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Samaneh Mazaheri

Researcher at University of Ontario Institute of Technology

Publications -  27
Citations -  268

Samaneh Mazaheri is an academic researcher from University of Ontario Institute of Technology. The author has contributed to research in topics: Image segmentation & Coronary arteries. The author has an hindex of 9, co-authored 26 publications receiving 154 citations. Previous affiliations of Samaneh Mazaheri include University of Tehran & Information Technology University.

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Journal ArticleDOI

New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

TL;DR: An imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease and can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms.
Journal ArticleDOI

A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm

TL;DR: A Modified Differential Evolution approach to Feature Selection (MDEFS) is proposed by utilizing two new mutation strategies to create a feasible balance between exploration and exploitation and maintain the classification performance in an acceptable range concerning both the number of features and accuracy.
Proceedings ArticleDOI

Echocardiography Image Segmentation: A Survey

TL;DR: This paper surveys the literature often recent researches on echocardiography image segmentation methods, focusing on techniques developed for medical use, and presents a classification of methodology in terms of use of prior information.
Journal ArticleDOI

Landfill site selection using GIS-based multi-criteria evaluation (case study: SaharKhiz Region located in Gilan Province in Iran)

TL;DR: Fuzzy logic has more flexibility to resolve conflicts of human judgment, and it also has higher accuracy than Boolean logic in the selection of optimal landfill sites for MSW in SaharKhiz Region, Gilan Province, based on ecological and socioeconomic parameters.
Proceedings ArticleDOI

Coronary Artery Segmentation in Angiograms with Pattern Recognition Techniques -- A Survey

TL;DR: A survey for the main class, pattern recognition, which is a famous technique in this manner is proposed and a table is made to compare all the algorithms in each category against criteria such as: user interaction, angiography types, dimensionality, enhancement method, full coronary artery output, whole tree output, and 3D reconstruction ability.