Government College of Engineering, Kalahandi

About: Government College of Engineering, Kalahandi is a based out in . It is known for research contribution in the topics: Taguchi methods & Compressive strength. The organization has 74 authors who have published 112 publications receiving 362 citations. The organization is also known as: Government College of Engineering Kalahandi.

Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods

Abstract: Owing to the pandemic scenario of COVID-19 disease cases all over the world, the outbreak prediction has become extremely complex for the emerging scientific research. Several epidemiological mathematical models of spread are increasing daily to forecast the predictions appropriately. In this study, the classical susceptible-infected-recovered (SIR) modeling approach was employed to study the different parameters of this model for India. This approach was analyzed by considering different governmental lockdown measures in India. Some assumptions were considered to fit the model in the Python simulation for each lockdown scenario. The predicted parameters of the SIR model exhibited some improvement in each case of lockdown in India. In addition, the outcome results indicated that extreme interventions should be performed to tackle this type of pandemic situation in the near future.

An adaptive local linear optimized radial basis functional neural network model for financial time series prediction

Abstract: For financial time series, the generation of error bars on the point of prediction is important in order to estimate the corresponding risk. In recent years, optimization techniques-driven artificial intelligence has been used to make time series approaches more systematic and improve forecasting performance. This paper presents a local linear radial basis functional neural network (LLRBFNN) model for classifying finance data from Yahoo Inc. The LLRBFNN model is learned by using the hybrid technique of backpropagation and recursive least square algorithm. The LLRBFNN model uses a local linear model in between the hidden layer and the output layer in contrast to the weights connected from hidden layer to output layer in typical neural network models. The obtained prediction result is compared with multilayer perceptron and radial basis functional neural network with the parameters being trained by gradient descent learning method. The proposed technique provides a lower mean squared error and thus can be considered as superior to other models. The technique is also tested on linear data, i.e., diabetic data, to confirm the validity of the result obtained from the experiment.

Design and implementation of sharp edge FIR filters using hybrid differential evolution particle swarm optimization

Abstract: The filter is an important building block of modern communication and electronic systems. Based on well-defined bandwidth, it extracts the desired portion of the spectrum when the raw input signal is applied. Efficacy of filtering depends on the preciseness of bandwidth to avoid co-channel interference and signal loss. In addition, it must provide higher stopband attenuation and passband attenuation very close to unity with a tolerable quantity of pass/stop band ripple. Design/implementation of sharp edge modern FIR filter is structured as a multi-objective, constrained, complex, and highly nonlinear (hence multimodal) error minimization challenge. Hence, this work proposes a novel objective (normalized error fitness) function and a robust hybrid algorithm for the effective searching of the optimal filter coefficients for providing excellent sharp edge frequency response during the filtering action. Most popular particle swarm optimization (PSO) and differential evolution (DE) algorithm are effectively combined together to frame the proposed hybrid DE-PSO algorithm for enhancing the exploration and exploitation abilities of it. The proposed hybrid algorithm is validated using twelve different benchmark functions. Through simulations, the qualitative performance of the proposed approach is compared with the conventional PSO, DE, real-coded genetic algorithm and the Parks–McClellan method.