An efficient and robust communication channel is a critical factor in underground coal mines to ensure the safety of the working environment. Through this paper, we explore how the functionalities and operational and analytical efficiency of SAGES can be analyzed utilizing historical data acquired during the depillaring operations carried out in underground coal mines using SAGES for safety and productivity during deployment with the incorporation of IoT with Artificial Intelligence. Applying predictive machine learning of convergence, load at withdrawal, and duration of yielding, in hrs will automate the analysis of the after-effects of depillaring operations. This will ultimately help in the maintenance of components of SAGES and measures to be taken on the safety of the workplace. Linear estimators, gradient boosting, and clustering methods are employed to detect the output properties of the deployed SAGES. Further, we illustrate and analyze a fine-tuning approach for supervised and unsupervised algorithms subsequently, which are analyzed and the performance of various estimator models in our experiments are compared. To end with, we perform an ensemble of the models for each target variable.

Causality detection draws plenty of attention in the field of Natural Language Processing and linguistics research. It has essential applications in information retrieval, event prediction, question answering, financial analysis, and market research. In this study, we explore several methods to identify and extract cause-effect pairs in financial documents using transformers. For this purpose, we propose an approach that combines POS tagging with the BIO scheme, which can be integrated with modern transformer models to address this challenge of identifying causality in a given text. Our best methodology achieves an F1-Score of 0.9551, and an Exact Match Score of 0.8777 on the blind test in the FinCausal-2021 Shared Task at the FinCausal 2021 Workshop.