Farid Hashemian Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
Research Areas
Biography and Research Information
OverviewAI-generated summary
Farid Hashemian's research focuses on the application of machine learning and simulation techniques to enhance system reliability and optimize maintenance strategies. He has investigated methods for improving all-terminal network reliability, employing machine learning frameworks for joint reliability enhancement and maintenance. Hashemian has also explored the use of machine learning and metaheuristics for network reliability enhancement. His work extends to system simulation, including the development of automated input distribution fitting for simulation libraries. Additionally, Hashemian has studied user behavior in digital platforms, specifically analyzing e-book platforms to understand the link between user behavior, customer segmentation, and subscription sales through A/B testing. His scholarship metrics include an h-index of 2 with 28 citations across 7 publications.
Metrics
- h-index: 2
- Publications: 8
- Citations: 29
Selected Publications
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SSL-MTab: Self-Supervised Distillation for Missing Data in Tabular Prediction Tasks (2026)
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A Machine Learning Framework for Joint Reliability Improvement and Maintenance of All-Terminal Networks (2026)
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Network Reliability Enhancement Using Machine Learning and Metaheuristics (2026)
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From User Behavior to Subscription Sales: An Insight Into E-Book Platform Leveraging Customer Segmentation and A/B Testing (2024)
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System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System (2023)
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Applying Machine Learning Methods to Improve All-Terminal Network Reliability (2023)
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