Читать книгу Cyberphysical Smart Cities Infrastructures. Optimal Operation and Intelligent Decision Making онлайн

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The process of non‐annotation helps to work with data that lacks classes because typical machine learning algorithms fail to detect non‐annotation classes. Furthermore, sensing to learn helps new algorithms predict information that is completely lacking from the dataset itself. These advancements lead us closer to (or are part of) ZSL, which is critical for the advancement of Smart Cities.

The second promise is one‐shot learning, in which each epoch in a training phase has only one sample per each class that is taken by a DL algorithm or a combination of neural networks [7].

The third promises but not the least one is few (k)‐shot learning in which each epoch in a training phase has only few (k) samples per each class that are taken by a DL algorithm or a combination of neural networks [7].

Decision‐making problems are becoming challenging issue in smart cities where not only the problem itself but also other relevant problems in other aspects of smart cities need to be analyzed. Additionally, decision makers must depict the consequence of the decision they are going to make. Thus, decision‐making systems are needed in smart cities in which the systems take care of all issues within the connected networks and only some limited information is taken that would be enough make an optimum decision. In this section, we highlight the challenging decision making problems and solutions.