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

Autor: Venkateswarlu Y; Department of Computer Science and Engineering, BVC College of Engineering, Rajahmundry, East Godavari District, Andhra Pradesh, India., Baskar K; Kongunadu College of Engineering and Technology, Thottiam, Tamilnadu, India., Wongchai A; Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai, Thailand., Gauri Shankar V; Manipal University Jaipur, Jaipur, Rajasthan, India., Paolo Martel Carranza C; Universidad de Huánuco, Huánuco, Peru., Gonzáles JLA; Pontificia Universidad Católica Del Peru, San Miguel, Peru., Murali Dharan AR; Debre Berhan University, Debre Berhan, Ethiopia.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Aug 16; Vol. 2022, pp. 4948947. Date of Electronic Publication: 2022 Aug 16 (Print Publication: 2022).
DOI: 10.1155/2022/4948947
Abstrakt: 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.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Yalla Venkateswarlu et al.)
Databáze: MEDLINE
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