Name: Marwan Abdul Hameed Ashour
Scientific Rank: Professor of Statistics and Operations Research at the University of Baghdad Professional Experience: Vice President, University of Baghdad for Scientific Affairs (2021-2024) Head of Continuing Education Center, University of Baghdad (2018-2023) Head of Quality Assurance and Academic Performance Department, University of Baghdad (2014–2017) Head of the Career Center, University of Baghdad (2016–2017) Head of Computer Center, University of Baghdad, College of Administration & Economics (2009–2012) Training Director of the Arab Trainers Union Awards: Science Day Award for the best-authored book in 2019 and 2020, granted by the Ministry of Higher Education and Scientific Research. **Published Work: **I have authored multiple books, including "Application and Analysis of a Quantitative Method for Business" (2009), "Artificial Neural Networks and Time Series Prediction Methods" (2018), "Institution of Education Rankings by Data Envelopment Analysis" (2018), "Operations Research: The First Part" (2018), and "Linear Programming Problems: Certainty and Uncertainty" (2019). Has more than fifty published papers and more than thirty papers in international conferences. Training Courses: Teaching methods and educational rehabilitation, University of Baghdad (2004) Develop skills in teaching methods and English, University of Erlangen, Germany (2010) Training course for building leadership capacity and the quality of administration, UNESCO-Jordan (2015) Use of data in the decision-making process, IREX (2016) The application of good laboratory international standard items, concepts of ISO 17025 & 15189, IIE (2016) Accreditation processes and procedures at the Accreditation Council Engineering and Technology ABET, Arab Organization for Quality Assurance in Education, American University of Beirut, Lebanon (2016) Professional Memberships: Editorial Board Memberships in the International Journal of Manufacturing Economics and Management Editorial Board Memberships in The Arab Journal of Quality in Education Member of the Iraqi Statistical Association Member of the Union of Arab Statisticians Member of the Iraqi Economic Association Member of the Association of Iraqi Operations Research Member of the Institutional Accreditation Council at the Ministry of Higher Education and Scientific Research Member of the Quality Council at the University of Baghdad Member of the Scientific Council of the Center for Continuing Education at the University of Baghdad
PHD - Statistics and Operations Research
Vice President, University of Baghdad for Scientific Affairs (2021-2024) Head of Continuing Education Center, University of Baghdad (2018-2023) Head of Quality Assurance and Academic Performance Department, University of Baghdad (2014–2017) Head of the Career Center, University of Baghdad (2016–2017) Head of Computer Center, University of Baghdad, College of Administration & Economics (2009–2012) Training Director of the Arab Trainers Union
Science Day Award for the best-authored book in 2019 Science Day Award for the best-authored book in 2020
In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc
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Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and
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