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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Harnessing probabilistic neural network with triple tree seed algorithm-based smart enterprise quantitative risk

Iyad Katib1, Emad Albassam1, Sanaa A Sharaf1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.

Scientific Reports
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an Improved Metaheuristics with Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for smart enterprise systems. The novel approach achieves high accuracy in financial risk assessment, enhancing enterprise decision-making.

Keywords:
ClassificationDeep learningFeature selectionFinancial decisionsMetaheuristicsRisk assessmentSmart enterprise system

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Statistics
  • Enterprise Risk Management

Background:

  • Enterprise Management Systems (EMS) are crucial for long-term business success, integrating AI, data science, and statistics.
  • Effective risk assessment within EMS is vital for informed enterprise decision-making.
  • Advancements in AI, machine learning (ML), and deep learning (DL) are driving the development of sophisticated risk assessment models.

Purpose of the Study:

  • To present an Improved Metaheuristics with a Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for Smart Enterprise Systems.
  • To apply feature selection and deep learning for accurate business risk estimation.
  • To showcase the application of applied probability and statistics in interdisciplinary studies for risk management.

Main Methods:

  • Data preprocessing transforms raw financial data into a usable format.
  • Oppositional Lion Swarm Optimization (OLSO) is employed for feature selection (FS) to identify optimal feature subsets.
  • The Triple Tree Seed Algorithm (TTSA) optimizes a Probabilistic Neural Network (PNN) for classifying financial risks.

Main Results:

  • The IMDLRA-SES technique effectively estimates business risks using feature selection and deep learning models.
  • The TTSA hyperparameter optimization significantly enhances the PNN model's classification efficiency.
  • Experimental evaluations on German and Australian credit datasets demonstrate superior performance over existing methods.

Conclusions:

  • The IMDLRA-SES model offers a robust and accurate solution for financial risk assessment in smart enterprise systems.
  • The integration of advanced metaheuristics and deep learning provides a powerful tool for improving enterprise decision-making.
  • The study validates the effectiveness of applied probability and statistics in developing advanced risk management frameworks.