Due to unpredictability of climatic conditions across the world, early fire forecasting has become more challenging and critical for many oil and gas sectors. It is extremely hard for anyone to predict fires with any degree of certainty, especially in the gas or oil sectors. Until now, the models in use have not been adequate. However, this is critical in order to maintain workers and property safe. As a result, this research work investigates the different approaches available for fire hazard assessment and prediction in order to deal with fire dangers. Also, this research work presents the statistical machine learning methods to detect fire accidents in petroleum industries based on risk index models and risk assessment parameters by performing a statistical process. Moreover, this research work develops a statistical machine learning method to enhance the accuracy in predicting the fire occurrence. Finally, the proposed algorithm is measured by utilizing the performance metrics such as accuracy, proposed risk index, and sensitivity.
Communication is an essential thing for any work to be done on desired condition. There are several ways are there to communicate between a person to a machine. Switches are made as primary communication medium to do a specific task by a machine. The advancement of technologies introduced a remote switch for operation. The advancement is still continuing with voice recognition, hand gesture movement and mind reading models. Recently machine to machine communication was introduced to communicate their status between another machine or a system for implementing smart work on its own intelligence. The paper introduces a smart communication between vehicles to traffic signals and vehicle to vehicle using Li-Fi (Light Fidelity) technology for sharing necessary information to the nearby vehicle. The paper also describes the limitations and challenges in the Li-Fi technology for further communication improvement.
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