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- W4313494474 abstract "Abstract Big data has been utilized and attracted various researchers due to the phenomenal increase in computational application which has developed an overwhelming flow of data. Further, with an expeditious blooming of emerging applications such as social media applications, semantic Web, and bioinformatics applications, data heterogeneity is increasing swiftly. Accordingly, a variety of data needs to be executed with less high accuracy and less. However, effective data analysis and processing of large-scale data are compelling which is considered a critical challenge in the current scenario. To overcome these issues, various techniques have been developed and executed but still, it is significant to improve in accuracy. The current study proposed a hybrid technique of BiLSTM-SAE has been proposed for business big data analytics. Bidirectional LSTM is considered as an advanced version of the conventional LSTM approach. The performance comparison of the proposed method BiLSTM-SAE with existing Random forest-RF has been processed. The final result reported that the proposed method BiLSTM-SAE had been procured with better accuracy of 0.836. Moreover, the training and validation accuracy and loss on different performance metrics have been studied and conducted in the research." @default.
- W4313494474 created "2023-01-06" @default.
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- W4313494474 date "2023-01-04" @default.
- W4313494474 modified "2023-10-17" @default.
- W4313494474 title "BiLSTM_SAE:A Hybrid Deep Learning Framework for Predictive Data Analytics System in Traffic Modeling" @default.
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- W4313494474 doi "https://doi.org/10.21203/rs.3.rs-2422617/v1" @default.
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