article · Computational Intelligence and Neuroscience
Accurately predicting financial distress in small and medium-sized enterprises is vital for assessing economic resilience, but practical applications often suffer from sub-optimal classification efficiency and accuracy. To address this, a financial crisis prediction framework named OALOFS-MLC has been designed for big data environments. The system uses Hadoop MapReduce to manage large-scale financial data efficiently. It introduces an oppositional ant lion optimiser algorithm to select the most relevant features, reducing data complexity and boosting predictive precision. For the classification and grading stage, the framework applies a deep random vector functional links network. When tested against a baseline dataset, this combined approach demonstrated superior performance compared to existing benchmark techniques, offering a robust method to anticipate business failures in data-rich financial settings.
Small and medium-sized enterprises form the backbone of national economies, making early warning systems for financial insolvency essential. By pairing big data infrastructure with advanced optimisation and deep learning, organisations can process vast financial records more effectively. This improves the accuracy of crisis forecasting, helping businesses and institutions recognise distress signals before failure occurs.
The model is aimed at financial crisis forecasting for small and medium-sized enterprises, which could serve financial institutions, credit rating agencies, and economic analysts seeking early insolvency detection. Operating on Hadoop MapReduce, it is built to handle enterprise-level big data. However, the system appears to be at an early stage of development, having been validated only against a baseline research dataset rather than tested in live commercial environments.
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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.
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DOI: 10.1155/2022/4948947
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