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Automatic Anonymising and Desensitising Of Data 

Of the vast amount of information on the Internet, most is plain text.

‘Industry 4.0’, the name given to the emerging use of a suite of technologies across many industry sectors, is revolutionising industrial operations in workplaces.

This study aims to explore the possibility of sharing existing knowledge with regards to creating an industrial database for these prevalent hazards within the cement industry, which will incorporate the high risk activities

As recent developments in text mining, natural language processing and machine learning are becoming more mainstream, the task of generating data driven insights and learning from health and safety operational intelligence, particularly from

Organisations generate a wealth of routine health and safety data as part of day-to-day operations that provide a useful proxy measure of how well they manage their health and safety risks as an organisation.

Much industry guidance has been published over recent years, specific to different industry contexts, on how to better use leading indicators of health and safety performance to support assessments of the adequacy of workplace risk control m

Mass manufactured products are used by both consumers (public) and industrial users for work activities.

Building Information Modelling (BIM) supports the digitisation of asset build projects and uses information relating to the asset of interest to build a three dimensional model with supporting intelligent, structured data attached. 

Across the process industries, both on and offshore, a key focus is upon maintaining primary containment. Primary containment refers to keeping flammable and toxic materials inside plant items such as vessels, tanks and pipework.

Diving is known to be a high risk activity where the worker is in an environment that is hostile to normal human existence and requires a life support system and knowledge of physiological limitations to be able to carry out