J
Javad Haddadnia
Researcher at Hakim Sabzevari University
Publications - 129
Citations - 1732
Javad Haddadnia is an academic researcher from Hakim Sabzevari University. The author has contributed to research in topics: Facial recognition system & Feature extraction. The author has an hindex of 20, co-authored 126 publications receiving 1453 citations. Previous affiliations of Javad Haddadnia include Amirkabir University of Technology & University of Windsor.
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A mass classification and image retrieval model for mammograms
TL;DR: This model is formed based on breast density, according to the categories defined by the breast imaging-reporting and data system (BIRADS), which is a standard on the assessment of mammographic images and is tested on the Mammographic Image Analysis Society (MIAS) database.
Journal Article
Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems
TL;DR: A new algorithm that can detect breast cancer based on combining artificial intelligent system and Fine Needle Aspiration can be introduced as a useful diagnostic system for comprehensive treatment of breast cancer.
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Alzheimer’s disease diagnosis using genetic programming based on higher order spectra features
TL;DR: In this article , a genetic programming (GP) based data-driven evolutionary computation based model was used for the diagnosis of Alzheimer's disease using spontaneous speech analysis (SSA) and the results showed that the GP method achieved better performance compared to other the state-of-the-art approaches.
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Striking patterns in natural magic squares’ associated electrostatic potentials: Matrices of the 4th and 5th order
Peyman Fahimi,Cyrus Ahmadi Toussi,Cyrus Ahmadi Toussi,Walter Trump,Javad Haddadnia,Chérif F. Matta,Chérif F. Matta,Chérif F. Matta +7 more
TL;DR: It is shown that characteristic patterns emerge from plots of the ESPs of the matrices representing the studied squares, and these findings may help to open a new perspective regarding magic squares unsolved problems.
Journal Article
Speech Enhancement using Laplacian Mixture Model under Signal Presence Uncertainty
TL;DR: In this paper, an estimator for speech enhancement based on Laplacian Mixture Model has been proposed, which estimates the complex DFT coefficients of clean speech from noisy speech using the MMSE estimator.