Leveraging DNA-Based Computing to Improve the Performance of Artificial Neural Networks in Smart Manufacturing

Bioinspired computing methods, such as Artificial Neural Networks (ANNs), play a significant role in machine learning. This is particularly evident in smart manufacturing, where ANNs and their derivatives, like deep learning, are widely used for pattern recognition and adaptive control. However, ANNs sometimes fail to achieve the desired results, especially when working with small datasets. To address this limitation, this article presents the effectiveness of DNA-Based Computing (DBC) as a complementary approach. DBC is an innovative machine learning method rooted in the central dogma of molecular biology that deals with the genetic information of DNA/RNA to protein. In this article, two machine learning approaches are considered. In the first approach, an ANN was trained and tested using time series datasets driven by long and short windows, with features extracted from the time domain. Each long-window-driven dataset contained approximately 150 data points, while each short-window-driven dataset had approximately 10 data points. The results showed that the ANN performed well for long-window-driven datasets. However, its performance declined significantly in the case of short-window-driven datasets. In the last approach, a hybrid model was developed by integrating DBC with the ANN. In this case, the features were first extracted using DBC. The extracted features were used to train and test the ANN. This hybrid approach demonstrated robust performance for both long- and short-window-driven datasets. The ability of DBC to overcome the ANN’s limitations with short-window-driven datasets underscores its potential as a pragmatic machine learning solution for developing more effective smart manufacturing systems, such as digital twins.