The primary aim of this study is to investigate suitable Statistical Neural Network (SNN) models and their hybrid version for COVID-19 mortality prediction in Indian populations and is to estimate the future COVID-19 death cases for India. SNN models such as Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For this purpose, we have used two datasets as D1 and D2. The performances of these models are evaluated using Root Mean Square Error (RMSE) and “R,” a correlation value between actual and predicted value. To improve prediction accuracy, the new hybrid models have been constructed by combining SNN models and the Non-linear Autoregressive Neural Network (NAR-NN). This is to predict the future error of the SNN models, which adds to the predicted value of these models for getting better MRP value. The results showed that the PNN and RBFNN-based MRP model performed better than the other models for COVID-19 datasets D2 and D1, respectively.

SEIR compartmental model for COVID-19 pandemic (SEIR_COVID) with exposed group is proposed and analysed in this paper. The steady state of this model is determined to control the pandemic. Zero Disease equilibrium and pandemic equilibrium are presented in SEIR_COVID model. In each case, the equilibrium prints are locally asymptotically stable under certain conditions. The Routh-Hurwitz criteria are used to study the stability of the equilibrium for COVID-19. This study showed that proposed SEIR_COVID model using Routh-Hurwitz criteria will be more effective for the COVID-19 pandemic prediction.

Regression model is an important tool for modeling and analyzing data. In this chapter, the proposed model comprises three phases. The first phase concentrates on sampling techniques to get best sample for building the regression model. The second phase is to predict the residual of logistic regression (LR) model using time series analysis method: autoregressive. The third phase is to develop enhanced logistic regression (ELR) model by combining both LR model and residual prediction (RP) model. The empirical study is carried out to study the performance of the ELR model using large diabetic dataset. The results show that ELR model has a higher level of accuracy than the traditional logistic regression model.

In current trends without preexisting infrastructures, Mobile ad-hoc networks (MANETs) are a specific kind of wireless sensor networks which are quickly adopted. Collaborative, medical, military or embedded applications are some of the different types used here. Large number of nodes is used at the large scale of network when MANETs provides new challenges where many clustering algorithms are mitigated. Clusters are the group of entities which allow the clustering algorithm for structuring of network creates a hierarchical structure. Identity, mobility, degree, density, weight, etc., are some of the specific or combination of metrics where each cluster contains a particular node called cluster head. The characteristics of the transmission medium are sharing, low bandwidth, and the routing protocols (information diffusion, path finding, etc.) are the two drawbacks for MANETs. Resource management and net-work performance (routing delay, bandwidth consumption and throughput) are improved to play a vital role for clustering in mobile ad hoc networks. Identifier Neighbor based clustering, Energy based clustering, Mobility based clustering, Topology based clustering, and Weight based clustering are studied and analyzed for existing clustering approaches of MANETs in this article. Then the advantages and disadvantages of these techniques include clustering definition with the clustering approach based on the observation.