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Your search keyword '"Darwish, Jumanah Ahmed"' showing total 11 results
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11 results on '"Darwish, Jumanah Ahmed"'

1. Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement

Author

Mahmood, Tariq, Ahmad, Ibtasam, Ansar, Malik Muhammad Zeeshan, Darwish, Jumanah Ahmed, and Sherwani, Rehan Ahmad Khan

Subjects
Quantitative Finance - Statistical Finance, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantum Physics
Abstract

In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short-term memory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi Stock Exchange (KSE) 100 index by taking monthly data of twenty-six economic, social, political, and administrative indicators from February 2004 to December 2020. The comparative results of LSTM and QLSTM predicted values of the KSE 100 index with the actual values suggested QLSTM a potential technique to predict stock market trends.

Published
2024

10. Bivariate Transmuted Burr Distribution: Properties and Application.

Author

Darwish, Jumanah Ahmed, Al turk, Lutfiah Ismail, and Shahbaz, Muhammad Qaiser

Subjects
*MAXIMUM likelihood statistics, *MARGINAL distributions, *HAZARD function (Statistics)
Abstract

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way of modeling complex joint phenomenon. In this article, a new bivariate distribution is proposed which is known as the bivariate transmuted Burr (BTB) distribution. This new bivariate distribution is extension of the univariate transmuted Burr (TB) distribution to two variables. The proposed BTB distribution is explored in detail and the marginal and conditional distributions for the distribution are obtained. Joint and conditional moments alongside hazard rate functions are obtained. The maximum likelihood estimation (MLE) for the parameters of the BTB distribution is also done. Finally, real data application of the BTB distribution is given. It is observed that the proposed BTB distribution is a suitable fit for the data used. [ABSTRACT FROM AUTHOR]

Published
2021
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11. The Bivariate Transmuted Family of Distributions: Theory and Applications.

Author

Darwish, Jumanah Ahmed, Al turk, Lutfiah Ismail, and Shahbaz, Muhammad Qaiser

Subjects
BIVARIATE analysis, DISTRIBUTION (Probability theory), RANDOM variables, MAXIMUM likelihood statistics, STATISTICAL models
Abstract

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
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