A Study on Unit Gompertz Distribution with Multi-Layer Artificial Neural Network Modeling

Tabassum  Naz Sindhu; Andac Batur Colak; Showkat  Ahmad Lone; Tahani  A Abushal; Nevine  M Gunaime; Anum  Shafiq

Authors

Tabassum Naz Sindhu Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
Andac Batur Colak Department of Information Systems and Technologies, Nigde ˘ Omer Halisdemir University, ¨ Nigde, T ˘ urkiye
Showkat Ahmad Lone Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, (Jeddah-M), Riyadh, Saudi Arabia
Tahani A Abushal Department of Mathematics, College of Science, Umm Al-Qura University, Makkah, Saudi Arabia
Nevine M Gunaime Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, (Jeddah-M), Riyadh, Saudi Arabia
Anum Shafiq Center for Theoretical Physics, Khazar University, Baku, Azerbaijan

Keywords:

Unit Gompertz model, multi-layer perceptron, artificial neural network modeling, reliability

Abstract

By incorporating the unit Gompertz (UG) distribution into the framework of artificial neural network (ANN) modelling, we present a novel approach in this paper. We include the UG distribution in our ANN modelling framework to improve the precision and interpretability of predictions. Through this integration, we hope to learn new things, strengthen forecast accuracy, and better understand the mechanisms at work in our data. After performing computations for a number of situations using the Hazard Rate Function (HRF), Cumulative Density Function (CDF), Probability Density Function (PDF), and Reliability (R) functions, a data collection has been developed. With this UG distribution and ANN modelling combo, it is expected that the ability to analyze and predict these functions would improve. Two separate artificial neural network models have been created using a total of 32 data set gathered. The generated multi-layer perceptron network models utilized 15% for model validation, 70% of data for model training, and 15% for model testing. The findings demonstrate that ANNs are highly accurate at predicting the PDF, CDF, HRF, and R functions of the UG model.

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