The current paper concentrates on the data of food safety sensors on four key environmental parameters, including the temperature, humidity, microbial load, and transport time. The parameters were observed with histograms of 1000 samples, thus providing the information about the refrigeration controls, risks of microbial contamination and hazards in transit. The results potentially will be used to design reinforcement learning (RL) tools to plan dynamic inspection and routing to enhance food safety management.
Turn pages by dragging a corner, clicking a page, or with the ← → keys.
Loading article…
Open the PDF instead
DOI: https://doi.org/10.63328/IJRDES-V7CIP4
OpenYou've reached the end of this article.
Lung cancer is accountable for more deaths each year than all of the other leading cancers - breast,...
The growing rates of air pollution are both highly hazardous to the human environment and health, an...
Leukaemia, a malignant disorder of the blood and bone marrow, requires early and accurate detection ...
Multilevel inverters (MLIs) have gained an increasingly high profile in the area of power electronic...
