2 research outputs found
A numerical study on the dynamics of SIR epidemic model through Genocchi wavelet collocation method
No full textAbstract Epidemic models can play a major role in understanding the spread of diseases and their control. These mathematical models have plenty of significance in various scientific domains, including public health, to investigate disease propagation and ecology. This article explains the dynamics of SIR epidemic model of arbitrary order with aid of a precise numerical approach called Genocchi wavelet collocation method. The main purpose of this investigation is to explore and discover the results for system of nonlinear ordinary differential equations arising in the considered mathematical model and to investigate the dynamical aspects of SIR model via Caputo fractional derivative which is non-local in behaviour. The projected method depicts rapid algorithms and is extremely precise, reliable, and uses fewer computational resources. Also, this method is simpler than the other traditional numerical methods as it merges the operational matrix with the collocation method in order to transform fractional-order problem into algebraic equations which enables to obtain satisfactory results. The approximate solution obtained using proposed algorithm exposes the nature of their interactions. Furthermore, the numerical outcomes are represented through graphs for different fractional order and compared the results with Runge–Kutta method and residual power series method. The projected technique is very effective, accurate, free from controlling parameters and consume less time to investigate nonlinear complications arising in diverse fields of epidemical and biological models. Ultimately, the current study help to inspect the wild class of models and their performance which are occurring in real world
Classification of Bharatanatyam postures using tailored features and artificial neural network
No full textBharatanatyam is a classical dance form of India that upholds the rich culture of India. This dance is learned under the supervision of Guru, the teacher traditionally called in India. The scarcity of experts resulted in the decline of people practicing this dance. There is a need for leveraging technology in preserving and promoting this traditional dance and propagating it amongst the youth. In this research, it is attempted to develop a methodology for automated classification of Bharatanatyam dance postures. The methodology involves extraction of existing features such as speeded up robust features (SURF) and histogram of oriented gradients (HOG), which are used to train and test an artificial neural network (ANN). The results are corroborated with deep learning architectures such as AlexNet and GoogleNet. The proposed methodology has yielded a classification accuracy of 99.85% as compared with 93.10% and 94.25% of AlexNet and GoogleNet respectively. The proposed method finds applications such as assistance to Bharatanatyam dance teachers, e-learning of dance, and evaluating the correctness of the postures
