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Abstract With increasing demand for a renewable source of energy and the rapid development of smart cities and cluster computing, wind energy is gaining widespread significance on a global basis. There has been the unprecedented increase in annual installation of wind turbines reaching 60 GW for the first time in history from 51.7 GW of wind energy installation in 2014. Most of the high-power installations utilized double-fed induction generators (DFIG) due to low power losses, which reduced the cost of power converters required in the circuit. Nevertheless, natural factors, such as varying wind speed, influence the output of generators which results in the unsteady generation of power thus affecting the stability of the system. A neural network-based DFIG integrated with super capacitor energy storage system (SCESS) is proposed in this paper. Modeling and simulation have been carried out in MATLAB/Simulink and the observations indicate the significant improvement in the stability of the system. [ABSTRACT FROM AUTHOR]

In this paper, we show a deep learning model for bot detection, named as DeBot, for industrial network traffic. DeBot uses a novel Cascade Forward Back Propagation Neural Network (CFBPNN) model with a subset of features using the Correlation-based Feature Selection (CFS) technique. A time series-based Nonlinear Auto-regressive Network with eXogenous inputs (NARX) technique analyzes the factors having a higher impact on the target variable and predict the behavioral pattern. To the best of our knowledge, we pioneer the use of optimal feature selection and integration with the cascading model of deep learning in bot detection of IIoTs. We conduct a thorough set of experiments on five popular bot datasets: NF-UNSW-NB15, NF-ToN-IoT, NF-BoT-IoT, NF-CSE-CIC-IDS2018, and ToN-IoT-Windows. We compare CFBPNN with other existing neural network models. We observe that CFBPNN in DeBot shows 100% accuracy in all the datasets with subset evaluation and obtains optimum F1-score and zero precision. [Display omitted] • We address the multi-layered network model with IIoT features to enable bot attack detection. • Our DeBot model evaluates the subset with optimal features with high impact on attack class and predict the risk factor. • We pioneer the implementation of time series-based Nonlinear Auto-Regressive eXogenous (NARX) model to predict the risk in the specified time gap. • We introduce a new cascading model with recurrent feature to preserve the relationship between input and output variables. [ABSTRACT FROM AUTHOR]

• Improve speed of learning process. • Decreases the computational time. • To reduce the complexity of a model and makes it simple and easier to interpret. • To improve the accuracy of a model and reduce over fitting. The Neural Network has a significant impact in developing predictive models in a wide range of applications. In this paper, a neuro-fuzzy prediction model is developed depending on improving the performance of the traditional artificial neural networks using Adaptive Momentum Optimizer. This optimizer simulates the behavior of the International Trade Analysis in the agriculture industry, and this method is used to determine the optimal parameters of artificial neural networks. The proposed model is compared with the existing models such as Support Vector Machine, Random Forest, Decision Tree and traditional Artificial Neural Network models. To examine the forecasting performance of the proposed approach, agriculture datasets is used. The performance of the models was assessed using different performance evaluation criteria and the empirical results show that the back propagation neural network with Adam optimizer attains favorable prediction accuracy of 96.78%, and a better convergence rate. Compared to other benchmark algorithms, the proposed algorithm performs better, and the result validates the effectiveness of the back propagation with Adam optimizer for Natural Language Processing. Image, graphical abstract [ABSTRACT FROM AUTHOR]