Articlegold2024

Harnessing probabilistic neural network with triple tree seed algorithm-based smart enterprise quantitative risk management framework

Iyad A. Katib · Emad Albassam · Sanaa Abdullah Sharaf · Mahmoud Ragab

10 citations0/0 files storedScientific Reports

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Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Evaluation and Optimization Models

Abstract

Enterprise risk management (ERM) frameworks convey vital principles that help create a consistent risk management culture, irrespective of employee turnover or industry standards. Enterprise Management System (EMS) are becoming a popular research area for assuring a company's long-term success. Statistical pattern recognition, federated learning, database administration, visualization technology, and social networking are all used in this field, which includes artificial intelligence (AI), data science, and statistics. Risk assessment in EMS is critical for enterprise decision-making to be effective. Recent advancements in AI, machine learning (ML), and deep learning (DL) concepts have enabled the development of effective risk assessment models for EMS. This special issue seeks groundbreaking research articles that showcase the application of applied probability and statistics to interdisciplinary studies. This study offers Improved Metaheuristics with a Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for Smart Enterprise Systems. Using feature selection (FS) and DL models, the provided IMDLRA-SES technique estimates business risks. Preprocessing is used in the IMDLRA-SES technique to change the original financial data into a usable format. In addition, an FS technique based on oppositional lion swarm optimization (OLSO) is utilized to find the best subset of features. In addition, the presence or absence of financial hazards in firms is classified using the triple tree seed algorithm (TTSA) with a probabilistic neural network (PNN) model. The TTSA is used as a hyperparameter optimizer to improve the efficiency of the PNN-based categorization. An extensive set of experimental evaluations is performed on German and Australian credit datasets to illustrate the IMDLRA-SES model's improved performance. The performance validation of the IMDLRA-SES model portrayed a superior accuracy value of 95.70% and 96.09% over existing techniques.

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References

2021The Impact of Financial Enterprises’ Excessive Financialization Risk Assessment for Risk Control based on Data Mining and Machine Learning 2024Combining intra-risk and contagion risk for enterprise bankruptcy prediction using graph neural networks 2023Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation 2021Enterprise Risk Assessment Based on Machine Learning 2019Automatic Information System of Risk Assessment for Agricultural Enterprises of Ukraine 2016Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management 2021The implementation of leisure tourism enterprise management system based on deep learning

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