A
Ali Azari
Researcher at University of Maryland, Baltimore County
Publications - 10
Citations - 123
Ali Azari is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Information technology & Cluster analysis. The author has an hindex of 6, co-authored 10 publications receiving 108 citations. Previous affiliations of Ali Azari include Tarbiat Modares University.
Papers
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Proceedings ArticleDOI
Predicting Hospital Length of Stay (PHLOS): A Multi-tiered Data Mining Approach
TL;DR: This paper proposes a methodology that employs clustering to create the training sets to train different classification algorithms and consistently found that using clustering as a precursor to form the training set gives better prediction results as compared to non-clustering based training sets.
Proceedings ArticleDOI
B-dids: Mining anomalies in a Big-distributed Intrusion Detection System
TL;DR: The architecture of a Big-distributed Intrusion Detection System (B-dIDS) to discover multi-pronged attacks which are anomalies existing across multiple subnets in a distributed network.
Journal ArticleDOI
Healthcare Data Mining: Predicting Hospital Length of Stay PHLOS
TL;DR: This study provides insight into the underlying factors that influence hospital length of stay, using a multi-tiered data mining approach to form training sets and identifying patients who need aggressive or moderate early interventions to prevent prolonged stays.
Proceedings ArticleDOI
Imbalanced learning to predict long stay Emergency Department patients
TL;DR: A framework that predicts patients with prolonged ED stays (> 14 hours) from data available at triage and integrates a class imbalance learning ensemble method into this framework produces much better results for prolonged stays than only using traditional logistic regression methods.
Posted Content
Information Technology Policy Trends in the World
TL;DR: In this article, the authors analyze worldwide information technology (IT) policy trends development using technology-diffusion, policy-making models and identify a framework based on the approach of "Hanna".