A distributed and energy-efficient KNN for EEG classification with dynamic money-saving policy in heterogeneous clusters

Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in High-Performance Computing (HPC) systems, where cost significantly impacts the tasks executed. Among these tasks, classification problems are considered due to their...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Computing Ročník 105; číslo 11; s. 2487 - 2510
Hlavní autoři:	Escobar, Juan José, Rodríguez, Francisco, Prieto, Beatriz, Kimovski, Dragi, Ortiz, Andrés, Damas, Miguel
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vienna Springer Vienna 01.11.2023 Springer Nature B.V
Témata:	Artificial Intelligence Classification Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Datasets Electroencephalography Energy consumption Energy management Energy policy High performance systems Information Systems Applications (incl.Internet) K-nearest neighbors algorithm Parallel programming Redundancy Regular Paper Software Engineering Sustainability Task complexity Heterogeneous clusters Energy-aware computing EEG classification KNN 68W15 Parallel and distributed programming Money-saving
ISSN:	0010-485X, 1436-5057
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in High-Performance Computing (HPC) systems, where cost significantly impacts the tasks executed. Among these tasks, classification problems are considered due to their great computational complexity, which is sometimes aggravated when processing high-dimensional datasets. In addition, implementing efficient applications for high-performance systems is not an easy task since hardware must be considered to maximize performance, especially on heterogeneous platforms with multi-core CPUs. Thus, this article proposes an efficient distributed K -Nearest Neighbors (KNN) for Electroencephalogram (EEG) classification that uses minimum Redundancy Maximum Relevance (mRMR) as a feature selection technique to reduce the dimensionality of the dataset. The approach implements an energy policy that can stop or resume the execution of the program based on the cost per Megawatt. Since the procedure is based on the master-worker scheme, the performance of three different workload distributions is also analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works that use the same dataset. It achieves a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy consumed by the sequential version. Moreover, the results show that financial costs can be reduced when energy policy is activated and the importance of developing efficient methods, proving that energy-aware computing is necessary for sustainable computing.
AbstractList	Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in High-Performance Computing (HPC) systems, where cost significantly impacts the tasks executed. Among these tasks, classification problems are considered due to their great computational complexity, which is sometimes aggravated when processing high-dimensional datasets. In addition, implementing efficient applications for high-performance systems is not an easy task since hardware must be considered to maximize performance, especially on heterogeneous platforms with multi-core CPUs. Thus, this article proposes an efficient distributed K-Nearest Neighbors (KNN) for Electroencephalogram (EEG) classification that uses minimum Redundancy Maximum Relevance (mRMR) as a feature selection technique to reduce the dimensionality of the dataset. The approach implements an energy policy that can stop or resume the execution of the program based on the cost per Megawatt. Since the procedure is based on the master-worker scheme, the performance of three different workload distributions is also analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works that use the same dataset. It achieves a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy consumed by the sequential version. Moreover, the results show that financial costs can be reduced when energy policy is activated and the importance of developing efficient methods, proving that energy-aware computing is necessary for sustainable computing. Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in High-Performance Computing (HPC) systems, where cost significantly impacts the tasks executed. Among these tasks, classification problems are considered due to their great computational complexity, which is sometimes aggravated when processing high-dimensional datasets. In addition, implementing efficient applications for high-performance systems is not an easy task since hardware must be considered to maximize performance, especially on heterogeneous platforms with multi-core CPUs. Thus, this article proposes an efficient distributed K -Nearest Neighbors (KNN) for Electroencephalogram (EEG) classification that uses minimum Redundancy Maximum Relevance (mRMR) as a feature selection technique to reduce the dimensionality of the dataset. The approach implements an energy policy that can stop or resume the execution of the program based on the cost per Megawatt. Since the procedure is based on the master-worker scheme, the performance of three different workload distributions is also analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works that use the same dataset. It achieves a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy consumed by the sequential version. Moreover, the results show that financial costs can be reduced when energy policy is activated and the importance of developing efficient methods, proving that energy-aware computing is necessary for sustainable computing.
