Yazar "Abushal, Tahani A." seçeneğine göre listele

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    A decreasing failure rate model with a novel approach to enhance the artificial neural network's structure for engineering and disease data analysis
    (Elsevier, 2024) Sindhu, Tabassum Naz; Çolak, Andaç Batur; Lone, Showkat Ahmad; Shafiq, Anum; Abushal, Tahani A.
    The study focuses on key metrics used to examine the characteristics of a lifetime random variable distribution in reliability and survival theory research. In this analysis, metrics including the probability density function time, mean residual lifespan, mean time between failures, hazard rate, and reliability function are essential. The focus of the inquiry is these important parameters in relation to the Burr-Hatke exponential model specifically. The study focuses on key metrics used to examine the characteristics of a lifetime random variable distribution in reliability and survival theory research. In this analysis, metrics including the probability density function time, mean residual lifespan, mean time between failures, hazard rate, and reliability function are essential The focus of the inquiry is these important parameters in relation to the Burr-Hatke exponential model specifically. A key component of the research is a comparison of the outcomes from the artificial intelligence approach and those from conventional literature-based methodologies. This comparison study sheds light on how well the artificial neural network framework performs while evaluating the Burr-Hatke exponential model’s technical features. The study allows a comprehensive analysis of the training and prediction capabilities of the growing neural network by calculating multiple performance measures. This comprehensive strategy improves our comprehension of the model’s survival traits and reliability, offering significant contributions to the larger field of study. The network structure’s mean square error was estimated to be 5.19E-04, and its coefficient of determination value was 0.99987 for the first neural network model. For the second neural network model, the coefficient of determi nation value was 0.99999 and the mean square error value was 4.58E-06. The outcomes amply revealed the neural network structure’s extraordinarily high prediction accuracy and the degree to which the prediction outputs agree with those of the Maximum Likelihood Estimation technique.
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    Modeling and survival exploration of breast carcinoma: A statistical, maximum likelihood estimation, and artificial neural network perspective
    (Elsevier, 2023) Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Abushal, Tahani A.
    The core objective of this research is to describe the behavior of the distribution using the MLE method to es timate its parameters, as well as to determine the optimal Artificial Neural Network method by comparing it to the maximum likelihood estimation method and applying it to real data for breast cancer patients to determine survival, risk, and other survival study functions of the log-logistic distribution. The parameters were defined in the input layer of the artificial neural network developed for the purpose of survival analysis and reliability function, hazard rate function, probability density function, reserved hazard rate function, Mills ratio, Odd function and CHR values were obtained in the output layer. The findings show that risk function increases with the increase in the time of infection and then decreases for a group of breast cancer patients under study, which corresponds to the theoretical properties of this according to the practical conclusions. The examination of survival analysis reveals that practical conclusions correspond to the theoretical properties of log-logistic dis tribution. Artificial neural networks have proven to be one of the ideal tools that can be used to predict various vital parameters, especially survival of cancer patients, with their high predictive capabilities.
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    Reliability study of generalized Rayleigh distribution based on inverse power law using artificial neural network with Bayesian regularization
    (Elsevier, 2023) Çolak, Andaç Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Shafiq, Anum; Abushal, Tahani A.
    Using the generalized Rayleigh distribution and the inverse power law, this paper proposes a new reliability model and investigates the effect of the key parameters on reliability measurements. This proposed new model offers a more accurate way to model the performance of electronic components over their lifetimes. In order to analyze the reliability parameters, a multi-layer artificial neural network model has been developed by using the datasets generated by numerical methods and obtained in four different scenarios. Using the artificial neural network model with 5 neurons in the hidden layer, the reliability parameters Hazard Rate Function, Odds function, Reversed Hazard Rate Function, Mean Time to Failure and Mean Time Between Failures have been estimated. The results obtained have been analyzed comprehensively and explained with graphics. The study findings showed that there was a direct relationship between the reliability parameters examined in all scenarios and an increase in the Mean Time Between Failures value appeared for each scenario. However, it has also been seen that the developed artificial neural network can make predictions with very high accuracy and is a powerful engineering tool that can be utilized in reliability analysis.