Climate Change and Food Security Management–based Urban Health Care Systems Using Artificial Intelligence Techniques

Global climate change, caused by human activity, has serious effects for Earth and its inhabitants. One of the implications is already obvious over the world, and it is related to food security. A lack of food security has major consequences for health, particularly among poor groups that rely heavily on a nutritious diet for a good life. The growth of machine learning (ML) algorithms provides the computer power to handle huge amounts of data. This research proposes a novel technique in climate change analysis–based food security management in urban healthcare system using the machine learning model. Here, the urban region climate analysis–based data has been collected and processed for climate change detection using the spatial fuzzy encoder with radial belief neural networks. Then, the food security analysis has been carried out for health security management. Experimental analysis has been carried out in terms of training accuracy, random precision, mean square error, recall, and F1 score for various climate analysis datasets. Proposed techniques attained are training accuracy 98%, F1 score of 93%, random precision of 90%, recall of 94%, and MSE of 92%.