M
Mahdi Salehi
Researcher at Ferdowsi University of Mashhad
Publications - 406
Citations - 3856
Mahdi Salehi is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Stock exchange & Audit. The author has an hindex of 22, co-authored 362 publications receiving 2591 citations. Previous affiliations of Mahdi Salehi include University of Mysore & University of Pretoria.
Papers
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E-Banking in Emerging Economy: Empirical Evidence of Iran
Mahdi Salehi,Mehrdad Alipour +1 more
TL;DR: In this paper, the results of this study show that e-banking serves several advantages to Iranian banking sector, however, the study also shows that the Iranian customers have not enough knowledge regarding e-Banking which is rendering by banking sector in Iran.
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Usefulness of Accounting Information System in Emerging Economy: Empirical Evidence of Iran
TL;DR: The main objective of an accounting information system (AIS) is the collection and recording of data and information regarding events that have an economic impact upon organizations and the maintenance, processing and communication of information to internal and external stakeholders.
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Competitive Advantages and Strategic Information Systems
TL;DR: The concepts of information system as a strategic tool are discussed and the competitiveness as a major factor for life of organizations in information edge is preyed of information technology challenges.
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Fraud Detection and Audit Expectation Gap: Empirical Evidence from Iranian Bankers
Mahdi Salehi,Zhila Azary +1 more
TL;DR: In this article, the authors determined the expectation gap in auditor's responsibility between auditors and bankers in Iran and concluded that the bankers have reasonableness expectation gap from auditors.
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Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping
Mahdi Boroughani,Mahdi Boroughani,Sima Pourhashemi,Sima Pourhashemi,Hossein Hashemi,Mahdi Salehi,Abolghasem Amirahmadi,Mohammad Ali Zangane Asadi,Ronny Berndtsson +8 more
TL;DR: Three statistical-based machine learning algorithms were used including Weights of Evidence (WOE), Frequency Ratio (FR), and Random Forest (RF) to produce DSSM for the Khorasan Razavi Province, in north-eastern Iran, and results indicated better performance of the RF algorithm.