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- W4293263047 abstract "Nowadays, a vast amount of data is considered for statistical analytical tools. Big Data analytics have generated an exponentially increasing variety of medical data from IoT devices such as drones. Today, the epidemiological data collection pattern in disease monitoring is gathering with drones. This includes organized, semi-structured and unstructured data, leading to drone data trawling. This chapter provides big data to increase healthcare quality by applying effective machine learning (ML) strategies for segregation and drones-compilation data. The process intends to resolve the drone's use of trawled data and provide real-time analyses of the data. The ML algorithm implemented the Apache Spark core for smoother segregated streaming from various crowd sources. Three drones were configured for the experiment. The principle will increase the accuracy of health care forecasts based on the investigation findings. This result showed that relative to hierarchical clustering and density-dependent clustering, the K-means cluster has the highest smoothness rate of real-time segregating data." @default.
- W4293263047 created "2022-08-27" @default.
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- W4293263047 date "2022-08-24" @default.
- W4293263047 modified "2023-10-18" @default.
- W4293263047 title "Clustering crowdsourced healthcare data from drones using Big Data analytics" @default.
- W4293263047 doi "https://doi.org/10.1049/pbse016e_ch6" @default.
- W4293263047 hasPublicationYear "2022" @default.
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