Institution
Islamic Azad University
Education•Tehran, Iran•
About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Catalysis. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.
Topics: Population, Catalysis, Adsorption, Fuzzy logic, Nonlinear system
Papers published on a yearly basis
Papers
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TL;DR: The high water flux and high metal ions removal within 18 h filtration time showed the high potential of PVDF/ PAN/chitosan/UiO-66-NH2 membrane for the removal of metal ions from aqueous solutions.
327 citations
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TL;DR: The MOPSO method has been used for management and optimal distribution of energy resources in proposed micro-grid and the problem was analyzed with the NSGA-II algorithm to demonstrate the efficiency of the proposed method.
327 citations
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TL;DR: In this paper, a review on application of nanofluids in heat exchangers has been addressed, and it can be concluded that the use of nanophotonics in most cases improves heat transfer, which reduces the volume of heat exchanger, saving energy, consequently water consumption and industrial waste.
325 citations
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TL;DR: Two automated methods to diagnose mass types of benign and malignant in mammograms are presented and different classifiers (such as random forest, naive Bayes, SVM, and KNN) are used to evaluate the performance of the proposed methods.
Abstract: CNN templates are generated using a genetic algorithm to segment mammograms.An adaptive threshold is computed in region growing process by using ANN and intensity features.In tumor classification, CNN produces better results than region growing.MLP produces the highest classification accuracy among other classifiers.Results on DDSM images are more appropriate than those of MIAS. Breast cancer is regarded as one of the most frequent mortality causes among women. As early detection of breast cancer increases the survival chance, creation of a system to diagnose suspicious masses in mammograms is important. In this paper, two automated methods are presented to diagnose mass types of benign and malignant in mammograms. In the first proposed method, segmentation is done using an automated region growing whose threshold is obtained by a trained artificial neural network (ANN). In the second proposed method, segmentation is performed by a cellular neural network (CNN) whose parameters are determined by a genetic algorithm (GA). Intensity, textural, and shape features are extracted from segmented tumors. GA is used to select appropriate features from the set of extracted features. In the next stage, ANNs are used to classify the mammograms as benign or malignant. To evaluate the performance of the proposed methods different classifiers (such as random forest, naive Bayes, SVM, and KNN) are used. Results of the proposed techniques performed on MIAS and DDSM databases are promising. The obtained sensitivity, specificity, and accuracy rates are 96.87%, 95.94%, and 96.47%, respectively.
323 citations
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TL;DR: In this article, the effects of some admixtures including silica nanoparticles, silica fume and Class F fly ash on different properties of high performance self compacting concrete (HPSCC) were presented.
323 citations
Authors
Showing all 83704 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ajit Kumar Mohanty | 141 | 1124 | 93062 |
Pierluigi Paolucci | 138 | 1965 | 105050 |
Eric Conte | 132 | 1206 | 84593 |
Patrizia Azzi | 132 | 1275 | 83686 |
D. Del Re | 131 | 1406 | 87230 |
Jean-Laurent Agram | 128 | 1221 | 84423 |
Seyed Mohsen Etesami | 128 | 1101 | 76488 |
Jean-Charles Fontaine | 128 | 1190 | 84011 |
Roberta Arcidiacono | 128 | 1322 | 80917 |
Tejinder Virdee | 128 | 1208 | 74372 |
Frank Hartmann | 127 | 1116 | 81455 |
Paolo Azzurri | 126 | 1058 | 81651 |
Achim Stahl | 124 | 1248 | 111121 |
Federica Primavera | 120 | 876 | 63895 |
Riccardo Andrea Manzoni | 120 | 946 | 67897 |