Geodesic
Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT
Computer Science and Information Systems, Tome 18 (2021) no. 2.

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For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.
Keywords: machine learning, big data analysis, extreme learning machine, IoT, security, and privacy 
@article{CSIS_2021_18_2_a12,
     author = {Sushil Kumar Singh and Jeonghun Cha and Tae Woo Kim and Jong Hyuk Park},
     title = {Machine {Learning} {Based} {Distributed} {Big} {Data} {Analysis} {Framework} for {Next} {Generation} {Web} in {IoT}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {18},
     number = {2},
     year = {2021},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2021_18_2_a12/}
}
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Sushil Kumar Singh; Jeonghun Cha; Tae Woo Kim; Jong Hyuk Park. Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. Computer Science and Information Systems, Tome 18 (2021) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2021_18_2_a12/