Author	Ortiz, Andrés Damas, Miguel Kimovski, Dragi Prieto, Beatriz Escobar, Juan José Rodríguez, Francisco
Author_xml	– sequence: 1 givenname: Juan José orcidid: 0000-0002-4258-0264 surname: Escobar fullname: Escobar, Juan José email: jjescobar@ugr.es organization: Department of Software Engineering, CITIC, University of Granada – sequence: 2 givenname: Francisco surname: Rodríguez fullname: Rodríguez, Francisco organization: Department of Computer Engineering, Automation and Robotics, CITIC, University of Granada – sequence: 3 givenname: Beatriz surname: Prieto fullname: Prieto, Beatriz organization: Department of Computer Engineering, Automation and Robotics, CITIC, University of Granada – sequence: 4 givenname: Dragi surname: Kimovski fullname: Kimovski, Dragi organization: Institute of Information Technology, University of Klagenfurt – sequence: 5 givenname: Andrés surname: Ortiz fullname: Ortiz, Andrés organization: Department of Communications Engineering, University of Málaga – sequence: 6 givenname: Miguel surname: Damas fullname: Damas, Miguel organization: Department of Computer Engineering, Automation and Robotics, CITIC, University of Granada
BookMark	eNp9kE9PGzEQxa0KpIY_X6AnSz27Ha93194jQilURXApEjfLeMeJUWIH29uy375ugoTEgctYI7_fm5l3Qo5CDEjIFw7fOID8ngF6kAwawYDzQTD5iSx4K3rWQSePyAKAA2tV9_CZnOT8BFClaliQlws6-lySf5wKjtSEkWLAtJoZOuetx1Dor9tb6mKiy-UVtRuTs68_pvgY6F9f1nScg9l6S7d1p5ll88eHFd3Fjbcz9YGusWCKq2obp1wNplz7fEaOndlkPH99T8n9j-Xvy2t2c3f18_LihlnRi8IGO3QGHIpO1aK4QNEKtNiPvGlaB9z1xjWy6yUH0xsF7WCFbOp5CgzKTpySrwffXYrPE-ain-KUQh2pG9UPUoHkqqrUQWVTzDmh09aX_YklGb_RHPT_nPUhZ13D0_uctaxo8w7dJb81af4YEgcoV3FYYXrb6gPqHzKokuk
CitedBy_id	crossref_primary_10_1016_j_bspc_2025_108528 crossref_primary_10_1007_s42979_024_03396_x crossref_primary_10_3390_s25030846
Cites_doi	10.1002/hbm.23730 10.1002/asjc.2983 10.18201/ijisae.75836 10.1088/1741-2552/aace8c 10.1007/s13246-020-00897-w 10.3390/s21062096 10.1007/s11227-021-03740-5 10.1016/j.jphysparis.2006.03.012 10.1016/j.neucom.2016.09.123 10.1093/comjnl/bxm099 10.1007/s10586-007-0045-4 10.1504/IJAACS.2015.073191 10.1155/2019/8348791 10.1016/j.neucom.2015.08.112 10.3390/su13116114 10.1016/j.neunet.2018.04.018 10.3233/THC-174836 10.3390/challe6010117 10.1016/j.bspc.2021.102917 10.1016/j.future.2018.09.039 10.1016/j.cmpb.2004.10.009 10.1088/1741-2560/10/4/046014 10.1109/TGCN.2021.3107915 10.1371/journal.pone.0234178 10.1109/34.75512 10.1016/j.eswa.2006.02.005 10.1016/j.compbiomed.2016.08.012 10.1016/j.eswa.2018.03.053 10.1109/TSG.2015.2508443 10.1016/j.neucom.2021.08.003 10.1007/s11760-020-01767-4 10.1038/s42254-020-0208-2 10.1016/j.bbe.2017.08.006 10.1109/MC.2010.98 10.3390/computers8020042 10.1038/d41586-020-02503-1 10.1007/3-540-56602-3_152 10.1109/ISPA.2008.68 10.1007/978-3-642-15646-5_24 10.1109/CSB.2003.1227396 10.1007/978-3-319-16483-0_35 10.1109/CLOUD.2012.50 10.1007/978-3-319-58943-5_30 10.1007/978-94-017-8996-7_2 10.1109/IPDPS.2006.1639597 10.1109/CarpathianCC.2019.8765944 10.1166/asl.2018.10758 10.1109/INFCOM.2011.5934885 10.1007/978-3-319-59153-7_3 10.1109/CADSM.2019.8779312 10.1186/s13638-019-1497-y 10.1007/978-3-319-59153-7_2 10.1007/978-3-319-19258-1_12 10.1016/j.patter.2021.100340
ContentType	Journal Article
Copyright	The Author(s) 2023 The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: The Author(s) 2023 – notice: The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	C6C AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL 8G5 ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ GUQSH HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M2O MBDVC P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U
