An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment

As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2022-08, Vol.2022, p.1-10
Main Authors: Venkateswarlu, Yalla, Baskar, K., Wongchai, Anupong, Gauri Shankar, Venkatesh, Paolo Martel Carranza, Christian, Gonzáles, José Luis Arias, Murali Dharan, A. R.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Big Data
Classification
Cloud computing
Data analysis
Data management
Datasets
Deep learning
Design
Economic forecasting
Feature selection
Financial management
Internet of Things
Machine learning
Mathematical models
Outliers (statistics)
Prediction models
Small & medium sized enterprises-SME
Small business
Social factors
Social interactions
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Summary:As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country’s economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.
ISSN:1687-5265
1687-5273
DOI:10.1155/2022/4948947