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Smart city article details
| Title | Application Of Machine Learning And Iot For Smart Cities |
|---|---|
| ID_Doc | 9931 |
| Authors | Pradhan N.; Singh A.S.; Sagar S.; Singh A.; Elngar A.A. |
| Year | 2021 |
| Published | Machine Learning Approaches for Convergence of IoT and Blockchain |
| DOI | http://dx.doi.org/10.1002/9781119761884.ch5 |
| Abstract | Internet of Things (IoT) is an evolving technology in which numbers of devices are connected to the Internet and to number of associated device. The IoT is large domain in which number of smart devices is connected and individually is connected together and provides information in a specific way, which they can be utilized. IoT consist of phenomenal number of objects of every kind that is from smart microwaves—which naturally cooks food in the specified time, to self-driving vehicles—whose complex sensors identifies the objects in the path. Machine learning (ML) is an innovation today and is the procedure of elimination of human intervention at every possible opportunity. It is enabling the information to learn design independent from anyone else and take autonomous choices without a coder composing another arrangement of codes. Each time the IoT sensors assemble information, there must be somebody at the backend to arrange the information, process them and guarantee data is conveyed back to the gadget for decision making. ML comes to play with its algorithm which helps to incorporate discover information from huge data sets and focus on structure discovery, anticipating classes and qualities, highlight extraction, and the sky is the limit from there. Distinctive ML methods, for example, decision trees, clustering, and neural and Bayesian systems, help the gadgets to recognize designs in various sorts of informational collections originating from different sources and take proper decision based on their investigation. Such difficulties are confronted particularly on account of embedded system. © 2021 Scrivener Publishing LLC. |
| Author Keywords | Bayesian systems; clustering; decision trees; IoT; machine learning |
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