A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg–Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model’s performance is evaluated on a four-node star network and is measured in terms of the MSE and R${}^2$ values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability.

This paper focuses on developing a prediction model to predict heart stroke using the parameters, namely, age, hypertension, previous heart disease status, average body glucose level, BMI, and smoking status. The prediction model is developed using a support vector machine (SVM) algorithm. Further, the SVM algorithm with various decision boundaries like linear, quadratic, and cubic are also produced. The performance prediction results show that the linear and quadratic SVM has performed better in predicting the heart stoke with greater accuracy values. This is true for both the male and female databases during training and testing.

This paper is focused on the design of a complex fractional-order differentiator and integrator for the order $\alpha+j\beta$ . Furthermore, for these differentiators and integrators' practical realization, an approximation using a curve fitting-based iterative algorithm is proposed. The design of various complex fractional PID controllers using the proposed differentiator and integrator has been presented. The simulation study results show that the designed controllers with complex orders have produced a steady behavior equivalent to fractional controllers' behavior. The step response characteristics on the process plant also show that the designed controllers performed well in controlling the process.

This paper focuses on developing a weather prediction model to predict temperature and humidity. Further, a classification model is also extended to predict the weather condition using the expected model’s output. The proposed hybrid model can predict the temperature and humidity and forecast future weather conditions. The prediction and classification models are created using neural networks and k-nearest neighbors, respectively. The prediction model’s results have shown the best ability for both the output variables (temperature and humidity) with R ² values close to one and MSE values close to zero. Further, the classification model’s results also showed better execution in classifying the weather conditions with the highest accuracy values.

The principal focus of this paper is to develop a prediction model to predict the turbidity of beach waves. The prediction model is developed using a nonlinear autoregressive neural network model using three input parameters: water temperature, wave height, and wave period. The beach wave turbidity is predicted without installing any additional sensors. The performance of the developed model is evaluated on three beaches in Chicago Park’s district. The proposed model performance showed better tracking ability for all the three considered beaches. The R ² and mean square errors MSE also confirm the best prediction model’s performance for both training and testing.

This paper focuses on the development of a hybrid algorithm using moth-flame and particle swarm optimization. The main aim of the proposed algorithm is to improve the conventional moth-flame algorithm's performance for obtaining the faster convergence speed and best global optimal solution. Also, the proposed algorithm is to enhance the performance of particle swarm on getting global searchability. The proposed hybrid optimization algorithm's performance is evaluated on solving the well-known benchmark functions. The results show that the proposed hybrid optimization performed better than conventional moth-flame and particle swarm algorithms to obtain the best optimal solution with faster convergence.