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Artificial Intelligence Model for Classification of Sensitivity Data Utilized at Industrial Applications

Every electronic gadget in the structure has the potential to cause ignite, whether through a short circuit or another means which is also the primary cause of fire accidents. Smoke detectors are used to save the lives of causalities during hazards. In the manufacturing industry of smoke detectors, around 12 percent of products are getting failed due to the parameters of sensitivity criteria. This paper provides an approach to improve the success rate sensitivity data of the products as an industrial application using a sequential model provided by the tensor flow. Model accuracy and loss were results compared at different epochs. The final artificial intelligence model was developed with a minimum number of layers and requires less computational time. This paper helps the industry to improve product quality and efficiency.