The generation of electrospun nanofibrous with controlled size, shape, and spatial orientation is crucial for the development of biomedical and electronic devices. Aligned nanofibers are advantageous over random nanofibers because control of the spatial orientation can improve electrical and optical properties and play an important role in tissue engineering applications, impacting the mechanical and biological properties of the scaffold. Therefore, different machine learning models have been developed to predict the optimal production of electrospun-aligned poly(vinyl alcohol) nanofibers. The database was obtained by multiple assays using the airgap electrospinning setup and varying the voltage, the distance between the tip and collector, and polymer concentration. Binary classification models were developed, which can predict the production or not of aligned nanofibers. In addition, regression models have been developed to predict the orientation, angle, and diameter of the nanofibers when there is a production of nanofibers. A convolutional neural network has also been developed. It was concluded that for the binary classification, the artificial neural network performs better predictions obtaining an accuracy equal to 0.94, and for the validation set, an accuracy equal to 0.90 and an F1-score equal to 0.87 were obtained.

中文翻译：

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数据驱动的机器学习方法，用于对生产进行建模并预测对齐电纺纳米纤维的特性

具有受控尺寸、形状和空间取向的电纺纳米纤维的产生对于生物医学和电子设备的开发至关重要。对齐纳米纤维比随机纳米纤维更有优势，因为空间取向的控制可以改善电学和光学性能，并在组织工程应用中发挥重要作用，影响支架的机械和生物性能。因此，已经开发了不同的机器学习模型来预测静电纺丝排列聚（乙烯醇）纳米纤维的最佳生产。该数据库是通过使用气隙静电纺丝装置并改变电压、尖端和收集器之间的距离以及聚合物浓度的多次测定获得的。开发了二元分类模型，可以预测排列纳米纤维的生产与否。此外，还开发了回归模型来预测生产纳米纤维时纳米纤维的方向、角度和直径。还开发了卷积神经网络。结论是，对于二元分类，人工神经网络执行更好的预测，获得等于 0.94 的准确度，而对于验证集，获得等于 0.90 的准确度和等于 0.87 的 F1 分数。