The recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's complexity and distributed nature. Therefore, a reliable modelling approach is needed to boost Blockchain-based applications' development and evaluation. While simulation-based solutions have been researched, machine learning (ML) model-based techniques are rarely discussed in conjunction with evaluating blockchain application performance. Our novel research makes use of two ML model-based methods. Firstly, we train a $k$ nearest neighbour ($k$NN) and support vector machine (SVM) to predict blockchain performance using predetermined configuration parameters. Secondly, we employ the salp swarm optimization (SO) ML model which enables the investigation of optimal blockchain configurations for achieving the required performance level. We use rough set theory to enhance SO, hereafter called ISO, which we demonstrate to prove achieving an accurate recommendation of optimal parameter configurations; despite uncertainty. Finally, statistical comparisons indicate that our models have a competitive edge. The $k$NN model outperforms SVM by 5\% and the ISO also demonstrates a reduction of 4\% inaccuracy deviation compared to regular SO.

翻译：区块链技术的最新进展巩固了其作为各领域可行替代方案的地位。然而，由于底层基础设施的复杂性和分布式特性，评估区块链应用的性能可能具有挑战性。因此，需要一种可靠的建模方法以促进基于区块链应用的开发与评估。尽管已有基于仿真的解决方案研究，但基于机器学习模型的技术在结合区块链应用性能评估方面鲜有探讨。我们的创新研究采用了两种基于机器学习模型的方法。首先，我们训练了k最近邻（kNN）和支持向量机（SVM）模型，利用预定配置参数预测区块链性能。其次，我们采用樽海鞘群优化（SO）机器学习模型，该模型能够探索实现所需性能水平的最佳区块链配置。我们利用粗糙集理论对SO进行改进（以下简称ISO），并证明其在不确定性条件下能够准确推荐最优参数配置。最后，统计比较表明我们的模型具有竞争优势：kNN模型性能优于SVM 5%，而ISO相比常规SO将不准确度偏差降低了4%。