Katib Iyad, Albassam Emad, Sharaf Sanaa A, Ragab Mahmoud
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Sci Rep. 2024 Sep 27;14(1):22293. doi: 10.1038/s41598-024-73876-w.
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.
企业风险管理（ERM）框架传达了重要原则，有助于营造一致的风险管理文化，无论员工更替或行业标准如何。企业管理系统（EMS）正成为确保公司长期成功的热门研究领域。该领域使用了统计模式识别、联邦学习、数据库管理、可视化技术和社交网络等，包括人工智能（AI）、数据科学和统计学。EMS中的风险评估对于有效的企业决策至关重要。人工智能、机器学习（ML）和深度学习（DL）概念的最新进展推动了针对EMS的有效风险评估模型的开发。本期特刊寻求具有开创性的研究文章，展示应用概率和统计在跨学科研究中的应用。本研究为智能企业系统提供了一种基于深度学习的风险评估模型的改进元启发式算法（IMDLRA-SES）。所提供的IMDLRA-SES技术使用特征选择（FS）和DL模型来估计商业风险。IMDLRA-SES技术中使用预处理将原始财务数据转换为可用格式。此外，利用基于对立狮群优化（OLSO）的FS技术来找到最佳特征子集。此外，使用具有概率神经网络（PNN）模型的三叉树种子算法（TTSA）对公司中财务风险的存在与否进行分类。TTSA用作超参数优化器，以提高基于PNN的分类效率。在德国和澳大利亚信用数据集上进行了广泛的实验评估，以说明IMDLRA-SES模型的改进性能。IMDLRA-SES模型的性能验证显示，与现有技术相比，其准确率分别高达95.70%和96.09%。
