An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical Data: A COVID-19 Case Study
Feature Subset Selection (FSS) is an NP-hard problem to remove redundant and irrelevant features particularly from medical data, and it can be effectively addressed by metaheuristic algorithms. However, existing binary versions of metaheuristic algorithms have issues with convergence and lack an eff...
Uložené v:
| Vydané v: | Journal of bionics engineering Ročník 21; číslo 1; s. 426 - 446 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Singapore
Springer Nature Singapore
01.01.2024
Springer Nature B.V |
| Predmet: | |
| ISSN: | 1672-6529, 2543-2141 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Feature Subset Selection (FSS) is an NP-hard problem to remove redundant and irrelevant features particularly from medical data, and it can be effectively addressed by metaheuristic algorithms. However, existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method, resulting in suboptimal solutions that hinder diagnosis and prediction accuracy. This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm (IBQANA) for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms. The proposed IBQANA’s contributions include the Hybrid Binary Operator (HBO) and the Distance-based Binary Search Strategy (DBSS). HBO is designed to convert continuous values into binary solutions, even for values outside the [0, 1] range, ensuring accurate binary mapping. On the other hand, DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence. By combining exploration and exploitation phases based on an adaptive probability function, DBSS effectively avoids local optima. The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets, with feature numbers ranging from 8 to 10,509. IBQANA's effectiveness is evaluated regarding the accuracy, fitness, and selected features and compared with seven binary metaheuristic algorithms. Furthermore, IBQANA is utilized to detect COVID-19. The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets. The proposed method presents a promising solution to the FSS problem in medical data preprocessing. |
|---|---|
| AbstractList | Feature Subset Selection (FSS) is an NP-hard problem to remove redundant and irrelevant features particularly from medical data, and it can be effectively addressed by metaheuristic algorithms. However, existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method, resulting in suboptimal solutions that hinder diagnosis and prediction accuracy. This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm (IBQANA) for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms. The proposed IBQANA’s contributions include the Hybrid Binary Operator (HBO) and the Distance-based Binary Search Strategy (DBSS). HBO is designed to convert continuous values into binary solutions, even for values outside the [0, 1] range, ensuring accurate binary mapping. On the other hand, DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence. By combining exploration and exploitation phases based on an adaptive probability function, DBSS effectively avoids local optima. The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets, with feature numbers ranging from 8 to 10,509. IBQANA's effectiveness is evaluated regarding the accuracy, fitness, and selected features and compared with seven binary metaheuristic algorithms. Furthermore, IBQANA is utilized to detect COVID-19. The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets. The proposed method presents a promising solution to the FSS problem in medical data preprocessing. |
| Author | Fatahi, Ali Nadimi-Shahraki, Mohammad H. Zamani, Hoda |
| Author_xml | – sequence: 1 givenname: Ali surname: Fatahi fullname: Fatahi, Ali organization: Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Big Data Research Center, Najafabad Branch, Islamic Azad University – sequence: 2 givenname: Mohammad H. orcidid: 0000-0002-0135-1115 surname: Nadimi-Shahraki fullname: Nadimi-Shahraki, Mohammad H. email: nadimi@iaun.ac.ir, nadimi@ieee.org organization: Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Big Data Research Center, Najafabad Branch, Islamic Azad University – sequence: 3 givenname: Hoda surname: Zamani fullname: Zamani, Hoda organization: Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Big Data Research Center, Najafabad Branch, Islamic Azad University |
| BookMark | eNp9kcuKFDEUhoOMYM_oC7g64DqaW1Wq3JU9M9ow2kir25CqOmkz1KVNUg3tW_jGxmlBcDGrA4f_O5f_vyQX0zwhIS85e80Z02-iEkIWlAlJGVNS0tMTshKFklRwxS_Iipda0LIQ9TNyGeM9Y0UtKrkiv5oJNuMhzEfs4Z2fbDjB58VOaRlpa2NuNkdvJ_hkj35vk58n2B6SH_1PDNAM-zn49H2ENMMOB-wS3DiXiz8i3KJNS0DYLW3EBC7MI3zE3nd2gGub7FtoYL39trmmvIZ13gW7tPSn5-Sps0PEF3_rFfl6e_Nl_YHebd9v1s0d7WRVJiqd1LaWlUJ0stetQ1132AmmVNUj160qlavLruVVbV3NpaiKSqNTbSE0F1pekVfnufn5HwvGZO7nJUx5pZGiKIRiismsqs6qLswxBnSm8-nBhxSsHwxn5k8A5hyAyQGYhwDMKaPiP_QQ_JgNfhySZyhm8bTH8O-qR6jfSEmbAg |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2025_126404 crossref_primary_10_1007_s10586_024_04455_x crossref_primary_10_1007_s10586_024_04666_2 crossref_primary_10_1007_s13042_024_02130_6 crossref_primary_10_1038_s41598_025_90660_6 crossref_primary_10_1007_s00500_025_10445_x crossref_primary_10_1038_s41598_024_57098_8 crossref_primary_10_1007_s42235_024_00580_w crossref_primary_10_1038_s41598_024_59034_2 crossref_primary_10_1007_s10586_024_04750_7 crossref_primary_10_1007_s10586_024_04328_3 crossref_primary_10_1007_s10586_024_04368_9 crossref_primary_10_1007_s10586_024_04441_3 crossref_primary_10_1016_j_chaos_2024_115636 crossref_primary_10_1038_s41598_024_57518_9 crossref_primary_10_1007_s10586_024_04488_2 crossref_primary_10_1007_s11831_023_10037_8 crossref_primary_10_1016_j_swevo_2024_101795 crossref_primary_10_1007_s10586_024_04361_2 crossref_primary_10_1007_s10586_024_04447_x crossref_primary_10_1007_s10586_024_04408_4 crossref_primary_10_1142_S0217732325500191 crossref_primary_10_1007_s10462_024_10793_4 crossref_primary_10_1007_s10462_024_11096_4 crossref_primary_10_1007_s10462_024_10857_5 crossref_primary_10_1007_s11128_025_04787_6 crossref_primary_10_1109_ACCESS_2024_3445269 crossref_primary_10_1007_s13042_024_02216_1 crossref_primary_10_1109_ACCESS_2025_3547057 crossref_primary_10_1007_s42484_024_00201_z crossref_primary_10_1186_s40537_024_00931_8 crossref_primary_10_1007_s10462_024_10729_y crossref_primary_10_3390_a17050172 crossref_primary_10_1038_s41598_024_56919_0 crossref_primary_10_1007_s10586_024_04879_5 |
| Cites_doi | 10.3390/math10152770 10.1109/SIBGRAPI.2012.47 10.1007/978-981-15-3290-0_19 10.1016/j.cie.2021.107250 10.1111/exsy.12666 10.1016/j.eswa.2022.118642 10.1016/j.cma.2022.114616 10.1007/s11831-023-09928-7 10.1016/j.cor.2005.11.017 10.1515/mt-2020-0053 10.1016/j.asoc.2019.03.002 10.1109/CEC.2013.6557555 10.3389/fpubh.2020.00357 10.1016/j.neucom.2015.06.083 10.1016/j.cma.2022.114570 10.3390/pr9091551 10.1016/j.swevo.2023.101304 10.1111/coin.12397 10.1007/s00521-017-2988-6 10.3934/naco.2020017 10.1016/j.asoc.2022.108630 10.1155/2022/3714475 10.1016/j.asoc.2019.105576 10.1016/j.swevo.2012.09.002 10.1016/j.neucom.2022.06.075 10.1016/j.eswa.2023.120367 10.3390/electronics11050831 10.3390/bios12100821 10.3390/math11040862 10.3390/math10152742 10.48550/arXiv.2003.11055 10.1023/A:1012487302797 10.1016/j.advengsoft.2013.12.007 10.1016/j.future.2017.05.044 10.1016/j.knosys.2015.07.006 10.1016/j.swevo.2018.01.001 10.1109/CEC.2010.5586536 10.1023/A:1008202821328 10.6029/smartcr.2014.03.007 10.1007/s00366-020-01268-5 10.1016/j.advengsoft.2016.01.008 10.1016/j.knosys.2019.105277 10.1016/j.eswa.2022.116895 10.1111/exsy.12553 10.1007/s10898-007-9149-x 10.1214/aoms/1177731944 10.1016/j.knosys.2018.05.009 10.1007/b107408 10.3390/e23121637 10.1109/TSMC.1987.4309029 10.1016/j.eswa.2021.116368 10.1016/j.future.2019.02.028 10.1007/s00521-015-1920-1 10.1016/j.patrec.2007.05.011 10.1016/j.future.2023.01.006 10.1109/4235.585893 10.1111/exsy.12992 10.1016/j.procs.2015.09.064 10.1109/ACCESS.2020.3010287 10.1371/journal.pone.0274850 10.1007/s42235-021-0050-y 10.3390/app12063209 10.1109/MCI.2006.329691 10.1109/SSD.2009.4956825 10.1007/s11047-009-9175-3 10.1109/ICAPR.2009.36 10.1007/s12559-022-10099-z 10.3390/electronics8101130 10.1016/j.engappai.2021.104314 10.1109/MIPRO.2015.7160458 10.1016/j.eswa.2020.113377 10.1007/s00521-014-1629-6 10.1038/scientificamerican0792-66 10.1016/j.bspc.2023.104614 10.1371/journal.pone.0278491 10.1016/j.jpdc.2009.09.009 10.1007/s11042-022-11949-6 10.1109/ChinaGrid.2011.17 10.1109/ACCESS.2020.3025164 10.1016/j.enconman.2020.113491 10.3390/app13010564 10.3390/pr9122276 10.1007/s10586-022-03649-5 10.3390/bdcc6040104 10.1109/CEC.2012.6256452 10.1016/j.eswa.2021.115351 10.1016/j.compbiomed.2021.104984 10.1016/j.knosys.2022.108789 10.1016/j.engappai.2014.03.007 10.1016/S0004-3702(97)00043-X 10.1109/CEC.2006.1688716 |
