Yazar "Lone, Showkat Ahmad" seçeneğine göre listele

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    Comparative study of artificial neural network versus parametric method in COVID-19 data analysis
    (Elsevier, 2022) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Alsubie, Abdelaziz; Jarad, Fahd
    Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.
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    Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network
    (Wiley-V C H Verlag Gmbh, 2022) Shafiq, Anum; Colak, Andac Batur; Swarup, Chetan; Sindhu, Tabassum Naz; Lone, Showkat Ahmad
    The study of reliability analysis of mixture model is essential in confirming the quality of devices, equipment, and electronic tube flops etc. In recent years, statisticians have developed more interest in mixture model research, notably in the last decade, without taking into account the issue of modeling the metrics of reliability of mixture models using artificial neural networks. In the present study, the influence of pertinent parameters on reliability metrics is studied. The effect of components and mixing parameters for failure function, reversed hazard rate function, mean time to failure, hazard rate function, mean inactivity time, mean residual life, reliability function, Mills Ratio profiles are plotted and discussed. A multi-layer artificial neural network is developed using the numerical analysis results obtained using four different scenarios. The values extracted from the artificial neural network and the numerical findings of the reliability studies are extensively compared and examined. The deviation rates obtained for the developed artificial neural network model are obtained at values lower than 0.12%. The outcomes demonstrate that neural networks are a powerful and effective mathematical tool that can be used in the reliability analysis of mixing models.
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    Reliability modeling and analysis of mixture of exponential distributions using artificial neural network
    (Wiley, 2024) Shafiq, Anum; Colak, Andac Batur; Lone, Showkat Ahmad; Sindhu, Tabassum Naz; Muhammad, Taseer
    In recent years, statisticians have become more and more interested in the study of mixture models, especially in the last decade, without adequately considering the difficulty of modeling the reliability measures of mixture models using artificial neural networks. In this study, in which artificial neural networks and mixed model reliability criteria are analyzed, various reliability parameters are calculated considering different scenarios. In order to estimate the obtained numerical reliability parameters, a multilayer artificial neural network model has been developed. Seven different reliability parameter values have been obtained from the artificial neural network model designed with four input parameters. The prediction values obtained from the artificial neural network model developed with five neurons in the hidden layer have been compared with numerical data, and the performance of the model has been analyzed comprehensively. The mean squared error (MSE) value for the network model has been calculated as 1.98E-08 and the R value as 0.99991. The results clearly revealed that the artificial neural network model developed using data from the appropriate statistical model is an excellent tool that can be used to estimate reliability measures.