Average per capita GDP income is an important economic indicator. Economists use this term to determine the amount of progress or decline in the country's economy. It is also used to determine the order of countries and compare them with each other. Average per capita GDP income was first studied using the Time Series (Box Jenkins method), and the second is linear and non-linear regression; these methods are the most important and most commonly used statistical methods for forecasting because they are flexible and accurate in practice. The comparison is made to determine the best method between the two methods mentioned above using specific statistical criteria. The research found that the best approach is to build a model for predicting Iraq’s average GDP per capita income by relying on the amounts of average GDP per capita income in the past years (1981-2020). The researcher found that in a second way, it became clear that the non-linear regression model of the Asian model was the best model representing (average per capita GDP income) in Iraq, and this model was used to predict the period (20221-2027). When comparing the two methods of projected amounts up to 2027, it was found that the best method was the second based on the indicator mean absolute percentage error (MAPE) because he has the least value.
Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results
... Show MoreThis work is devoted to define new generalized gamma and beta functions involving the recently suggested seven-parameter Mittag-Leffler function, followed by a review of all related special cases. In addition, necessary investigations are affirmed for the new generalized beta function, including, Mellin transform, differential formulas, integral representations, and essential summation relations. Furthermore, crucial statistical application has been realized for the new generalized beta function.
In this study, an improved process was proposed for the synthesis of structure-controlled Cu2O nanoparticles, using a simplified wet chemical method at room temperature. A chemical solution route was established to synthesize Cu2O crystals with various sizes and morphologies. The structure, morphology, and optical properties of Cu2O nanoparticles were analyzed by X-ray diffraction, SEM (scanning electron microscope), and UV-Vis spectroscopy. By adjusting the aqueous mixture solutions of NaOH and NH2OH•HCl, the synthesis of Cu2O crystals with different morphology and size could be realized. Strangely, it was found that the change in the ratio of de-ionized water and NaOH aqueous solution led to the synthesis of Cu2O crystals of differen
... Show MoreText Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as t
... Show MoreThis paper demonstrates the construction of a modern generalized Exponential Rayleigh distribution by merging two distributions with a single parameter. The "New generalized Exponential-Rayleigh distribution" specifies joining the Reliability function of exponential pdf with the Reliability function of Rayleigh pdf, and then adding a shape parameter for this distribution. Finally, the mathematical and statistical characteristics of such a distribution are accomplished
MWCNTs and hybrid nanocomposite ZnO/Se/MWCNTs have been prepared via Solvothermal technique using Parr reactor at the temperature 180°C and SeCl2 as a catalyst. The obtained MWCNTs and ZnO/Se/MWCNTs are investigated using the FE-SEM, XRD, UV-VIS Spectroscopy and Z-Scan. The novelty of this research is studying the nonlinear optical properties for these prepared materials and the results exhibit that the thickness of the deposited film for hybrid nanocomposite ZnO/Se/MWCNTs is increased, which in turn, increase the nonlinear phase shift of the laser beam compared with the MWCNTs.
The integration of Artificial Intelligence with Big Data Analytics is one of the most groundbreaking developments that could change the face of educational sustainability in higher education.. Using AI and Big Data technologies not only makes the educational process more efficient but also changes the way people learn and thus opens the door for educators and institutions to make decisions based on the data. The document imparts the manner that the use of AI and the digital revolution can remove student requirements, execute the efficiency of the curriculum, and acquire the balance of educational resources through a majority of instances and the latest developments in that field. Furthermore, the paper, along with the issues of morality wit
... Show MoreStatistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c
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