They were created to imitate neural networks within the human brain.
Artificial neural networks use a machine learning algorithm based on a model of neurons within the human brain. ANNs are comprised of layers of nodes which have three layers. These consist of an input layer, one or more hidden layers, and an output layer.
This is all done with large amounts of information presented in datasets. This comes from the data collected during the crops’ growing season, along with using the appropriate software to enter the statistics. Researchers use the ANN tools to support agricultural production by making it more efficient to grow and harvest crops.
An example of using an artificial neural network to predict disease was published this year in the journalIn the study, ANN was used to predict and detect Parkinson’s disease in patients, along with tracking its progression, using data from nocturnal breathing. Researchers trained the ANN using data from 7,671 participants who had PD. The ANN model could output an estimated PD severity and progression as well, based on its datasets.
In other words, studying and analyzing the structure of some types of ANN doesn’t provide full insight on its functionality and how it arrives at its predictions. Hence the term "black box."There are numerous types of artificial neural network models used in supervised and unsupervised learning. These differ in terms of complexity, use cases, and structure.
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