| ContentType | Journal Article |
| Copyright | Jilin University 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Jilin University 2023. |
| Copyright_xml | – notice: Jilin University 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: Jilin University 2023. |
| DBID | AAYXX CITATION 7X7 7XB 8FE 8FG 8FH 8FI ABJCF AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FYUFA GNUQQ HCIFZ L6V LK8 M7P M7S PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS |
| DOI | 10.1007/s42235-023-00433-y |
| DatabaseName | CrossRef Proquest Health and Medical Complete ProQuest Central (purchase pre-March 2016) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Materials Science & Engineering Collection ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Technology collection Natural Science Collection ProQuest One Community College ProQuest Central Health Research Premium Collection ProQuest Central Student SciTech Premium Collection ProQuest Engineering Collection Biological Sciences Biological Science Database Engineering Database ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering collection |
| DatabaseTitle | CrossRef ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection ProQuest Engineering Collection Health Research Premium Collection Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) Engineering Collection Engineering Database ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Biological Science Database ProQuest SciTech Collection ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | ProQuest Central Student |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) Biology |
| EISSN | 2543-2141 |
| EndPage | 446 |
| ExternalDocumentID | 10_1007_s42235_023_00433_y |
| GroupedDBID | --K --M -EM -SC -S~ .~1 0R~ 1B1 1~. 1~5 2B. 2C. 4.4 406 457 4G. 5GY 5VR 5VS 7-5 71M 8P~ 8UJ 92E 92I 92Q 93N AACDK AACTN AAEDT AAEDW AAHNG AAIAL AAIKJ AAJBT AAKOC AALRI AAOAW AASML AATNV AAUYE AAXDM AAXKI AAXUO ABAKF ABDZT ABECU ABFTV ABJNI ABKCH ABMAC ABMQK ABTEG ABTKH ABTMW ABWVN ABXDB ACAOD ACDAQ ACDTI ACGFS ACHSB ACNNM ACOKC ACPIV ACRLP ACRPL ACZOJ ADBBV ADEZE ADKNI ADMUD ADNMO ADRFC ADTZH ADURQ ADYFF AEBSH AECPX AEFQL AEIPS AEKER AEMSY AENEX AESKC AFBBN AFKWA AFQWF AFUIB AGDGC AGHFR AGJBK AGMZJ AGQEE AGRTI AGUBO AGYEJ AHJVU AIAKS AIEXJ AIGIU AIKHN AILAN AITGF AITUG AJOXV AJZVZ AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMKLP AMRAJ AMXSW AMYLF ANKPU AXJTR AXYYD BGNMA BKOJK BLXMC CAJEC CCEZO CEKLB CHBEP CS3 CW9 DPUIP DU5 EBLON EBS EFJIC EJD EO9 EP2 EP3 FA0 FDB FIGPU FINBP FIRID FNLPD FNPLU FSGXE FYGXN GBLVA GGCAI GJIRD HG6 HZ~ IAO IHR IKXTQ ITC IWAJR J-C J1W JJJVA JZLTJ KOM KOV LLZTM M41 M4Y MO0 N9A NPVJJ NQJWS NU0 O-L O9- O9J OAUVE OZT P-8 P-9 P2P PC. PT4 Q-- Q38 RIG RLLFE ROL RSV SDC SDF SDG SES SJYHP SNE SNPRN SOHCF SOJ SPC SRMVM SSLCW SST SSZ STPWE T5K TCJ TGP U1G U5M UOJIU UTJUX VEKWB VFIZW WFFXF ZMTXR 7X7 8FI 9DU AATTM AAYWO AAYXX ABBRH ABDBE ABFSG ABJCF ABRTQ ACLOT ACSTC ACVFH ADCNI AEUPX AEZWR AFDZB AFFHD AFHIU AFKRA AFOHR AFPUW AHPBZ AHWEU AIGII AIIUN AIXLP AKBMS AKYEP ATHPR AYFIA BBNVY BENPR BGLVJ BHPHI CCPQU CITATION EFKBS EFLBG FYUFA HCIFZ HMCUK M7P M7S PHGZM PHGZT PQGLB PTHSS UKHRP ~HD 7XB 8FE 8FG 8FH AZQEC DWQXO GNUQQ L6V LK8 PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c386t-3f37a9384eef3d7bfe79cec20448de17b464f96cb189af91328587ef4b5271273 |
| IEDL.DBID | M7S |
| ISICitedReferencesCount | 42 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001071598500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1672-6529 |
| IngestDate | Sat Oct 25 06:49:31 EDT 2025 Tue Nov 18 20:51:05 EST 2025 Sat Nov 29 02:07:54 EST 2025 Fri Feb 21 02:40:15 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Feature subset selection Bioinspired Binary metaheuristic algorithms Medical datasets Optimization Machine learning |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c386t-3f37a9384eef3d7bfe79cec20448de17b464f96cb189af91328587ef4b5271273 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-0135-1115 |
| PQID | 3255240403 |
| PQPubID | 7435083 |
| PageCount | 21 |
| ParticipantIDs | proquest_journals_3255240403 crossref_citationtrail_10_1007_s42235_023_00433_y crossref_primary_10_1007_s42235_023_00433_y springer_journals_10_1007_s42235_023_00433_y |
| PublicationCentury | 2000 |
| PublicationDate | 20240100 2024-01-00 20240101 |
| PublicationDateYYYYMMDD | 2024-01-01 |
| PublicationDate_xml | – month: 1 year: 2024 text: 20240100 |
| PublicationDecade | 2020 |
| PublicationPlace | Singapore |
| PublicationPlace_xml | – name: Singapore |
| PublicationTitle | Journal of bionics engineering |
| PublicationTitleAbbrev | J Bionic Eng |
| PublicationYear | 2024 |
| Publisher | Springer Nature Singapore Springer Nature B.V |
| Publisher_xml | – name: Springer Nature Singapore – name: Springer Nature B.V |
| References | KarabogaDBasturkBA powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithmJournal of Global Optimization20073934594712346178 KhalidAMHamzaHMMirjaliliSHosnyKMBCOVIDOA: a novel binary coronavirus disease optimization algorithm for feature selectionKnowledge-Based Systems2022248108789354646669014647 FarisHMafarjaMMHeidariAAAljarahIAlamA-ZMirjaliliSFujitaHAn efficient binary salp swarm algorithm with crossover scheme for feature selection problemsKnowledge-Based Systems20181544367 GuyonIElisseeffAAn introduction to variable and feature selectionJournal of Machine Leaning Research200337–811571182 MirjaliliSFarisHAljarahIEvolutionary machine learning techniques2019Springer12 MirjaliliSWangG-GCoelhoLdSBinary optimization using hybrid particle swarm optimization and gravitational search algorithmNeural Computing and Applications201425614231435 PiriJMohapatraPAcharyaBGharehchopoghFSGerogiannisVCKanavosAManikaSFeature selection using artificial gorilla troop optimization for biomedical data: a case analysis with COVID-19 dataMathematics202210152742 YıldızBSPholdeeNBureeratSErdaşMUYıldızARSaitSMComparision of the political optimization algorithm, the Archimedes optimization algorithm and the levy flight algorithm for design optimization in industryMaterials Testing20216343563592021MTest..63..356Y HuangJCaiYXuXA hybrid genetic algorithm for feature selection wrapper based on mutual informationPattern Recognition Letters20072813182518442007PaReL..28.1825H AslanMGunduzMKiranMSJayaX: jaya algorithm with xor operator for binary optimizationApplied Soft Computing201982105576 Tanabe, R., Fukunaga, A. (2013) Success-history based parameter adaptation for differential evolution. IEEE congress on