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This paper considers autonomous environment discovery for energy harvesting Internet of things (IoT) applications. A self-sustainable mobile micro-robot explores an unknown environment to collect data under energy constraints. The data are fed into a machine- learning model to construct an energy harvesting map of the environment, which will then be used by IoT devices for self-sustainable operations...
Wireless indoor localization is a key technology for the future Internet of things (IoT) paradigm. In this paper, we perform an experimental comparative study of machine learning-based localization schemes, such as k-nearest neighbor (k-NN) and variants of support vector machine (SVM), based on the received signal strength (RSS) measurements of the ambient frequency modulation (FM) and digital video...
Multi-label learning is a hot spot in machine learning and data mining. The multi-label learning model can predict one or more labels for a test instance. PT5 is a effective method to solving multi-label learning problem, it is critical to set an optimal threshold in this method. More error labels will be predicted if a lower threshold was set, and the labels will be predicted not entire if a big...
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