This research aims to estimate stock returns, according to the Rough Set Theory approach, test its effectiveness and accuracy in predicting stock returns and their potential in the field of financial markets, and rationalize investor decisions. The research sample is totaling (10) companies traded at Iraq Stock Exchange. The results showed a remarkable Rough Set Theory application in data reduction, contributing to the rationalization of investment decisions. The most prominent conclusions are the capability of rough set theory in dealing with financial data and applying it for forecasting stock returns.The research provides those interested in investing stocks in financial markets with significant financial analysis tools that exceed the traditional statistical methods. The originality of the research lies in the diversification of financial and statistical analysis tools and methods of forecasting stock returns
In this paper we introduce several estimators for Binwidth of histogram estimators' .We use simulation technique to compare these estimators .In most cases, the results proved that the rule of thumb estimator is better than other estimators.
This work deals with kinetics and chemical equilibrium studies of esterification reaction of ethanol with acetic acid. The esterification reaction was catalyzed by an acidic ion exchange resin (Amberlyst- 15) using a batch stirred tank reactor. The pseudo-homogenous and Eley-Rideal models were successfully fitted with experimental data. At first, Eley-Rideal model was examined for heterogeneous esterification of acetic acid and ethanol. The pseudo-homogenous model was investigated with a power-law model. The apparent reaction order was determined to be (0.88) for Ethanol and (0.92) for acetic acid with a correlation coefficient (R2) of 0.981 and 0.988, respectively. The reaction order was determined to be 4.1087x10-3 L0.8/(mol0.8.min) with
... Show MoreAccording to the current situation of peroxidase (POD), the relevant studies on this enzyme indicated its importance as a tool in clinical biochemistry and different industrial fields. Most of these studies used the fruits and vegetables as source of this enzyme. So that in order to couple the growing requirements for POD with the recent demands for reduc-ing disposal volume by recycling the plant waste, the aim of the present study was to extract POD through management of municipal bio-waste of Iraqi maize species. A simple, green and economical method was used to extract this enzyme. Our results revealed that maize cobs are rich sources of POD, where the activity of this enzyme was found to be 7035.54 U/g of cobs. In pilot experiments thi
... Show MoreOne of the concerns of adopting an e-voting systems in the pooling place of any critical elections is the possibility of compromising the voting machine by a malicious piece of code, which could change the votes cast systematically. To address this issue, different techniques have been proposed such as the use of vote verification techniques and the anonymous ballot techniques, e.g., Code Voting. Verifiability may help to detect such attack, while the Code Voting assists to reduce the possibility of attack occurrence. In this paper, a new code voting technique is proposed, implemented and tested, with the aid of an open source voting. The anonymous ballot improved accordingly the paper audit trail used in this machine. The developed system,
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Predicting peterophysical parameters and doing accurate geological modeling which are an active research area in petroleum industry cannot be done accurately unless the reservoir formations are classified into sub-groups. Also, getting core samples from all wells and characterize them by geologists are very expensive way; therefore, we used the Electro-Facies characterization which is a simple and cost-effective approach to classify one of Iraqi heterogeneous carbonate reservoirs using commonly available well logs.
The main goal of this work is to identify the optimum E-Facies units based on principal components analysis (PCA) and model based cluster analysis(MC
... Show MoreMachine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed
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