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JCR Impact Factor: 0.700
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Issues per year: 4
Current issue: May 2024
Next issue: Aug 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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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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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.

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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.

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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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  2/2022 - 6

Classification of Low-Resolution Flying Objects in Videos Using the Machine Learning Approach

STANCIC, I. See more information about STANCIC, I. on SCOPUS See more information about STANCIC, I. on IEEExplore See more information about STANCIC, I. on Web of Science, VEIC, L., MUSIC, J. See more information about  MUSIC, J. on SCOPUS See more information about  MUSIC, J. on SCOPUS See more information about MUSIC, J. on Web of Science, GRUJIC, T. See more information about GRUJIC, T. on SCOPUS See more information about GRUJIC, T. on SCOPUS See more information about GRUJIC, T. on Web of Science
 
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Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,493 KB) | Citation | Downloads: 715 | Views: 1,401

Author keywords
artificial neural networks, computer vision, feature extraction, machine learning, object detection

References keywords
detection(23), drone(14), sensors(10), machine(9), learning(9), tracking(5), system(5), sensor(5), classification(5), review(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 45 - 52
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02006
Web of Science Accession Number: 000810486800006
SCOPUS ID: 85131761618

Abstract
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A challenge of detecting and identifying drones has emerged due to the significant increase in recreational and commercial drones operating range, payload size, and overall capabilities. Consequently, drones may pose a risk to airspace safety or violate non-flying zone in the vicinity of vulnerable buildings. This paper presents an initial study for a machine-learning classification system applied to flying objects visible with a low resolution that is too distant from the camera to be efficiently classified by other methods. The original dataset in form of labeled high-resolution videos containing low-resolution drone, bird, and airplane objects was collected and carefully prepared. Computationally inexpensive features based on object shape and trajectory descriptors were recommended and tested with several ML models. The accuracy of the best-proposed model tested on our dataset was 98%. The results of this study demonstrate that Machine Learning classification seems to be promising and can be implemented in future multi-stage drone detection and identification system.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 1 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 2
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Cited-By CrossRef

[1] Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network, Dai, Deen, Cao, Lihua, Liu, Yangfan, Wang, Yao, Wu, Zhaolong, Remote Sensing, ISSN 2072-4292, Issue 20, Volume 15, 2023.
Digital Object Identifier: 10.3390/rs15204985
[CrossRef]

[2] Multiple Flying Object Detection using AlexNet Architecture for Aerial Surveillance Applications, Bajpai, Abhishek, Srivastava, Vaibhav, Yadav, Shruti, Sharma, Yash, 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), ISBN 979-8-3503-3509-5, 2023.
Digital Object Identifier: 10.1109/ICCCNT56998.2023.10307613
[CrossRef]

Updated 2 days, 22 hours ago

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