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Additionally, due to the huge quantity of data, researchers used DL algorithms especially transfer learning and meta‐learning [7] and some other famous machine learning techniques to learn within reinforcement learning like Q‐learning 31–33 for generating smart systems [34, 35].
2.3.2.1 Big Data Algorithms and Challenges
Due to the big data revolution, the enormous volume of high‐performance computations are unavoidable in such smart cities. Thus, big data algorithms are getting one of the important functioning pieces of smart cities.
2.3.2.2 Machine Learning Process and Challenges
The learning process based on given input where data are not sufficient does not produce reliable and robust results [7]. Machine learning algorithms get stuck in local minimum or maximum easily due to a large amount of data that leads to a problem called overfitting [7]. Furthermore, machine learning algorithms fail to learn positions and states (classes) when the number occurrence in the data is far less than others. For instance, within the smart healthcare domain, when capturing data, the probability of having all desired classes looks low, and the number of rare diseases would be insufficient for learning.