A significant problem for industrial management has always been the frequency of workplace fatalities and injuries. From production plants to offices to construction sites and beyond, every workplace has safety threats and hazards. To recognize certain objects, circumstances, practices, etc., that can cause harm, especially to individuals, a detailed look at the workplace becomes crucial. It is important to evaluate and determine how severe and likely the danger is after identification is made. The range of cumulative challenges, partially linked to technology developments with rising expectations, has to be broadly considered. Currently, it needs systematic risk estimation, enhancement of past lessons in learning, and the concept of appropriate data processing techniques to be combined with adequate capacity to cope with unforeseen events and provide the right support to enable risk management. Therefore, this paper suggests a risk management methodology focused on artificial intelligence (AI). A Deep Neural Network for Workplace Risk Estimation (DNN-WRE) model is explicitly generated and trained to predict the working environment's risks. The DNN-WRE is evaluated by a common risk prediction seen in the workplace, i.e., Musculoskeletal Disorders (MDs). Furthermore, this paper re-tuned the transfer learning network ResNet 18 to predict MDs. The training and validation of the proposed DNN-WRE have been observed with the highest prediction accuracy of 93.86% compared with the pre-trained ResNet 18 model.

工业管理的一个重要问题一直是工作场所死亡和受伤的频率。从生产工厂到办公室，再到建筑工地，甚至其他地方，每个工作场所都面临安全威胁和危险。要认识到可能对人尤其是个人造成伤害的某些对象，情况，做法等，对工作场所进行详细了解就变得至关重要。重要的是，评估并确定识别后危险的严重程度和可能性。累积挑战的范围，部分与期望值不断提高的技术发展有关，必须予以广泛考虑。目前，它需要系统地进行风险评估，增加以往的学习经验，适当的数据处理技术的概念与足够的能力相结合来应对不可预见的事件，并提供适当的支持以进行风险管理。因此，本文提出了一种针对人工智能（AI）的风险管理方法。明确生成了用于工作场所风险估计的深度神经网络（DNN-WRE）模型，并对其进行了训练，以预测工作环境的风险。DNN-WRE通过工作场所常见的风险预测（即肌肉骨骼疾病（MDs））进行评估。此外，本文重新调整了转移学习网络ResNet 18以预测MD。与预训练的ResNet 18模型相比，已观察到对拟议DNN-WRE的训练和验证具有93.86％的最高预测准确性。因此，本文提出了一种针对人工智能（AI）的风险管理方法。明确生成了用于工作场所风险估计的深度神经网络（DNN-WRE）模型，并对其进行了训练，以预测工作环境的风险。DNN-WRE通过工作场所常见的风险预测（即肌肉骨骼疾病（MDs））进行评估。此外，本文重新调整了转移学习网络ResNet 18以预测MD。与预训练的ResNet 18模型相比，已观察到对拟议DNN-WRE的训练和验证具有93.86％的最高预测准确性。因此，本文提出了一种针对人工智能（AI）的风险管理方法。明确生成了用于工作场所风险估计的深度神经网络（DNN-WRE）模型，并对其进行了训练，以预测工作环境的风险。DNN-WRE通过工作场所常见的风险预测（即肌肉骨骼疾病（MDs））进行评估。此外，本文重新调整了转移学习网络ResNet 18以预测MD。与预训练的ResNet 18模型相比，已观察到对拟议DNN-WRE的训练和验证具有93.86％的最高预测准确性。明确生成了用于工作场所风险估计的深度神经网络（DNN-WRE）模型，并对其进行了训练，以预测工作环境的风险。DNN-WRE通过工作场所常见的风险预测（即肌肉骨骼疾病（MDs））进行评估。此外，本文重新调整了转移学习网络ResNet 18以预测MD。与预训练的ResNet 18模型相比，已观察到对拟议DNN-WRE的训练和验证具有93.86％的最高预测准确性。明确生成了用于工作场所风险估计的深度神经网络（DNN-WRE）模型，并对其进行了训练，以预测工作环境的风险。DNN-WRE通过工作场所常见的风险预测（即肌肉骨骼疾病（MDs））进行评估。此外，本文重新调整了转移学习网络ResNet 18以预测MD。与预训练的ResNet 18模型相比，已观察到对拟议DNN-WRE的训练和验证具有93.86％的最高预测准确性。