evolutionary computation, Cancun, Mexico, 71–78. XuZHeidariAAKuangFKhalilAMafarjaMZhangSChenHPanZEnhanced Gaussian bare-bones grasshopper optimization: Mitigating the performance concerns for feature selectionExpert Systems with Applications2023212118642 KumarVMinzSFeature selection: a literature reviewSmartCR201443211229 Purohit, A., Chaudhari, NS., Tiwari, A. (2010) Construction of classifier with feature selection based on genetic programming. IEEE Congress on Evolutionary Computation, Barcelona, Spain, 1–5. TuJChenHWangMGandomiAHThe colony predation algorithmJournal of Bionic Engineering2021183674710 RashediENezamabadi-PourHSaryazdiSBGSA: Binary gravitational search algorithmNatural Computing2010937277452685088 HemdanEE-DShoumanMAKararMECovidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray imagesArXiv preprint ArXiv202010.48550/arXiv.2003.11055 SayedGIHassanienAEAzarATFeature selection via a novel chaotic crow search algorithmNeural Computing and Applications2019311171188 TurkogluBUymazSAKayaEBinary artificial algae algorithm for feature selectionApplied Soft Computing2022120108630 YildizBSPholdeeNBureeratSYildizARSaitSMRobust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithmExpert Systems2021383e12666 NamaSSahaAKChakrabortySGandomiAHAbualigahLBoosting particle swarm optimization by backtracking search algorithm for optimization problemsSwarm and Evolutionary Computation202379101304 Nadimi-ShahrakiMHZamaniHDMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimizationExpert Systems with Applications2022198116895 GuptaSDeepKA novel random walk grey wolf optimizerSwarm and Evolutionary Computation20194410111210.1016/j.swevo.2018.01.001 AkinolaOAgushakaOEzugwuABinary dwarf mongoose optimizer for solving high-dimensional feature selection problemsPLoS ONE202217101261:CAS:528:DC%2BB38Xis1Wku7jK10.1371/journal.pone.0274850 Al-QanessMAHelmiAMDahouAElazizMAThe applications of metaheuristics for human activity recognition and fall detection using wearable sensors: A comprehensive analysisBiosensors20221210821362909589599938 AwadAEl-HefnawyNAbdel-kaderHEnhanced particle swarm optimization for task scheduling in cloud computing environmentsProcedia Computer Science201565920929 AgushakaJOEzugwuAEAbualigahLDwarf mongoose optimization algorithmComputer Methods in Applied Mechanics and Engineering20223911145702022CMAME.391k4570A4372742 MirjaliliSDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Computing and Applications201627410531073 ChakrabortySSahaAKChhabraAImproving whale optimization algorithm with elite strategy and its application to engineering-design and cloud task scheduling problemsCognitive Computation202310.1007/s12559-022-10099-z GongTTuson2007AL. Differential Evolution for Binary Encoding. Berlin251262 Nadimi-ShahrakiMHFatahiAZamaniHMirjaliliSAbualigahLAn improved moth-flame optimization algorithm with adaptation mechanism to solve numerical and mechanical engineering problemsEntropy2021231216372021Entrp..23.1637N4356092349459438700729 Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D. (2011) Cloud task scheduling based on load balancing ant colony optimization. Sixth Annual ChinaGrid Conference, Liaoning, China, 3–9. Marandi, A., Afshinmanesh, F., Shahabadi, M., Bahrami, F. (2006) Boolean particle swarm optimization and its application to the design of a dual-band dual-polarized planar antenna. IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada, 2006, 3212–3218. MohammadzadehHGharehchopoghFSA novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detectionComputational Intelligence20213711762094221974 Deriche, M. (2009) Feature selection using ant colony optimization. 6th International Multi-Conference on Systems, Signals and Devices, Djerba, Tunisia, 1–4. Koller, D., Sahami, M. (1996) Toward optimal feature selection. Proceedings of the Thirteenth International Conference on International Conference on Machine Learning. Bari, Italy, 1996: 284–292. WangSJiaHAbualigahLLiuQZhengRAn improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problemsProcesses20219915511:CAS:528:DC%2BB3MXis1SnurzI StornRPriceKDifferential evolution–a simple and efficient heuristic for global optimization over continuous spacesJournal of Global Optimization19971143413591479553 GuptaSAbderazekHYıldızBSYildizARMirjaliliSSaitSMComparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problemsExpert Systems with Applications2021183115351 HelmiAMAl-qanessMADahouAAbd ElazizMHuman activity recognition using marine predators algorithm with deep learningFuture Generation Computer Systems2023142340350 Cervante, L., Xue, B., Zhang, M., Shang, L. (2012) Binary particle swarm optimisation for feature selection: A filter based approach. IEEE Congress on Evolutionary Computation, Brisbane, QLD, Australia, 1–8. Nadimi-ShahrakiMHAsghari VarzanehZZamaniHMirjaliliSBinary starling murmuration optimizer algorithm to select effective features from medical dataApplied Sciences20231315641:CAS:528:DC%2BB3sXmsVCnsQ%3D%3D OmaraFAArafaMMGenetic algorithms for task scheduling problemJournal of Parallel and Distributed Computing2010701132210.1016/j.jpdc.2009.09.009 Nadimi-ShahrakiMHFatahiAZamaniHMirjaliliSOlivaDHybridizing of whale and moth-flame optimization algorithms to solve diverse scales of optimal power flow problemElectronics2022115831 ChowdhuryMERahmanTKhandakarAMazharRKadirMAMahbubZBIslamKRKhanMSIqbalAAl EmadiNCan AI help in screening viral and COVID-19 pneumonia?IEEE Access2020813266513267610.1109/ACCESS.2020.3010287 LiuYHeidariAACaiZLiangGChenHPanZAlsufyaniABourouisSSimulated annealing-based dynamic step shuffled frog leaping algorithm: optimal performance design and feature selectionNeurocomputing2022503325362 Nadimi-ShahrakiMHZamaniHAsghari VarzanehZMirjaliliSA Systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizationsArchives of Computational Methods in Engineering202310.1007/s11831-023-09928-73735974010220350 SnyderSEHusariGSnyderSEThor: A deep learning approach for face mask detection to prevent the COVID-19 pandemicSoutheast Con 2021, Atlanta2021USAGA TooJAbdullahARMohd SaadNA new quadratic binary Harris hawk optimization for feature selectionElectronics20198101130 Nadimi-ShahrakiMHFatahiAZamaniHMirjaliliSBinary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical dataMathematics202210152770 YıldızBSPholdeeNPanagantNBureeratSYildizARSaitSMA novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problemsEngineering with Computers202238287188310.1007/s00366-020-01268-5 MirjaliliSLewisAThe whale optimization algorithmAdvances in Engineering Software2016955167 FaramarziAHeidarinejadMMirjaliliSGandomiAHMarine predators algorithm: a nature-inspired metaheuristicExpert Systems with Applications2020152113377 TahaACosgraveBMckeeverSUsing feature selection with machine learning for generation of insurance insightsApplied Sciences202212632091:CAS:528:DC%2BB38XnvF2jtbk%3D SharmaSSahaAKRoySMirjaliliSNamaSA mixed sine cosine butterfly optimization algorithm for global optimization and its applicationCluster Computing20222564573460010.1007/s10586-022-03649-5 Mirjalili, S., Zhang, H., Mirjalili, S., Chalup, S., Noman, N. (2020) A novel U-shaped transfer function for binary particle swarm optimisation. Soft Computing for Problem Solving 2019, Singapore, 241–259. HeidariAAMirjaliliSFarisHAljarahIMafarjaMChenHHarris hawks optimization: algorithm and applicationsFuture Generation Computer Systems201997849872 DorigoMBirattariMStutzleTAnt colony optimizationIEEE Computational Intelligence Magazine2006142839 Nadimi-ShahrakiMHFatahiAZamaniHMirjaliliSAbualigahLAbd ElazizMMigration-based moth-flame optimization algorithmProcesses20219122276 