A Design of Intelligent Autonomous Mining with Dynamic Time Warping Through Deep Learning for End-to-End Industrial Internet of Things Application

Abstract

Industrial Internet of Things (IIoT) environment is tremendously developed in recent times which needs improvement in terms of Autonomous Mining. For that purpose, in this article Intelligent Autonomous Mining with Dynamic Time Warping through Deep Learning (IADTDL) is developed. The architecture of the model consists of core modules, including system model construction, dataset details, IIoT architecture, hybrid feature extraction, semantic segmentation process, modeling approach and selection, deep learning classification, equipment description, and input data source integration. The proposed model is implemented and simulated using Python. Performance evaluation is carried out using key metrics such as accuracy, loss, precision, recall, F1-score, and ROC curve. The results are compared with existing baseline models, including STRCA, DIMLF, and UDMCP. Experimental findings demonstrate that the proposed IADTDL model achieves superior performance across all evaluation metrics, which greatly helps to enhance the suitability for intelligent autonomous mining in IIoT applications.