Ziad Ramadan
Clarkson University
9 Papers
115 Citations
Ziad Ramadan is an academic researcher from Clarkson University. The author has contributed to research in topics: Particle & Feature selection. The author has an hindex of 7, co-authored 9 publications. Previous affiliations of Ziad Ramadan include California Institute of Technology.
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Papers
Understanding and controlling rotations in factor analytic models
TL;DR: Methods for estimating the rotational ambiguity in any specific result are discussed, and it is emphasized that application of these techniques must be based on some external information about acceptable or desirable shapes of factors.
464
Receptor modeling of ambient and personal exposure samples: 1998 Baltimore Particulate Matter Epidemiology-Exposure Study
Philip K. Hopke,Ziad Ramadan,Pentti Paatero,Gary A. Norris,Matthew S. Landis,Ronald Williams,Charles W. Lewis +6 more
TL;DR: In this article, the sources of particulate matter exposure for an elderly population in a city north of Baltimore, MD were evaluated using advanced factor analysis models using versatile air pollutant samplers positioned at a community site, outside and inside of an elderly residential facility.
95
Application of PLS and Back-Propagation Neural Networks for the estimation of soil properties
TL;DR: In this article, two different multivariate calibration methods, Partial Least Squares (PLS) and Back-Propagation Neural Networks (BP-ANN), were applied to microbial community DNA to predict soil properties.
73
Quantification of ATOFMS data by multivariate methods.
David P. Fergenson,Xin-Hua Song,Ziad Ramadan,Jonathan O. Allen,Lara S. Hughes,Glen R. Cass,Philip K. Hopke,Kimberly A. Prather +7 more
TL;DR: This study represents the use of a multivariate calibration method, two-dimensional partial least-squares analysis, for calibrating single-particle mass spectral data, resulting in the quantification of 44 different chemical species.
51
Variable selection in classification of environmental soil samples for partial least square and neural network models
TL;DR: In this article, two variable selection methods were evaluated by comparing their predictions with respect to differentiating among environmental soil samples, and the results showed that GA variable selection had a significant improvement of the predictive ability than the stepwise variable selection method.
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