DOI	10.1007/s00607-023-01193-7
DatabaseName	Springer Nature OA Free Journals CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni Edition) Research Library (Alumni Edition) ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection ProQuest Technology Collection ProQuest One Community College ProQuest Central Korea Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student Research Library Prep SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ProQuest ABI/INFORM Global Computing Database ProQuest Research Library Research Library (Corporate) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central Basic
DatabaseTitle	CrossRef ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Research Library Prep Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Pharma Collection ABI/INFORM Complete ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Research Library ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) Business Premium Collection (Alumni)
DatabaseTitleList	ABI/INFORM Global (Corporate) CrossRef
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Mathematics Computer Science
EISSN	1436-5057
EndPage	2510
ExternalDocumentID	10_1007_s00607_023_01193_7
GrantInformation_xml	– fundername: European Regional Development Fund funderid: http://dx.doi.org/10.13039/501100008530 – fundername: Universidad de Granada – fundername: Ministerio de Ciencia, Innovación y Universidades grantid: PGC2018-098813-B-C31; PGC2018-098813-B-C32 funderid: http://dx.doi.org/10.13039/100014440
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 1N0 1SB 2.D 203 28- 29F 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 6TJ 78A 7WY 8AO 8FE 8FG 8FL 8G5 8TC 8UJ 8VB 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMOZ AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFFNX AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHQJS AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKVCP ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. B0M BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BKOMP BPHCQ BSONS C6C CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DWQXO EAD EAP EBA EBLON EBR EBS EBU ECS EDO EIOEI EJD EMK EPL ESBYG EST ESX FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K1G K60 K6V K6~ K7- KDC KOV KOW LAS LLZTM M0C M0N M2O M4Y MA- MK~ ML~ N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PT5 Q2X QOK QOS QWB R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TH9 TN5 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7X Z7Z Z81 Z83 Z88 Z8M Z8N Z8R Z8T Z8U Z8W Z92 ZL0 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFKWF AFOHR AGQPQ AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 7XB 8AL 8FD 8FK JQ2 L.- L7M L~C L~D MBDVC PKEHL PQEST PQUKI Q9U
ID	FETCH-LOGICAL-c363t-9c95a0fe358fe3813e343ece6d1224f01f6af2756710a6a8049c37200280ae753
IEDL.DBID	RSV
ISICitedReferencesCount	7
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001017581100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0010-485X
IngestDate	Wed Nov 26 13:51:57 EST 2025 Sat Nov 29 03:51:40 EST 2025 Tue Nov 18 22:20:39 EST 2025 Fri Feb 21 02:41:25 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Keywords	Heterogeneous clusters Energy-aware computing EEG classification KNN 68W15 Parallel and distributed programming Money-saving
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c363t-9c95a0fe358fe3813e343ece6d1224f01f6af2756710a6a8049c37200280ae753
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4258-0264
OpenAccessLink	https://link.springer.com/10.1007/s00607-023-01193-7
