Islamic Azad University

About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Catalysis. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction

Abstract: Artificial Intelligence models (AI) which computerize human reasoning has found a challenging test bed for various paradigms in many areas including financial time series prediction. Extensive researches have resulted in numerous financial applications using AI models. Since stock investment is a major investment activity, Lack of accurate information and comprehensive knowledge would result in some certain loss of investment. Hence, stock market prediction has always been a subject of interest for most investors and professional analysts. Stock market prediction is a challenging problem because uncertainties are always involved in the market movements. This paper proposes a hybrid intelligent model for stock exchange index prediction. The proposed model is a combination of data preprocessing methods, genetic algorithms and Levenberg-Marquardt (LM) algorithm for learning feed forward neural networks. Actually it evolves neural network initial weights for tuning with LM algorithm by using genetic algorithm. We also use data pre-processing methods such as data transformation and input variables selection for improving the accuracy of the model. The capability of the proposed method is tested by applying it for predicting some stock exchange indices used in the literature. The results show that the proposed approach is able to cope with the fluctuations of stock market values and also yields good prediction accuracy. So it can be used to model complex relationships between inputs and outputs or to find data patterns while performing financial prediction.

An electrochemical-amplified-platform based on the nanostructure voltammetric sensor for the determination of carmoisine in the presence of tartrazine in dried fruit and soft drink samples

Abstract: This work demonstrates the fabrication of a carbon paste electrode containing NiO/CNTs modified with 1-methyl-3-butylimidazolium bromide as the binder (CPE/1-M-3BIBr/NiO/CNTs), and its applications related to the highly sensitive detection of carmoisine. The CPE/1-M-3BIBr/NiO/CNTs system was successfully utilized for the nanomolar determination of carmoisine at pH = 7.0. The CPE/1-M-3BIBr/NiO/CNTs successfully resolved the oxidation signals of carmoisine and tartrazine with a peak separation of 450 mV. This is the first time that an application of an electrochemical sensor for the simultaneous determination of carmoisine and tartrazine, two important azo dyes, has been reported. In addition, wide dynamic linear concentration ranges (70–650 μM for carmoisine and 0.1–750 μM for tartrazine), low detection limits (20 nM for carmoisine and 0.06 μM tartrazine) and excellent reproducibility were reported. The CPE/1-M-3BIBr/NiO/CNTs were successfully used for the analysis of carmoisine and tartrazine in dried fruit and soft drink samples.

A fractional differential equation model for the COVID-19 transmission by using the Caputo–Fabrizio derivative

Abstract: We present a fractional-order model for the COVID-19 transmission with Caputo–Fabrizio derivative. Using the homotopy analysis transform method (HATM), which combines the method of homotopy analysis and Laplace transform, we solve the problem and give approximate solution in convergent series. We prove the existence of a unique solution and the stability of the iteration approach by using fixed point theory. We also present numerical results to simulate virus transmission and compare the results with those of the Caputo derivative.