摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据NewsRx编辑从中国郑州发回的新闻报道，该研究称：“我们在膜接触器中进行了去除水溶液中有机物的传质模型和计算，分别采用计算流体力学(CFD)和人工智能(AI)两种方法对分离过程进行了建模。”新闻记者从河南财经大学的研究中获得了一句话：“对于人工智能，我们探索了基于R和Z的核岭回归、高斯过程回归和泊松回归三种不同的回归模型来预测A组分C的浓度。”摘要:对超参数整定过程emp loys萤火虫群优化算法(GSO)进行了研究。结果表明,所用模型的有效性。高斯过程回归得到了值得注意的2s核为0.99791,均方误差为3.9666 x 101(mol/m3),AARD%为4.52000 x 10-1.核岭回归精度稍差,2值为0.97865.RMSE为1.2446 x 102(mol/m3)，NARD%为2.63808.泊松回归提供了相当好的性能，得出了0.95509的2得分，RMSE为1.8011 x 102(mol/m3)，ARD%为4.28969.
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ar tificial Intelligence. According to news reporting from Zhengzhou, People’s Repu blic of China, by NewsRx editors, the research stated, “We have carried out mode ling and computation of mass transfer in a membrane contactor for removal of org anic compounds from aqueous solutions. Both computational fluid dynamics (CFD) a nd Artificial Intelligence (AI) methods were utilized for modeling separation pr ocess.” The news correspondents obtained a quote from the research from Henan Finance Un iversity, “For the AI, we explored the application of three distinct regression models, namely Kernel Ridge Regression, Gaussian Process Regression, and Poisson Regression to predict the concentration of a component, C, based on r and z. To enhance the performance of these models, the hyper-parameter tuning process emp loys Glowworm Swarm Optimization (GSO). The findings illustrate the effectivenes s of the utilized models. Gaussian Process Regression achieves a noteworthy R2 s core of 0.99791, with a RMSE of 3.9666 x 101(mol/m3) and an AARD% of 4.52000 x 10-1. Kernel Ridge Regression, while slightly less accurate, achiev es a commendable R2 value of 0.97865, with an RMSE of 1.2446 x 102(mol/m3) and a n AARD% of 2.63808. Poisson Regression offers a respectable perfor mance, yielding an R2 score of 0.95509, along with an RMSE of 1.8011 x 102(mol/m 3) and an AARD% of 4.28969.”
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