent-Based User Instruction Classification with
  Machine Learning
Lochan Basyal Bijay Gaudel
Electric automation systems offer convenience and efficiency in controlling
electrical circuits and devices. Traditionally, these systems rely on
predefined commands for control, limiting flexibility and adaptability. In this
paper, we propose a novel approach to augment automation by introducing
intent-based user instruction classification using machine learning techniques.
Our system represents user instructions as intents, allowing for dynamic
control of electrical circuits without relying on predefined commands. Through
a machine learning model trained on a labeled dataset of user instructions, our
system classifies intents from user input, enabling a more intuitive and
adaptable control scheme. We present the design and implementation of our
intent-based electric automation system, detailing the development of the
machine learning model for intent classification. Experimental results
demonstrate the effectiveness of our approach in enhancing user experience and
expanding the capabilities of electric automation systems. Our work contributes
to the advancement of smart technologies by providing a more seamless
interaction between users and their environments.