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JCR Impact Factor: 0.700
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SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
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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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

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.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 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 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  3/2020 - 3

 HIGH-IMPACT PAPER 

An Artificial Immune System Approach for a Multi-compartment Queuing Model for Improving Medical Resources and Inpatient Bed Occupancy in Pandemics

BELCIUG, S. See more information about BELCIUG, S. on SCOPUS See more information about BELCIUG, S. on IEEExplore See more information about BELCIUG, S. on Web of Science, BEJINARIU, S.-I. See more information about  BEJINARIU, S.-I. on SCOPUS See more information about  BEJINARIU, S.-I. on SCOPUS See more information about BEJINARIU, S.-I. on Web of Science, COSTIN, H. See more information about COSTIN, H. on SCOPUS See more information about COSTIN, H. on SCOPUS See more information about COSTIN, H. on Web of Science
 
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Download PDF pdficon (1,269 KB) | Citation | Downloads: 956 | Views: 2,414

Author keywords
artificial intelligence, evolutionary computation, hospitals, optimization, queueing analysis

References keywords
optimization(22), inspired(12), nature(9), algorithms(9), intelligence(8), artificial(7), systems(6), gorunescu(6), algorithm(6), selection(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-08-31
Volume 20, Issue 3, Year 2020, On page(s): 23 - 30
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.03003
Web of Science Accession Number: 000564453800003
SCOPUS ID: 85090354940

Abstract
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In the context of the Covid-19 pandemic the pressure that is put on the medical systems is increasing exponentially. Healthcare systems resources are in general scarce, and hence they require policies that ensure the optimal usage of beds and utilization costs. The aim of this study is to explore how artificial immune system approaches for a multi-queuing model may aid the hospital managers improve their resources. The proposed system outlines the route of Covid-19 patients in the intensive care unit (ICU), the compartmental model proposes a reasonable composition of the ICU, considering the queuing parameters, while the artificial immune system optimizes the needed resources (beds plus associated costs). The methodology was demonstrated through a simulation based on real data collected from official sources.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 8 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 9
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Cited-By CrossRef

[1] Realizing Resilient Global Market Opportunities and Societal Benefits Through Innovative Digital Technologies in the Post COVID-19 Era: A Conceptual Framework and Critical Literature Review, Galetsi, Panagiota, Katsaliaki, Korina, Kumar, Sameer, IEEE Transactions on Engineering Management, ISSN 0018-9391, Issue , 2024.
Digital Object Identifier: 10.1109/TEM.2023.3303080
[CrossRef]

[2] Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review, Chee, Marcel Lucas, Ong, Marcus Eng Hock, Siddiqui, Fahad Javaid, Zhang, Zhongheng, Lim, Shir Lynn, Ho, Andrew Fu Wah, Liu, Nan, International Journal of Environmental Research and Public Health, ISSN 1660-4601, Issue 9, Volume 18, 2021.
Digital Object Identifier: 10.3390/ijerph18094749
[CrossRef]

[3] An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study, Ortiz-Barrios, Miguel, Petrillo, Antonella, Arias-Fonseca, Sebastián, McClean, Sally, de Felice, Fabio, Nugent, Chris, Uribe-López, Sheyla-Ariany, International Journal of Emergency Medicine, ISSN 1865-1380, Issue 1, Volume 17, 2024.
Digital Object Identifier: 10.1186/s12245-024-00626-0
[CrossRef]

[4] The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19, Galetsi, Panagiota, Katsaliaki, Korina, Kumar, Sameer, Social Science & Medicine, ISSN 0277-9536, Issue , 2022.
Digital Object Identifier: 10.1016/j.socscimed.2022.114973
[CrossRef]

[5] Analysis of Deep Transfer Learning Methods for Early Diagnosis of the Covid-19 Disease with Chest X-ray Images, ÖZDEMİR, Durmuş, ARSLAN, Naciye Nur, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, ISSN 2148-2446, Issue 2, Volume 10, 2022.
Digital Object Identifier: 10.29130/dubited.976118
[CrossRef]

[6] Regional medical resource synergistic security resilience assessment based on city network: A case study of YRD, PRD, and BTH, Kou, Longbin, Zhao, Hanping, Yang, Zhixiang, Li, Xianghui, Zhang, Yichi, Liang, Jinfan, Qiu, Haoyue, Zhang, Yumian, Cities, ISSN 0264-2751, Issue , 2024.
Digital Object Identifier: 10.1016/j.cities.2024.105277
[CrossRef]

[7] A data generator for covid-19 patients’ care requirements inside hospitals, Marin-Garcia, Juan A., Ruiz, Angel, Maheut, Julien, Garcia-Sabater, Jose P., WPOM-Working Papers on Operations Management, ISSN 1989-9068, Issue 1, Volume 12, 2021.
Digital Object Identifier: 10.4995/wpom.15332
[CrossRef]

[8] Optimization in the Context of COVID-19 Prediction and Control: A Literature Review, Jordan, Elizabeth, Shin, Delia E., Leekha, Surbhi, Azarm, Shapour, IEEE Access, ISSN 2169-3536, Issue , 2021.
Digital Object Identifier: 10.1109/ACCESS.2021.3113812
[CrossRef]

Updated 2 days, 13 hours ago

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