Although Scalable deep neural networks (SDNNs) have revolutionized the way many artificial intelligence tasks are performed, they are limited in their ability to deal with time scales in decentralized Linked Data interfaces. This limitation is due to the need of SDNNs to process large amounts of data in an efficient manner and the difficulty associated with decentralizing data in an effective manner. To support these tasks, other methods such as distributed processing, fuzzy logic, and graph theory may be employed to efficiently process the data. Additionally, temporal linear models that are able to take into account future trends and patterns in data may also be beneficial in dealing with time scales in decentralized Linked Data interfaces.
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