PelusiDMascellaRTalliniLNayakJNaikBDengYAn improved moth-flame optimization algorithm with hybrid searc ME Chowdhury (433_CR97) 2020; 8 S Wang (433_CR51) 2021; 9 AA Heidari (433_CR29) 2019; 97 SE Snyder (433_CR98) 2021 O Akinola (433_CR15) 2022; 17 S Mirjalili (433_CR26) 2014; 69 BS Yıldız (433_CR42) 2022; 38 FA Omara (433_CR32) 2010; 70 MH Nadimi-Shahraki (433_CR43) 2023; 11 C-J Liao (433_CR73) 2007; 34 J Too (433_CR71) 2019; 8 B Turkoglu (433_CR82) 2022; 120 GI Sayed (433_CR78) 2019; 31 H Faris (433_CR59) 2018; 154 433_CR103 M Abdel-Basset (433_CR50) 2021; 227 AR Jordehi (433_CR77) 2019; 78 D Karaboga (433_CR25) 2007; 39 433_CR30 I Guyon (433_CR12) 2002; 46 BS Yildiz (433_CR39) 2021; 38 MH Nadimi-Shahraki (433_CR89) 2023; 13 A Awad (433_CR31) 2015; 65 A Rakotomamonjy (433_CR13) 2003; 3 S Gupta (433_CR40) 2021; 183 J Piri (433_CR83) 2022; 10 433_CR21 S Chakraborty (433_CR63) 2023 AM Helmi (433_CR86) 2023; 142 E Rashedi (433_CR54) 2010; 9 H Zamani (433_CR47) 2022; 392 C-W Chen (433_CR5) 2020; 37 MH Nadimi-Shahraki (433_CR20) 2021; 9 A Taha (433_CR6) 2022; 12 J Huang (433_CR10) 2007; 28 S Tabakhi (433_CR18) 2014; 32 MH Nadimi-Shahraki (433_CR35) 2021; 23 433_CR17 H Zamani (433_CR49) 2021; 104 O Maimon (433_CR1) 2005 433_CR14 S Mirjalili (433_CR69) 2013; 9 S Mirjalili (433_CR2) 2019 S Mirjalili (433_CR58) 2016; 27 BS Yıldız (433_CR36) 2021; 63 P Bansal (433_CR64) 2022; 81 MA Kahya (433_CR72) 2021; 11 433_CR93 BS Yıldız (433_CR41) 2022; 39 433_CR96 MH Nadimi-Shahraki (433_CR80) 2023 433_CR90 D Pelusi (433_CR91) 2020; 191 R Babukarthik (433_CR99) 2020; 8 S Kumar Sahoo (433_CR101) 2023; 227 R Storn (433_CR23) 1997; 11 AM Khalid (433_CR66) 2022; 248 S Gupta (433_CR92) 2019; 44 R Kohavi (433_CR9) 1997; 97 MH Nadimi-Shahraki (433_CR33) 2022; 11 I Foroutan (433_CR19) 1987; 17 M Farhat (433_CR34) 2022; 2022 L Abualigah (433_CR84) 2022; 192 DH Wolpert (433_CR61) 1997; 1 C Iwendi (433_CR104) 2020; 8 E Emary (433_CR57) 2016; 172 L Abualigah (433_CR46) 2021; 157 A Faramarzi (433_CR44) 2020; 152 433_CR79 S Sharma (433_CR37) 2022; 25 433_CR74 M Aslan (433_CR75) 2019; 82 W Ren (433_CR88) 2023; 83 S Mirjalili (433_CR70) 2014; 25 E-SM El-Kenawy (433_CR52) 2023; 18 433_CR8 JO Agushaka (433_CR48) 2022; 391 433_CR7 433_CR4 S Mirjalili (433_CR27) 2015; 89 433_CR3 MA Al-Qaness (433_CR87) 2022; 12 EE-D Hemdan (433_CR102) 2020 Y He (433_CR60) 2018; 78 MH Nadimi-Shahraki (433_CR68) 2022; 10 R Rao (433_CR76) 2016; 7 V Kumar (433_CR16) 2014; 4 MH Nadimi-Shahraki (433_CR38) 2022; 198 M Dorigo (433_CR24) 2006; 1 M Friedman (433_CR94) 1940; 11 A Shaddeli (433_CR85) 2022; 6 S Chakraborty (433_CR100) 2021; 139 I Guyon (433_CR11) 2003; 3 S Nama (433_CR62) 2023; 79 Y Liu (433_CR65) 2022; 503 433_CR53 433_CR56 H Mohammadzadeh (433_CR81) 2021; 37 S Mirjalili (433_CR28) 2016; 95 Z Xu (433_CR67) 2023; 212 JH Holland (433_CR22) 1992; 267 J Tu (433_CR45) 2021; 18 S Kutsuna (433_CR95) 2021; 4 T Gong (433_CR55) 2007 |
| References_xml | – reference: Nadimi-ShahrakiMHZamaniHDMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimizationExpert Systems with Applications2022198116895 – reference: HeYXieHWongT-LWangXA novel binary artificial bee colony algorithm for the set-union knapsack problemFuture Generation Computer Systems2018787786 – reference: Nadimi-ShahrakiMHFatahiAZamaniHMirjaliliSAbualigahLAn improved moth-flame optimization algorithm with adaptation mechanism to solve numerical and mechanical engineering problemsEntropy2021231216372021Entrp..23.1637N4356092349459438700729 – reference: SharmaSSahaAKRoySMirjaliliSNamaSA mixed sine cosine butterfly optimization algorithm for global optimization and its applicationCluster Computing20222564573460010.1007/s10586-022-03649-5 – reference: Deriche, M. (2009) Feature selection using ant colony optimization. 6th International Multi-Conference on Systems, Signals and Devices, Djerba, Tunisia, 1–4. – reference: Jović, A., Brkić, K., Bogunović, N. (2015) A review of feature selection methods with applications. 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 1200–1205 – reference: Kennedy, J., Eberhart, R. (1948) Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks, 1995, 1942–1948. – reference: Nadimi-ShahrakiMHZamaniHFatahiAMirjaliliSMFO-SFR: An enhanced moth-flame optimization algorithm using an effective stagnation finding and replacing strategyMathematics2023114862 – reference: AgushakaJOEzugwuAEAbualigahLDwarf mongoose optimization algorithmComputer Methods in Applied Mechanics and Engineering20223911145702022CMAME.391k4570A4372742 – reference: AbualigahLYousriDAbd ElazizMEweesAAAl-QanessMAGandomiAHAquila optimizer: a novel meta-heuristic optimization algorithmComputers & Industrial Engineering2021157107250 – reference: DorigoMBirattariMStutzleTAnt colony optimizationIEEE Computational Intelligence Magazine2006142839 – reference: StornRPriceKDifferential evolution–a simple and efficient heuristic for global optimization over continuous spacesJournal of Global Optimization19971143413591479553 – reference: Abdel-BassetMEl-ShahatDChakraborttyRKRyanMParameter estimation of photovoltaic models using an improved marine predators algorithmEnergy Conversion and Management2021227113491 – reference: GuptaSAbderazekHYıldızBSYildizARMirjaliliSSaitSMComparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problemsExpert Systems with Applications2021183115351 – reference: TuJChenHWangMGandomiAHThe colony predation algorithmJournal of Bionic Engineering2021183674710 – reference: Purohit, A., Chaudhari, NS., Tiwari, A. (2010) Construction of classifier with feature selection based on genetic programming. IEEE Congress on Evolutionary Computation, Barcelona, Spain, 1–5. – reference: KumarVMinzSFeature selection: a literature reviewSmartCR201443211229 – reference: FarhatMKamelSAtallahAMKhanBDeveloping a marine predator algorithm for optimal power flow analysis considering uncertainty of renewable energy sourcesInternational Transactions on Electrical Energy Systems20222022371447510.1155/2022/3714475 – reference: ChenC-WTsaiY-HChangF-RLinW-CEnsemble feature selection in medical datasets: combining filter, wrapper, and embedded feature selection resultsExpert Systems.2020375e1255310.1111/exsy.12553 – reference: YıldızBSPholdeeNPanagantNBureeratSYildizARSaitSMA novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problemsEngineering with Computers202238287188310.1007/s00366-020-01268-5 – reference: SayedGIHassanienAEAzarATFeature selection via a novel chaotic crow search algorithmNeural Computing and Applications2019311171188 – reference: PelusiDMascellaRTalliniLNayakJNaikBDengYAn improved moth-flame optimization algorithm with hybrid search phaseKnowledge-Based Systems2020191105277 – reference: AbualigahLDiabatAChaotic binary group search optimizer for feature selectionExpert Systems with Applications2022192116368 – reference: WangSJiaHAbualigahLLiuQZhengRAn improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problemsProcesses20219915511:CAS:528:DC%2BB3MXis1SnurzI – reference: RaoRJaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problemsInternational Journal of Industrial Engineering Computations2016711934 – reference: Kumar SahooSHousseinEHPremkumarMKumar SahaAEmamMMSelf-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentationExpert Systems with Applications202322712036710.1016/j.eswa.2023.1203673719300010163947 – reference: Duangsoithong, R., Windeatt, T. (2009) Relevant and redundant feature analysis with ensemble classification. Seventh International Conference on Advances in Pattern Recognition, Kolkata, India 247–250 – reference: GongTTuson2007AL. Differential Evolution for Binary Encoding. Berlin251262 – reference: