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JCR Impact Factor: 0.800
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Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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  1/2018 - 14

A Novel Approach for Bi-Level Segmentation of Tuberculosis Bacilli Based on Meta-Heuristic Algorithms

AYAS, S. See more information about AYAS, S. on SCOPUS See more information about AYAS, S. on IEEExplore See more information about AYAS, S. on Web of Science, DOGAN, H. See more information about  DOGAN, H. on SCOPUS See more information about  DOGAN, H. on SCOPUS See more information about DOGAN, H. on Web of Science, GEDIKLI, E. See more information about  GEDIKLI, E. on SCOPUS See more information about  GEDIKLI, E. on SCOPUS See more information about GEDIKLI, E. on Web of Science, EKINCI, M. See more information about EKINCI, M. on SCOPUS See more information about EKINCI, M. on SCOPUS See more information about EKINCI, M. on Web of Science
 
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Download PDF pdficon (1,407 KB) | Citation | Downloads: 844 | Views: 4,067

Author keywords
computer aided analysis, heuristic algorithms, image segmentation, information entropy, particle swarm optimization

References keywords
tuberculosis(12), thresholding(10), segmentation(10), image(10), images(8), algorithm(7), algorithms(6), yang(5), sputum(5), method(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 113 - 120
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01014
Web of Science Accession Number: 000426449500014
SCOPUS ID: 85043229405

Abstract
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Image thresholding is the most crucial step in microscopic image analysis to distinguish bacilli objects causing of tuberculosis disease. Therefore, several bi-level thresholding algorithms are widely used to increase the bacilli segmentation accuracy. However, bi-level microscopic image thresholding problem has not been solved using optimization algorithms. This paper introduces a novel approach for the segmentation problem using heuristic algorithms and presents visual and quantitative comparisons of heuristic and state-of-art thresholding algorithms. In this study, well-known heuristic algorithms such as Firefly Algorithm, Particle Swarm Optimization, Cuckoo Search, Flower Pollination are used to solve bi-level microscopic image thresholding problem, and the results are compared with the state-of-art thresholding algorithms such as K-Means, Fuzzy C-Means, Fast Marching. Kapur's entropy is chosen as the entropy measure to be maximized. Experiments are performed to make comparisons in terms of evaluation metrics and execution time. The quantitative results are calculated based on ground truth segmentation. According to the visual results, heuristic algorithms have better performance and the quantitative results are in accord with the visual results. Furthermore, experimental time comparisons show the superiority and effectiveness of the heuristic algorithms over traditional thresholding algorithms.


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Cited-By CrossRef

[1] RUN: rational ubiquitous navigation, a model for automated navigation and searching in virtual environments, Raees, Muhammad, Ullah, Sehat, Virtual Reality, ISSN 1359-4338, Issue 2, Volume 25, 2021.
Digital Object Identifier: 10.1007/s10055-020-00468-0
[CrossRef]

[2] Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model, Hrizi, Olfa, Gasmi, Karim, Ben Ltaifa, Ibtihel, Alshammari, Hamoud, Karamti, Hanen, Krichen, Moez, Ben Ammar, Lassaad, Mahmood, Mahmood A., Li, Xingwang, Journal of Healthcare Engineering, ISSN 2040-2309, Issue , 2022.
Digital Object Identifier: 10.1155/2022/8950243
[CrossRef]

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Stefan cel Mare University of Suceava, Romania


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