AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification

Mohamed Abd Elaziz; Abdelghani Dahou; Shaker El-Sappagh; Alhassan Mabrouk; Mohamed Medhat Gaber

doi:10.3390/app12199710

AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification

Mohamed Abd Elaziz^*
, Abdelghani Dahou
, Shaker El-Sappagh
, Alhassan Mabrouk
, Mohamed Medhat Gaber

^*Corresponding author for this work

Galala University
Ajman University
Zagazig University
Lebanese American University
Ahmed Draia University
Benha University
Beni-Suef University

Research output: Contribution to journal › Article › peer-review

25 Citations (SciVal)

Original language	English
Article number	9710
Journal	Applied Sciences (Switzerland)
Volume	12
Issue number	19
DOIs	https://doi.org/10.3390/app12199710
Publication status	Published (VoR) - Oct 2022

Funding

This work has received financial support from the European Regional Development Fund (tRDF) and the Galician Regional Government, under the agreement for funding the Atlantic Research Center for Information and Communication Technologies (atlanTTic). This work was also supported by the Spanish Government under re-search project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” (PID2020-113795RB-C33/AEI/10.13039/501100011033).

Keywords

Aquila Optimization
Artificial Hummingbird Algorithm
feature selection algorithms
medical image classification
MobileNet

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10.3390/app12199710

Cite this

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title = "AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification",

keywords = "Aquila Optimization, Artificial Hummingbird Algorithm, feature selection algorithms, medical image classification, MobileNet",

author = "Elaziz, \{Mohamed Abd\} and Abdelghani Dahou and Shaker El-Sappagh and Alhassan Mabrouk and Gaber, \{Mohamed Medhat\}",

note = "Funding Information: This work has received financial support from the European Regional Development Fund (tRDF) and the Galician Regional Government, under the agreement for funding the Atlantic Research Center for Information and Communication Technologies (atlanTTic). This work was also supported by the Spanish Government under re-search project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” (PID2020-113795RB-C33/AEI/10.13039/501100011033). Publisher Copyright: {\textcopyright} 2022 by the authors.",

year = "2022",

month = oct,

doi = "10.3390/app12199710",

language = "English",

volume = "12",

journal = "Applied Sciences (Switzerland)",

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number = "19",

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TY - JOUR

T1 - AHA-AO

T2 - Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification

AU - Elaziz, Mohamed Abd

AU - Dahou, Abdelghani

AU - El-Sappagh, Shaker

AU - Mabrouk, Alhassan

AU - Gaber, Mohamed Medhat

N1 - Funding Information: This work has received financial support from the European Regional Development Fund (tRDF) and the Galician Regional Government, under the agreement for funding the Atlantic Research Center for Information and Communication Technologies (atlanTTic). This work was also supported by the Spanish Government under re-search project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” (PID2020-113795RB-C33/AEI/10.13039/501100011033). Publisher Copyright: © 2022 by the authors.

PY - 2022/10

Y1 - 2022/10

KW - Aquila Optimization

KW - Artificial Hummingbird Algorithm

KW - feature selection algorithms

KW - medical image classification

KW - MobileNet

UR - https://www.scopus.com/pages/publications/85139918219

UR - https://www.scopus.com/pages/publications/85139918219#tab=citedBy

U2 - 10.3390/app12199710

DO - 10.3390/app12199710

M3 - Article

AN - SCOPUS:85139918219

SN - 2076-3417

VL - 12

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

IS - 19

M1 - 9710

ER -

AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification

Funding

Keywords

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