Nadimi-ShahrakiMHFatahiAZamaniHMirjaliliSBinary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical dataMathematics202210152770 – reference: TheWorldometers: COVID-19 Coronavirus Pandemic. Retrieved 24 Sep 2022 from https://www.worldometers.info/coronavirus/ – reference: MirjaliliSMoth-flame optimization algorithm: A novel nature-inspired heuristic paradigmKnowledge-Based Systems201589228249 – reference: HeidariAAMirjaliliSFarisHAljarahIMafarjaMChenHHarris hawks optimization: algorithm and applicationsFuture Generation Computer Systems201997849872 – reference: RakotomamonjyAVariable selection using SVM-based criteriaJournal of Machine Learning Research200337–8135713702020764 – reference: NamaSSahaAKChakrabortySGandomiAHAbualigahLBoosting particle swarm optimization by backtracking search algorithm for optimization problemsSwarm and Evolutionary Computation202379101304 – reference: Nadimi-ShahrakiMHFatahiAZamaniHMirjaliliSAbualigahLAbd ElazizMMigration-based moth-flame optimization algorithmProcesses20219122276 – reference: MirjaliliSFarisHAljarahIEvolutionary machine learning techniques2019Springer12 – reference: Koller, D., Sahami, M. (1996) Toward optimal feature selection. Proceedings of the Thirteenth International Conference on International Conference on Machine Learning. Bari, Italy, 1996: 284–292. – reference: TurkogluBUymazSAKayaEBinary artificial algae algorithm for feature selectionApplied Soft Computing2022120108630 – reference: ChakrabortySSahaAKNamaSDebnathSCOVID-19 X-ray image segmentation by modified whale optimization algorithm with population reductionComputers in Biology and Medicine20211391049841:CAS:528:DC%2BB3MXisVehsrvO10.1016/j.compbiomed.2021.104984347399728556692 – reference: JordehiARBinary particle swarm optimisation with quadratic transfer function: A new binary optimisation algorithm for optimal scheduling of appliances in smart homesApplied Soft Computing201978465480 – reference: OmaraFAArafaMMGenetic algorithms for task scheduling problemJournal of Parallel and Distributed Computing2010701132210.1016/j.jpdc.2009.09.009 – reference: PiriJMohapatraPAcharyaBGharehchopoghFSGerogiannisVCKanavosAManikaSFeature selection using artificial gorilla troop optimization for biomedical data: a case analysis with COVID-19 dataMathematics202210152742 – reference: Marandi, A., Afshinmanesh, F., Shahabadi, M., Bahrami, F. (2006) Boolean particle swarm optimization and its application to the design of a dual-band dual-polarized planar antenna. IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada, 2006, 3212–3218. – reference: Kennedy, J., Eberhart, RC. (1997) A discrete binary version of the particle swarm algorithm. IEEE International Conference on Systems, Man, and Cybernetics. Computational cybernetics and simulation, Orlando, FL, USA, 1997, 4104–4108. – reference: AwadAEl-HefnawyNAbdel-kaderHEnhanced particle swarm optimization for task scheduling in cloud computing environmentsProcedia Computer Science201565920929 – reference: GuyonIElisseeffAAn introduction to variable and feature selectionJournal of Machine Leaning Research200337–811571182 – reference: GuptaSDeepKA novel random walk grey wolf optimizerSwarm and Evolutionary Computation20194410111210.1016/j.swevo.2018.01.001 – reference: BabukarthikRAdigaVAKSambasivamGChandramohanDAmudhavelJPrediction of COVID-19 using genetic deep learning convolutional neural network (GDCNN)IEEE Access202081776471776661:STN:280:DC%2BB2cfjvFSisA%3D%3D10.1109/ACCESS.2020.302516434786292 – reference: Kelly, M., Longjohn, R., Nottingham, K. The UCI machine learning repository. Retrieved April 1, 2022, from https://archive.ics.uci.edu – reference: Tanabe, R., Fukunaga, A. (2013) Success-history based parameter adaptation for differential evolution. IEEE congress on evolutionary computation, Cancun, Mexico, 71–78. – reference: HemdanEE-DShoumanMAKararMECovidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray imagesArXiv preprint ArXiv202010.48550/arXiv.2003.11055 – reference: GuyonIWestonJBarnhillSVapnikVGene selection for cancer classification using support vector machinesMachINE Learning200246138942210.1023/A:1012487302797 – reference: YildizBSPholdeeNBureeratSYildizARSaitSMRobust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithmExpert Systems2021383e12666 – reference: FaramarziAHeidarinejadMMirjaliliSGandomiAHMarine predators algorithm: a nature-inspired metaheuristicExpert Systems with Applications2020152113377 – reference: RenWBashkandiAHJahanshahiJAAlHamadAQMJavaheriDMohammadiMBrain tumor diagnosis using a step-by-step methodology based on courtship learning-based water strider algorithmBiomedical Signal Processing and Control202383104614 – reference: Nadimi-ShahrakiMHFatahiAZamaniHMirjaliliSOlivaDHybridizing of whale and moth-flame optimization algorithms to solve diverse scales of optimal power flow problemElectronics2022115831 – reference: ChowdhuryMERahmanTKhandakarAMazharRKadirMAMahbubZBIslamKRKhanMSIqbalAAl EmadiNCan AI help in screening viral and COVID-19 pneumonia?IEEE Access2020813266513267610.1109/ACCESS.2020.3010287 – reference: TahaACosgraveBMckeeverSUsing feature selection with machine learning for generation of insurance insightsApplied Sciences202212632091:CAS:528:DC%2BB38XnvF2jtbk%3D – reference: LiuYHeidariAACaiZLiangGChenHPanZAlsufyaniABourouisSSimulated annealing-based dynamic step shuffled frog leaping algorithm: optimal performance design and feature selectionNeurocomputing2022503325362 – reference: YıldızBSKumarSPholdeeNBureeratSSaitSMYildizARA new chaotic Lévy flight distribution optimization algorithm for solving constrained engineering problemsExpert Systems2022398e12992 – reference: Nadimi-ShahrakiMHAsghari VarzanehZZamaniHMirjaliliSBinary starling murmuration optimizer algorithm to select effective features from medical dataApplied Sciences20231315641:CAS:528:DC%2BB3sXmsVCnsQ%3D%3D – reference: MirjaliliSLewisAS-shaped versus V-shaped transfer functions for binary particle swarm optimizationSwarm and Evolutionary Computation20139114 – reference: KahyaMAAltamirSAAlgamalZYImproving whale optimization algorithm for feature selection with a time-varying transfer functionNumerical Algebra, Control & Optimization2021111874236695 – reference: SnyderSEHusariGSnyderSEThor: A deep learning approach for face mask detection to prevent the COVID-19 pandemicSoutheast Con 2021, Atlanta2021USAGA – reference: ShaddeliASoleimanian GharehchopoghFMasdariMSoloukVAn improved african vulture optimization algorithm for feature selection problems and its application of sentiment analysis on movie reviewsBig Data and Cognitive Computing202264104 – reference: IwendiCBashirAKPeshkarASujathaRChatterjeeJMPasupuletiSMishraRPillaiSJoOCOVID-19 patient health prediction using boosted random forest algorithmFrontiers in Public Health20208357327197677350612 – reference: FriedmanMA comparison of alternative tests of significance for the problem of m rankingsThe Annals of Mathematical Statistics194011186922085 – reference: El-KenawyE-SMMirjaliliSKhodadadiNAbdelhamidAAEidMMEl-SaidMIbrahimAFeature selection in wind speed forecasting systems based on meta-heuristic optimizationPLoS ONE2023182e02784911:CAS:528:DC%2BB3sXjt1egsL0%3D367497449904490 – reference: WolpertDHMacreadyWGNo free lunch theorems for optimizationIEEE Transactions on Evolutionary Computation1997116782 – reference: ForoutanISklanskyJFeature selection for automatic classification of non-gaussian dataIEEE Transactions on Systems, Man, and Cybernetics1987172187198 – reference: EmaryEZawbaaHMHassanienAEBinary grey wolf optimization approaches for feature selectionNeurocomputing2016172371381 – reference: Nakamura, RYM., Pereira, LAM., Costa, KA., Rodrigues, D., Papa, JP., Yang, XS. (2012) BBA: A Binary Bat Algorithm for Feature Selection. 