A smart grid is a modern electricity system enabling bidirectional flow of communication that works on the notion demand response. The stability prediction becomes necessary to make it more reliable and improve efficiency consistency electrical supply. Due sensor or failures, missing input data can often occur. It worth noting there has been no work conducted predict variables in past. Thus, this paper aims develop an enhanced forecasting model using neural networks handle data. Four case...

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

This paper is focused on the design of a complex fractional-order differentiator and integrator for order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha+j\beta$</tex> . Furthermore, these differentiators integrators' practical realization, an approximation using curve fitting-based iterative algorithm proposed. The various fractional PID controllers proposed has been presented. simulation study results show that designed with orders...

This paper focuses on developing a weather prediction model to predict temperature and humidity. Further, classification is also extended the condition using expected model's output. The proposed hybrid can humidity forecast future conditions. models are created neural networks k-nearest neighbors, respectively. results have shown best ability for both output variables (temperature humidity) with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML"...

The principal focus of this paper is to develop a prediction model predict the turbidity beach waves. developed using nonlinear autoregressive neural network three input parameters: water temperature, wave height, and period. predicted without installing any additional sensors. performance evaluated on beaches in Chicago Park's district. proposed showed better tracking ability for all considered beaches. R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML"...

This paper has introduced an improved salp swarm algorithm based on the inertia weight concept. The concept will help achieve better convergence obtaining a more accurate solution during both exploration and exploitation phases. Further, comparing algorithm's performance with different weights traditional is also made various benchmark functions. numerical results for best, worst, mean, standard deviation values show that proposed shown best than compared algorithms. actual most of test...

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

This paper focuses on developing a smart grid stability prediction model to handle the missing input variables. implements two models. The first is feedforward neural network designed predict with complete data. second includes novel work carried out, wherein variables are considered, which predicted using sub-neural network. network's obtained testing output taken run primary Levenberg-Marquardt algorithm used train both transfer functions tansig for hidden layer and purelin layer....

This paper focuses on developing an exponentially weighted sine cosine algorithm that helps achieve faster convergence and better global optimal solutions. The proposed has been achieved by incorporating exponential weight functions in the position updation equations change during each iteration. Further, a comparison of with traditional technique is made various benchmark functions-selecting these multiple categories such as unimodal, multimodal, composite. optimization results indicated...