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15 results on '"Boskabadi, Azam"'

1. Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines

Author

Kamalzadeh, Hossein, Sobhan, Saeid Nassim, Boskabadi, Azam, Hatami, Mohsen, and Gharehyakheh, Amin

Subjects
Economics - General Economics, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The steel industry has great impacts on the economy and the environment of both developed and underdeveloped countries. The importance of this industry and these impacts have led many researchers to investigate the relationship between a country's steel consumption and its economic activity resulting in the so-called intensity of use model. This paper investigates the validity of the intensity of use model for the case of Iran's steel consumption and extends this hypothesis by using the indexes of economic activity to model the steel consumption. We use the proposed model to train support vector machines and predict the future values for Iran's steel consumption. The paper provides detailed correlation tests for the factors used in the model to check for their relationships with the steel consumption. The results indicate that Iran's steel consumption is strongly correlated with its economic activity following the same pattern as the economy has been in the last four decades., Comment: 13 pages, 13 figures

Published
2019

5. Optimizing Water Supply through Reservoir Conversion and Storage of Return Flow- A Case Study at Joe Pool Lake

Author

Sekar, Srividya, Daghighi, Amin, Chen, Victoria, Clingenpeel, Glenn, Zhang, Yu, Rosenberger, Jay Michael, and Boskabadi, Azam

Abstract

Maintaining an adequate water supply is one of the key challenges faced by the Dallas-Fort Worth Metroplex, where increasing population and rising water demand have elevated the vulnerability of the communities to water shortages. In this paper, we conducted a preliminary study exploring the possibility of converting flood storage in the Joe Pool Lake (JPL) as a means to improve water supply reliability and achieve better cost efficiency. This study employs a mixed integer linear programming (MILP) approach that considers the costs of meeting conversion demand and supply requirements over the northern portion of the Trinity River Basin. It includes trade-offs between capturing and storing natural flow versus return flow from the treatment facilities of the Trinity River Authority (TRA). A set of hypothetical prices and demand figures with the record drought of 1940-1996 considered to test the LP model. The optimal strategy yields expansion of JPL and associated storage-diversion on an annual basis. Also, the outcomes of the analyses suggest that, while the conversion would have a positive impact on water availability, storing the return flow might not produce sufficient cost savings; unless higher prices were imposed on the stored-return flow., Texas Water Journal, Vol. 13 No. 1 (2022): Texas Water Journal

Published
2022
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8. Machine learning Clustering Algorithms Based on the DEA Optimization Approach for Banking System in Developing Countries

Author

Mirmozaffari, Mirpouya, Boskabadi, Azam, Azeem, Gohar, Massah, Reza, Boskabadi, Elahe, Dolatsara, Hamidreza Ahady, Liravian, Ata, Mirmozaffari, Mirpouya, Boskabadi, Azam, Azeem, Gohar, Massah, Reza, Boskabadi, Elahe, Dolatsara, Hamidreza Ahady, and Liravian, Ata

Abstract

Machine learning grows quickly, which has made numerous academic discoveries and is extensively evaluated in several areas. Optimization, as a vital part of machine learning, has fascinated much consideration of practitioners. The primary purpose of this paper is to combine optimization and machine learning to extract hidden rules, remove unrelated data, introduce the most productive Decision-Making Units (DMUs) in the optimization part, and to introduce the algorithm with the highest accuracy in Machine learning part. In the optimization part, we evaluate the productivity of 30 banks from eight developing countries over the period 2015-2019 by utilizing Data Envelopment Analysis (DEA). An additive Data Envelopment Analysis (DEA) model for measuring the efficiency of decision processes is used. The additive models are often named Slack Based Measure (SBM). This group of models measures efficiency via slack variables. After applying the proposed model, the Malmquist Productivity Index (MPI) is computed to evaluate the productivity of companies. In the machine learning part, we use a specific two-layer data mining filtering pre-processes for clustering algorithms to increase the efficiency and to find the superior algorithm. This study tackles data and methodology-related issues in measuring the productivity of the banks in developing countries and highlights the significance of DMUs productivity and algorithms accuracy in the banking industry by comparing suggested models.

Published
2020

15. A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic.

Author

Mirmozaffari, Mirpouya, Yazdani, Reza, Shadkam, Elham, Khalili, Seyed Mohammad, Tavassoli, Leyla Sadat, and Boskabadi, Azam

Subjects
PUBLIC hospitals, STATISTICAL hypothesis testing, COVID-19 pandemic, STOCHASTIC frontier analysis, PANDEMICS, DATA envelopment analysis, HOSPITAL quality control
Abstract

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented. [ABSTRACT FROM AUTHOR]

Published
2022
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