25th SIBGRAPI Conference on Graphics, Patterns and Images, Ouro Preto, Brazil, 2012, 291–297. – reference: MirjaliliSMirjaliliSMLewisAGrey wolf optimizerAdvances in Engineering Software2014694661 – reference: AkinolaOAgushakaOEzugwuABinary dwarf mongoose optimizer for solving high-dimensional feature selection problemsPLoS ONE202217101261:CAS:528:DC%2BB38Xis1Wku7jK10.1371/journal.pone.0274850 – reference: AslanMGunduzMKiranMSJayaX: jaya algorithm with xor operator for binary optimizationApplied Soft Computing201982105576 – reference: MirjaliliSDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Computing and Applications201627410531073 – reference: ChakrabortySSahaAKChhabraAImproving whale optimization algorithm with elite strategy and its application to engineering-design and cloud task scheduling problemsCognitive Computation202310.1007/s12559-022-10099-z – reference: HelmiAMAl-qanessMADahouAAbd ElazizMHuman activity recognition using marine predators algorithm with deep learningFuture Generation Computer Systems2023142340350 – reference: KhalidAMHamzaHMMirjaliliSHosnyKMBCOVIDOA: a novel binary coronavirus disease optimization algorithm for feature selectionKnowledge-Based Systems2022248108789354646669014647 – reference: MirjaliliSLewisAThe whale optimization algorithmAdvances in Engineering Software2016955167 – reference: N.C. Virus, Dataset, Kaggle. Retrieved 25 September 2020 from https://www.kaggle.com/datasets/sudalairajkumar/novel-corona-virus-2019-dataset – reference: ZamaniHNadimi-ShahrakiMHGandomiAHQANA: Quantum-based avian navigation optimizer algorithmEngineering Applications of Artificial Intelligence2021104104314 – reference: Nadimi-ShahrakiMHZamaniHAsghari VarzanehZMirjaliliSA Systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizationsArchives of Computational Methods in Engineering202310.1007/s11831-023-09928-73735974010220350 – reference: KohaviRJohnGHWrappers for feature subset selectionArtificial Intelligence199797127332410.1016/S0004-3702(97)00043-X – reference: Cervante, L., Xue, B., Zhang, M., Shang, L. (2012) Binary particle swarm optimisation for feature selection: A filter based approach. IEEE Congress on Evolutionary Computation, Brisbane, QLD, Australia, 1–8. – reference: TabakhiSMoradiPAkhlaghianFAn unsupervised feature selection algorithm based on ant colony optimizationEngineering Applications of Artificial Intelligence201432112123 – reference: MirjaliliSWangG-GCoelhoLdSBinary optimization using hybrid particle swarm optimization and gravitational search algorithmNeural Computing and Applications201425614231435 – reference: Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D. (2011) Cloud task scheduling based on load balancing ant colony optimization. Sixth Annual ChinaGrid Conference, Liaoning, China, 3–9. – reference: TooJAbdullahARMohd SaadNA new quadratic binary Harris hawk optimization for feature selectionElectronics20198101130 – reference: Mirjalili, S., Zhang, H., Mirjalili, S., Chalup, S., Noman, N. (2020) A novel U-shaped transfer function for binary particle swarm optimisation. Soft Computing for Problem Solving 2019, Singapore, 241–259. – reference: Al-QanessMAHelmiAMDahouAElazizMAThe applications of metaheuristics for human activity recognition and fall detection using wearable sensors: A comprehensive analysisBiosensors20221210821362909589599938 – reference: FarisHMafarjaMMHeidariAAAljarahIAlamA-ZMirjaliliSFujitaHAn efficient binary salp swarm algorithm with crossover scheme for feature selection problemsKnowledge-Based Systems20181544367 – reference: XuZHeidariAAKuangFKhalilAMafarjaMZhangSChenHPanZEnhanced Gaussian bare-bones grasshopper optimization: Mitigating the performance concerns for feature selectionExpert Systems with Applications2023212118642 – reference: ZamaniHNadimi-ShahrakiMHGandomiAHStarling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimizationComputer Methods in Applied Mechanics and Engineering20223921146162022CMAME.392k4616Z4379773 – reference: MohammadzadehHGharehchopoghFSA novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detectionComputational Intelligence20213711762094221974 – reference: LiaoC-JTsengC-TLuarnPA discrete version of particle swarm optimization for flowshop scheduling problemsComputers & Operations Research2007341030993111 – reference: YıldızBSPholdeeNBureeratSErdaşMUYıldızARSaitSMComparision of the political optimization algorithm, the Archimedes optimization algorithm and the levy flight algorithm for design optimization in industryMaterials Testing20216343563592021MTest..63..356Y – reference: MaimonORokachLData mining and knowledge discovery handbook2005Springer1 – reference: HuangJCaiYXuXA hybrid genetic algorithm for feature selection wrapper based on mutual informationPattern Recognition Letters20072813182518442007PaReL..28.1825H – reference: HollandJHGenetic algorithmsScientific American1992267166731992SciAm.267A..66H – reference: RashediENezamabadi-PourHSaryazdiSBGSA: Binary gravitational search algorithmNatural Computing2010937277452685088 – reference: BansalPGehlotKSinghalAGuptaAAutomatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithmMultimedia Tools and Applications202281688078834351536208818505 – reference: KarabogaDBasturkBA powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithmJournal of Global Optimization20073934594712346178 – reference: KutsunaSClinical manifestations of coronavirus disease 2019Japan Medical Association2021427680 – volume: 10 start-page: 2770 issue: 15 year: 2022 ident: 433_CR68 publication-title: Mathematics doi: 10.3390/math10152770 – ident: 433_CR56 doi: 10.1109/SIBGRAPI.2012.47 – ident: 433_CR79 doi: 10.1007/978-981-15-3290-0_19 – volume: 157 start-page: 107250 year: 2021 ident: 433_CR46 publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2021.107250 – volume: 38 start-page: e12666 issue: 3 year: 2021 ident: 433_CR39 publication-title: Expert Systems doi: 10.1111/exsy.12666 – volume: 212 start-page: 118642 year: 2023 ident: 433_CR67 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2022.118642 – volume: 392 start-page: 114616 year: 2022 ident: 433_CR47 publication-title: Computer Methods in Applied Mechanics and Engineering doi: 10.1016/j.cma.2022.114616 – year: 2023 ident: 433_CR80 publication-title: Archives of Computational Methods in Engineering doi: 10.1007/s11831-023-09928-7 – volume: 34 start-page: 3099 issue: 10 year: 2007 ident: 433_CR73 publication-title: Computers & Operations Research doi: 10.1016/j.cor.2005.11.017 – volume: 63 start-page: 356 issue: 4 year: 2021 ident: 433_CR36 publication-title: Materials Testing doi: 10.1515/mt-2020-0053 – volume: 78 start-page: 465 year: 2019 ident: 433_CR77 publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2019.03.002 – ident: 433_CR90 doi: 10.1109/CEC.2013.6557555 – volume: 8 start-page: 357 year: 2020 ident: 433_CR104 publication-title: Frontiers in Public Health doi: 10.3389/fpubh.2020.00357 – volume: 172 start-page: 371 year: 2016 ident: 433_CR57 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.06.083 – volume: 391 start-page: 114570 year: 2022 ident: 433_CR48 publication-title: Computer Methods in Applied Mechanics and Engineering doi: 10.1016/j.cma.2022.114570 – volume: 9 start-page: 1551 issue: 9 year: 2021 ident: 433_CR51 publication-title: Processes doi: 10.3390/pr9091551 – ident: 433_CR103 – start-page: 12 volume-title: Evolutionary machine learning techniques year: 2019 ident: 433_CR2 – volume: 79 start-page: 101304 year: 2023 ident: 433_CR62 publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2023.101304 – volume: 37 start-page: 176 issue: 1 year: 2021 ident: 433_CR81 publication-title: Computational Intelligence doi: 10.1111/coin.12397 – volume: 31 start-page: 171 issue: 1 year: 2019 ident: 433_CR78 publication-title: Neural Computing and Applications doi: 10.1007/s00521-017-2988-6 – volume: 11 start-page: 87 issue: 1 year: 2021 ident: 433_CR72 publication-title: Numerical Algebra, Control & Optimization doi: 10.3934/naco.2020017 – volume: 120 start-page: 108630 year: 2022 ident: 433_CR82 publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2022.108630 – volume: 2022 start-page: 3714475 year: 2022 ident: 433_CR34 publication-title: International Transactions on Electrical Energy Systems doi: 10.1155/2022/3714475 – volume: 82 start-page: 105576 year: 2019 ident: 433_CR75 publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2019.105576 – volume: 9 start-page: 1 year: 2013 ident: 433_CR69 publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2012.09.002 – ident: 433_CR96 – volume: 503 start-page: 325 year: 2022 ident: 433_CR65 publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.06.075 – ident: 433_CR53 – volume: 227 start-page: 120367 year: 2023 ident: 433_CR101 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.120367 – volume: 11 start-page: 831 issue: 5 year: 2022 ident: 433_CR33 publication-title: Electronics doi: 10.3390/electronics11050831 – volume: 12 start-page: 821 issue: 10 year: 2022 ident: 433_CR87 publication-title: Biosensors doi: 10.3390/bios12100821 – volume: 11 start-page: 862 issue: 4 year: 2023 ident: 433_CR43 publication-title: Mathematics doi: 10.3390/math11040862 – volume: 10 start-page: 2742 issue: 15 year: 2022 ident: 433_CR83 publication-title: Mathematics doi: 10.3390/math10152742 – year: 2020 ident: 433_CR102 publication-title: ArXiv preprint ArXiv doi: 10.48550/arXiv.2003.11055 – volume: 46 start-page: 389 issue: 1 year: 2002 ident: 433_CR12 publication-title: MachINE Learning doi: 10.1023/A:1012487302797 – volume: 69 start-page: 46 year: 2014 ident: 433_CR26 publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – ident: 433_CR93 – start-page: 251 volume-title: Tuson year: 2007 ident: 433_CR55 – volume: 78 start-page: 77 year: 2018 ident: 433_CR60 publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2017.05.044 – volume: 89 start-page: 228 year: 2015 ident: 433_CR27 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.07.006 – volume: 44 start-page: 101 year: 2019 ident: 433_CR92 publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2018.01.001 – ident: 433_CR14 doi: 10.1109/CEC.2010.5586536 – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 433_CR23 publication-title: Journal of Global Optimization doi: 10.1023/A:1008202821328 – volume: 4 start-page: 211 issue: 3 year: 2014 ident: 433_CR16 publication-title: SmartCR doi: 10.6029/smartcr.2014.03.007 – volume: 38 start-page: 871 issue: 2 year: 2022 ident: 433_CR42 publication-title: Engineering with Computers doi: 10.1007/s00366-020-01268-5 – volume: 95 start-page: 51 year: 2016 ident: 433_CR28 publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – volume: 191 start-page: 105277 year: 2020 ident: 433_CR91 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.105277 – volume: 198 start-page: 116895 year: 2022 ident: 433_CR38 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2022.116895 – volume: 37 start-page: e12553 issue: 5 year: 2020 ident: 433_CR5 publication-title: Expert Systems. doi: 10.1111/exsy.12553 – volume: 39 start-page: 459 issue: 3 year: 2007 ident: 433_CR25 publication-title: Journal of Global Optimization doi: 10.1007/s10898-007-9149-x – ident: 433_CR17 – volume: 11 start-page: 86 issue: 1 year: 1940 ident: 433_CR94 publication-title: The Annals of Mathematical Statistics doi: 10.1214/aoms/1177731944 – volume: 154 start-page: 43 year: 2018 ident: 433_CR59 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2018.05.009 – start-page: 1 volume-title: Data mining and knowledge discovery handbook year: 2005 ident: 433_CR1 doi: 10.1007/b107408 – volume: 23 start-page: 1637 issue: 12 year: 2021 ident: 433_CR35 publication-title: Entropy doi: 10.3390/e23121637 – volume: 17 start-page: 187 issue: 2 year: 1987 ident: 433_CR19 publication-title: IEEE Transactions on Systems, Man, and Cybernetics doi: 10.1109/TSMC.1987.4309029 – volume: 192 start-page: 116368 year: 2022 ident: 433_CR84 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.116368 – volume: 97 start-page: 849 year: 2019 ident: 433_CR29 publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2019.02.028 – volume: 27 start-page: 1053 issue: 4 year: 2016 ident: 433_CR58 publication-title: Neural Computing and Applications doi: 10.1007/s00521-015-1920-1 – volume: 28 start-page: 1825 issue: 13 year: 2007 ident: 433_CR10 publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2007.05.011 – volume: 142 start-page: 340 year: 2023 ident: 433_CR86 publication-title: Future Generation Computer Systems doi: 10.1016/j.future.2023.01.006 – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 433_CR61 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585893 – volume: 39 start-page: e12992 issue: 8 year: 2022 ident: 433_CR41 publication-title: Expert Systems doi: 10.1111/exsy.12992 – volume-title: Southeast Con 2021, Atlanta year: 2021 ident: 433_CR98 – volume: 65 start-page: 920 year: 2015 ident: 433_CR31 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.09.064 – volume: 8 start-page: 132665 year: 2020 ident: 433_CR97 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3010287 – volume: 17 start-page: 1 issue: 10 year: 2022 ident: 433_CR15 publication-title: PLoS ONE doi: 10.1371/journal.pone.0274850 – volume: 18 start-page: 674 issue: 3 year: 2021 ident: 433_CR45 publication-title: Journal of Bionic Engineering doi: 10.1007/s42235-021-0050-y – volume: 12 start-page: 3209 issue: 6 year: 2022 ident: 433_CR6 publication-title: Applied Sciences doi: 10.3390/app12063209 – volume: 1 start-page: 28 issue: 4 year: 2006 ident: 433_CR24 publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2006.329691 – ident: 433_CR7 doi: 10.1109/SSD.2009.4956825 – volume: 9 start-page: 727 issue: 3 year: 2010 ident: 433_CR54 publication-title: Natural Computing doi: 10.1007/s11047-009-9175-3 – ident: 433_CR3 doi: 10.1109/ICAPR.2009.36 – year: 2023 ident: 433_CR63 publication-title: Cognitive Computation doi: 10.1007/s12559-022-10099-z – volume: 8 start-page: 1130 issue: 10 year: 2019 ident: 433_CR71 publication-title: Electronics doi: 10.3390/electronics8101130 – volume: 104 start-page: 104314 year: 2021 ident: 433_CR49 publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2021.104314 – ident: 433_CR4 doi: 10.1109/MIPRO.2015.7160458 – volume: 152 start-page: 113377 year: 2020 ident: 433_CR44 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113377 – volume: 25 start-page: 1423 issue: 6 year: 2014 ident: 433_CR70 publication-title: Neural Computing and Applications doi: 10.1007/s00521-014-1629-6 – volume: 267 start-page: 66 issue: 1 year: 1992 ident: 433_CR22 publication-title: Scientific American doi: 10.1038/scientificamerican0792-66 – ident: 433_CR21 – volume: 83 start-page: 104614 year: 2023 ident: 433_CR88 publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2023.104614 – volume: 3 start-page: 1157 issue: 7–8 year: 2003 ident: 433_CR11 publication-title: Journal of Machine Leaning Research – volume: 18 start-page: e0278491 issue: 2 year: 2023 ident: 433_CR52 publication-title: PLoS ONE doi: 10.1371/journal.pone.0278491 – volume: 70 start-page: 13 issue: 1 year: 2010 ident: 433_CR32 publication-title: Journal of