Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control
AbstractProactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM
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Adeleke, J.A.; Moodley, D.; Rens, G.; Adewumi, A.O. Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control. Sensors 2017, 17, 807.
Adeleke JA, Moodley D, Rens G, Adewumi AO. Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control. Sensors. 2017; 17(4):807.Chicago/Turabian Style
Adeleke, Jude A.; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi O. 2017. "Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control." Sensors 17, no. 4: 807.
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