Design and Optimization of Deep Neural Networks for Multi-Label Classification on Smart Electrical Devices Monitoring System

This paper presents the design and optimization of deep neural networks (DNNs) tailored for multi-label classification on smart electrical devices monitoring system, an emerging category of intelligent measurement devices. The smart system is equipped with advanced sensing and data acquisition capabilities, generating complex datasets necessitating sophisticated analytical methods. This complex dataset comprises various properties regarding electrical devices, which can be used to train and evaluate an artificial intelligence (AI) model for classifying home devices when plugged in. Various DNN architectures are explored by using neural architecture search (NAS) and optimization techniques like pruning to enhance classification accuracy and computational efficiency. The article's approach includes leveraging transfer learning, regularization methods, and hyperparameter tuning to address the challenges of multi-label classification. Experimental results demonstrate significant improvements in prediction performance compared to traditional methods. The findings underscore the potential of optimized DNNs in advancing the functionality and reliability of the smart system in diverse application scenarios.