Parallel and Distributed Computing doi: 10.1016/j.jpdc.2009.09.009 – volume: 81 start-page: 8807 issue: 6 year: 2022 ident: 433_CR64 publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-022-11949-6 – ident: 433_CR30 doi: 10.1109/ChinaGrid.2011.17 – volume: 8 start-page: 177647 year: 2020 ident: 433_CR99 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3025164 – volume: 4 start-page: 76 issue: 2 year: 2021 ident: 433_CR95 publication-title: Japan Medical Association – volume: 227 start-page: 113491 year: 2021 ident: 433_CR50 publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2020.113491 – volume: 13 start-page: 564 issue: 1 year: 2023 ident: 433_CR89 publication-title: Applied Sciences doi: 10.3390/app13010564 – volume: 9 start-page: 2276 issue: 12 year: 2021 ident: 433_CR20 publication-title: Processes doi: 10.3390/pr9122276 – volume: 25 start-page: 4573 issue: 6 year: 2022 ident: 433_CR37 publication-title: Cluster Computing doi: 10.1007/s10586-022-03649-5 – volume: 6 start-page: 104 issue: 4 year: 2022 ident: 433_CR85 publication-title: Big Data and Cognitive Computing doi: 10.3390/bdcc6040104 – volume: 3 start-page: 1357 issue: 7–8 year: 2003 ident: 433_CR13 publication-title: Journal of Machine Learning Research – volume: 7 start-page: 19 issue: 1 year: 2016 ident: 433_CR76 publication-title: International Journal of Industrial Engineering Computations – ident: 433_CR8 doi: 10.1109/CEC.2012.6256452 – volume: 183 start-page: 115351 year: 2021 ident: 433_CR40 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.115351 – volume: 139 start-page: 104984 year: 2021 ident: 433_CR100 publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2021.104984 – volume: 248 start-page: 108789 year: 2022 ident: 433_CR66 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2022.108789 – volume: 32 start-page: 112 year: 2014 ident: 433_CR18 publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2014.03.007 – volume: 97 start-page: 273 issue: 1 year: 1997 ident: 433_CR9 publication-title: Artificial Intelligence doi: 10.1016/S0004-3702(97)00043-X – ident: 433_CR74 doi: 10.1109/CEC.2006.1688716 |
| SSID | ssj0059283 |
| Score | 2.495269 |
| Snippet | Feature Subset Selection (FSS) is an NP-hard problem to remove redundant and irrelevant features particularly from medical data, and it can be effectively... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 426 |
| SubjectTerms | Algorithms Artificial Intelligence Biochemical Engineering Bioinformatics Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Convergence COVID-19 Datasets Effectiveness Engineering Heuristic methods Navigation Operators (mathematics) Preprocessing Research Article Search methods Transfer functions |
| SummonAdditionalLinks | – databaseName: Springer LINK dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Nb9MwFLdggMQOwMoQhQ29AwcQWGrib25ZtwkuHTCYdoucxIZKa4rStFL3X_AfYztONxAgwTnOS-T37ef3ewg915oJXTKCR1JTTCtpsZRlghm3XFSKe4j1MGxCTCby_Fy9j01hi_62e1-SDJZ60-xGnSfz3cSOrkfdwuub6JZzd9Kr48fTs97-MpUG8M2EixRzlqrYKvN7Gj-7o6sY85eyaPA2x_f_7z8foHsxuoSsE4cddMPUA3Snmze5HqDta-iDA7QT9XoBLyL49MuH6HtWQ3fQYCo4CM268GHptn85w97jVZCtnETBRK8COMe8hhNndWbTS9NAdvFl3kzbrzNo53AaJuxAB4_sbCr4aHPZGPC2yrTgG1sg1ongULf6DWQwPjl7d4gTBWP3LfCXHNe76PPx0afxWxzHNuCSSN5iYonQikhqjCWVKKwRqjRlOnKZYGUSUVBOreJlkUilrXLpsGRSGEsLlorEhVOP0FY9r81jBEmhlRGcV7IgbltpQZi2TLi0yzrHO-JDlPTcy8uIae5Ha1zkGzTmwI3ccSMP3MjXQ_Rq8863DtHjr6v3eqHIo3YvcuLyMBcJ0REZote9EFw9_jO1J_-2_Cm6m7oYqjvx2UNbbbM0--h2uWqni-ZZkPofQ8H59g priority: 102 providerName: Springer Nature |
| Title | An Improved Binary Quantum-based Avian Navigation Optimizer Algorithm to Select Effective Feature Subset from Medical Data: A COVID-19 Case Study |
| URI | https://link.springer.com/article/10.1007/s42235-023-00433-y https://www.proquest.com/docview/3255240403 |
| Volume | 21 |
| WOSCitedRecordID | wos001071598500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2543-2141 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0059283 issn: 1672-6529 databaseCode: M7P dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2543-2141 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0059283 issn: 1672-6529 databaseCode: M7S dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2543-2141 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0059283 issn: 1672-6529 databaseCode: 7X7 dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2543-2141 dateEnd: 20241207 omitProxy: false ssIdentifier: ssj0059283 issn: 1672-6529 databaseCode: BENPR dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: Springer LINK customDbUrl: eissn: 2543-2141 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0059283 issn: 1672-6529 databaseCode: RSV dateStart: 20040301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fb9MwED6xDSReBhtMlI3qHngAgUUTJ_7BC8q6TfDSlRWmvkVOYsOkNR1tWqn8F_zH2I67CiT2wotfkjiWvvPd-c73HcBLpVKuypSSnlAJSSphiBBlRFJmGK8kcxTrvtkEHwzEeCyHIeA2D9cq1zrRK-pqWroY-TtqfV9rfZIe_XDzg7iuUS67GlpobMGOY0mI_dW90VoTpzL2NJwR4zFhaSxD0YwvnUusXXS1yXaVjsOLrP40TBtv868Eqbc7Z4_-d8WPYTd4nJi1IrIH93S9Dw_aHpSrfdgLu3uOrwIF9esn8CursQ036AqPfckufl5YEBYT4uxehdnSyhUO1NJTdExrPLe6Z3L1U88wu_5ml9F8n2AzxZHvs4MtSbLVrOh8zsVMo9NYukFX3oIhW4QnqlHvMcP--eWnExJJ7Nt_obvquHoKX89Ov_Q_ktC8gZRUsIZQQ7mSVCRaG1rxwmguS13GPXserHTEi4QlRrKyiIRURtpDsUgF1yYp0phH1qk6gO16WutngFGhpOaMVaKgFrakoKkyKbeHL2PNb491IFojl5eB2dw12LjObzmZPdq5RTv3aOerDry5_eam5fW48-2jNcR52OPzfINvB96uhWTz-N-zPb97tkN4GFvPqY3zHMF2M1voF3C_XDZX81kXtviYd2Hn-HQwvOg6eR_6cWTHi9Hlb_LDAzo |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFLbGAMELsMFEYcB5AAkE1pqbL0gIhZZp1UYHYkN9C05iw6Q1HW1aFP4Ff4TfyLGTtAKJve2B5ySO4nw-F_uc7yPksVIRV1kU0K5QIQ1zYagQmUcjZhjPJbMU605sgg-HYjSS79fIr7YXxpZVtjbRGep8ktk98p0AY1_0PmE3eH32jVrVKHu62kpo1LDY19V3TNlmrwZ9_L9PfH_37VFvjzaqAjQLBCtpYAKuZCBCrU2Q89RoLjOd-V1MVHLt8TRkoZEsSz0hlZGYrYlIcG3CNPK5h94ex71ELqMd57aEjI-WCV4kfUf76THuUxb5smnSca16Ifph2wuNs2I5w2j1pyNcRbd_Hcg6P7d783-boVvkRhNRQ1wvgQ2ypotNcrXW2Kw2yUZjvWbwtKHYfnab_IwLqLdTdA5vXEsyfJgjyOZjav16DvEC1w0M1cJRkEwKOETbOj75oacQn37Bzy6_jqGcwEenIwQ1CTR6DrAx9XyqwVpkXYJt34HmNAz6qlQvIYbe4adBn3oSevgusKWc1R1yfCGTtEXWi0mh7xLwUiU1ZywXaYAwCdMgUibimFwaDC-6rEO8FilJ1jC3WwGR02TJOe3QlSC6EoeupOqQ58tnzmreknPv3m4hlTQ2bJas8NQhL1pQri7_e7R754_2iFzbO3p3kBwMhvv3yXUfo8R6T2ubrJfTuX5ArmSL8mQ2fehWF5DPFw3W32mOWyY |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bb9MwFLZgXAQPwAqIwoDzwAMIrDXxnbfQUjGBuqHBtLfISeyt0ppOaVqp_Av-MbaTdAMBEuI5jh3lHJ-Lj7_vIPRCayZ0zggeSE0xLaTFUuYRZtxyUSjuKdZDswkxmcjjY3VwCcUfbrt3JckG0-BZmsp697ywuxvgG3VezSOL3RqegQuvr6Jr1DcN8vn64VFni5mKAxFnxEWMOYtVC5v5_Rw_u6aLePOXEmnwPOO7___N99CdNuqEpFGTbXTFlD10o-lDue6h25dYCXtou93vC3jZklK_uo--JyU0BxCmgHcBxAufl04syxn2nrCAZOU0DSZ6FUg75iXsO2s0m34zFSRnJ_NqWp_OoJ7DYei8Aw1tsrO14KPQZWXA2zBTgwe8QFs_gpGu9VtIYLh_tDfCkYKhWwv85cf1A_R1_P7L8ANu2zngnEheY2KJ0IpIaowlhcisESo3eTxwGWJhIpFRTq3ieRZJpa1yabJkUhhLMxaLyIVZD9FWOS_NIwRRppURnBcyI-630owwbZlw6Zh1DnnA-yjqJJnmLde5b7lxlm5YmoM0UieNNEgjXffR68075w3Tx19H73QKkra7fpESl5-5CIkOSB-96RTi4vGfZ3v8b8Ofo5sHo3H6aW_y8Qm6FbswqzkU2kFbdbU0T9H1fFVPF9WzsBl-AJjWBc0 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+Improved+Binary+Quantum-based+Avian+Navigation+Optimizer+Algorithm+to+Select+Effective+Feature+Subset+from+Medical+Data%3A+A+COVID-19+Case+Study&rft.jtitle=Journal+of+bionics+engineering&rft.au=Fatahi%2C+Ali&rft.au=Nadimi-Shahraki%2C+Mohammad+H&rft.au=Zamani%2C+Hoda&rft.date=2024-01-01&rft.pub=Springer+Nature+B.V&rft.issn=1672-6529&rft.eissn=2543-2141&rft.volume=21&rft.issue=1&rft.spage=426&rft.epage=446&rft_id=info:doi/10.1007%2Fs42235-023-00433-y |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1672-6529&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1672-6529&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1672-6529&client=summon |