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Nowadays, digital businesses with diverse deployment models such as cloud, mobile and edge devices for the internet of things will impact traffic, in both volume and dynamicity, at unprecedented rates. Moreover, due to the recent advances in optical networks and systems, the complexity of provisioning lightpaths is growing dramatically. Hence, optical network operators are forced to change their insight and move toward intent-based and self-driven networking, to cost-efficiently accommodate these challenging requirements. In this regard, knowledge-defined networking (KDN) promises to play a paramount role in realizing flexible and self-driven optical networks. In this work, we focus on one of the key aspects in this environment, i.e., prediction of quality of service for unestablished lightpaths. KDN is a solution that introduces machine learning techniques into the control plane of the network, to cope with inevitable complexities that arise in enabling network to operate autonomously and faster. For this, five machine learning models are evaluated for the classification and regression approaches. Multilayer perceptron, radial basis function and generalized regression neural network (GRNN) models are used for both of the regression and classification approaches, while support-vector machine and probabilistic neural network (PNN) models are used only for the classification scenario. Also, to discard the redundant features (among the considered experimental features) in the classification approach, input features are selected using the analysis of variance (ANOVA) test. The proposed models can accelerate and handle a significant part of operations in the closed-loop architecture of knowledge-defined optical networks, as a paradigm for designing self-driven optical networks. The best accuracies of quality of transmission prediction (classification approach) and optical signal-to-noise ratio estimation (regression approach) are achieved using PNN (with average accuracy of 99.6 ± 0.5%) and GRNN (with R-squared value of 0.957), respectively. [ABSTRACT FROM AUTHOR]

During the gas tungsten arc welding of nickel-based superalloys, the secondary phases such as Laves and carbides are formed in final stage of solidification. But, other phases such as γ″ and δ phases can precipitate in the microstructure, during aging at high temperatures. However, it is possible to minimize the formation of the Nb-rich Laves phases and therefore reduce the possibility of solidification cracking by adopting the appropriate welding conditions. This paper aims at the automatic microstructurally characterizing the kinetics of phase transformations on an Nb-base alloy, thermally aged at 650 and 950 °C for 10, 100 and 200 h, through backscattered ultrasound signals at frequency of 4 MHz and dual tree complex wavelet transform (DTCWT)-based feature extraction technique. The feature set comprises of statistical attributes (such as variance, skewness and kurtosis) extracted from the complex wavelet coefficients which are obtained using the DTCWT decomposition of a backscattered ultrasound signal. Also, the performance of the proposed feature extraction technique is compared with the conventional discrete wavelet transform. Finally, these features are fed to the probabilistic neural network (PNN) and radial basis function classifiers to automatic microstructural classification. The training process of these networks depends on the selection of the smoothing parameter of the networks' activation function in a hidden layer. In this article, we introduce the application of the Bees Algorithm to the automatic adaptation of smoothing parameters. The proposed feature extraction technique coupled with the optimized PNN yielded the highest average accuracy of 96 and 83 %, respectively, for thermal aging at 650 and 950 °C. Thus, the proposed processing system provides high reliability to be used for microstructure characterization through ultrasound signals. [ABSTRACT FROM AUTHOR]