$1.$ Data Collection and Preprocessing:

Gather a dataset of regulatory paperwork, permits, and compliance reports at the mine $.$ Preprocess the data by cleaning, normalizing, and structuring it to make it suitable for analysis. This step will involve converting unstructured data into a structured format, such as converting scanned documents into text or converting tables into a machine$-$readable format.

$2.$ Natural Language Processing $($NLP$)$:

Apply NLP techniques to extract relevant information from the text data. This may include tokenization, part$-$of$-$speech tagging, named entity recognition, and dependency parsing. These techniques will help the system understand the context, meaning, and relationships between different parts of the text.

$3.$ Rule$-$based System Development:

Develop a rule$-$based system to analyse the pre$-$processed data based on the regulations identified in step $1.$ This system should be able to identify and categorize different types of permits, paperwork, and compliance reports, and check if they adhere to the Mine Health and Safety Act regulations.

$4.$ Machine Learning $($ML$)$ Model Development:

Train ML models to identify patterns and relationships in the data that indicate compliance or non$-$compliance with regulations. This may involve supervised learning models, such as decision trees or support vector machines, or unsupervised learning models, such as clustering algorithms.

$5.$ Integration and Testing:

Integrate the NLP$,$ rule$-$based system, and ML models into a single system. Test the system on a sample dataset to ensure it accurately identifies compliance and non$-$compliance issues in the regulatory paperwork, permits, and compliance reports.

$6.$ Continuous Improvement:

Continuously improve the system by incorporating user feedback, updating the regulations, and refining the NLP and ML models.can you do this methodology for me as an example at any mine