PQID	2869780718
PQPubID	48322
PageCount	24
ParticipantIDs	proquest_journals_2869780718 crossref_citationtrail_10_1007_s00607_023_01193_7 crossref_primary_10_1007_s00607_023_01193_7 springer_journals_10_1007_s00607_023_01193_7
PublicationCentury	2000
PublicationDate	20231100 2023-11-00 20231101
PublicationDateYYYYMMDD	2023-11-01
PublicationDate_xml	– month: 11 year: 2023 text: 20231100
PublicationDecade	2020
PublicationPlace	Vienna
PublicationPlace_xml	– name: Vienna – name: Wien
PublicationTitle	Computing
PublicationTitleAbbrev	Computing
PublicationYear	2023
Publisher	Springer Vienna Springer Nature B.V
Publisher_xml	– name: Springer Vienna – name: Springer Nature B.V
References	Sabancı, Koklu (CR25) 2015; 3 Lawhern, Solon, Waytowich, Gordon, Hung, Lance (CR40) 2018; 15 Judith, Priya, Mahendran, Gadekallu, Ambati (CR53) 2022 Richhariya, Tanveer (CR23) 2018; 106 CR37 Lotze, Halsband (CR29) 2006; 99 CR33 CR32 CR31 Czarnul, Proficz, Krzywaniak (CR4) 2019 Raudys, Jain (CR46) 1991; 13 Truong, Nguyen, Kuhlmann, Bonyadi, Yang, Ippolito, Kavehei (CR24) 2018; 105 Tushar, Yuen, Smith, Poor (CR3) 2017; 8 CR2 Kumar, Lu (CR14) 2010; 43 Tripathi, Sivaraman, Tamarapalli (CR56) 2021; 6 CR9 CR49 Subasi (CR22) 2007; 32 CR48 CR47 Andrae, Edler (CR1) 2015; 6 CR43 CR42 Sharma, Sharma (CR19) 2016; 77 Aquino-Brítez, Ortiz, Ortega, León, Formoso, Gan, Escobar (CR39) 2021; 21 Jo, Lee, Oh (CR44) 2019; 8 Manganelli, Soldati, Martirano, Ramakrishna (CR7) 2021; 13 Ortega, Asensio-Cubero, Gan, Ortiz (CR35) 2016; 15 Ding, Wienke, Zhang (CR50) 2015; 8 Asensio-Cubero, Gan, Palaniappan (CR30) 2013; 10 Akbari, Ghofrani, Zakalvand, Tariq Sadiq (CR15) 2021; 69 CR16 Subasi, Erçelebi (CR21) 2005; 78 Wang, Wang, Zhao, Cheng (CR5) 2019; 92 CR12 CR11 CR10 CR54 Lefurgy, Wang, Ware (CR13) 2008; 11 CR52 CR51 González, Ortega, Escobar, Damas (CR36) 2021; 463 Hassaballah, Omran, Mahdy (CR55) 2008; 51 Choubey, Pandey (CR20) 2021; 15 Li, Wei, Xiong, Ma, Tian (CR6) 2021; 77 Ibrahim, Djemal, Alsuwailem (CR18) 2018; 38 CR28 Marković, Mizrahi, Querlioz, Grollier (CR8) 2020; 2 CR27 Li, Xu, Liu, Lu (CR26) 2018; 26 León, Escobar, Ortiz, Ortega, González, Martín-Smith, Gan, Damas (CR38) 2020; 15 Schirrmeister, Springenberg, Fiederer, Glasstetter, Eggensperger, Tangermann, Hutter, Burgard, Ball (CR41) 2017; 38 Deng, Zhu, Cheng, Zong, Zhang (CR45) 2016; 195 Saeedi, Saeedi, Maghsoudi (CR17) 2020; 43 Martín-Smith, Ortega, Asensio-Cubero, Gan, Ortiz (CR34) 2017; 250 M Li (1193_CR26) 2018; 26 SJ Raudys (1193_CR46) 1991; 13 H Akbari (1193_CR15) 2021; 69 P Martín-Smith (1193_CR34) 2017; 250 S Ibrahim (1193_CR18) 2018; 38 1193_CR28 1193_CR27 C Lefurgy (1193_CR13) 2008; 11 J González (1193_CR36) 2021; 463 Z Deng (1193_CR45) 2016; 195 J Asensio-Cubero (1193_CR30) 2013; 10 ND Truong (1193_CR24) 2018; 105 J Ortega (1193_CR35) 2016; 15 K Sabancı (1193_CR25) 2015; 3 1193_CR33 1193_CR32 1193_CR31 RT Schirrmeister (1193_CR41) 2017; 38 M Manganelli (1193_CR7) 2021; 13 A Subasi (1193_CR21) 2005; 78 ASG Andrae (1193_CR1) 2015; 6 D Aquino-Brítez (1193_CR39) 2021; 21 J León (1193_CR38) 2020; 15 1193_CR37 R Tripathi (1193_CR56) 2021; 6 D Marković (1193_CR8) 2020; 2 B Richhariya (1193_CR23) 2018; 106 1193_CR47 M Hassaballah (1193_CR55) 2008; 51 1193_CR43 1193_CR42 Z Wang (1193_CR5) 2019; 92 I Jo (1193_CR44) 2019; 8 P Czarnul (1193_CR4) 2019 M Lotze (1193_CR29) 2006; 99 H Sharma (1193_CR19) 2016; 77 1193_CR49 1193_CR9 1193_CR48 VJ Lawhern (1193_CR40) 2018; 15 M Saeedi (1193_CR17) 2020; 43 W Tushar (1193_CR3) 2017; 8 1193_CR12 1193_CR11 K Kumar (1193_CR14) 2010; 43 1193_CR10 1193_CR54 1193_CR52 1193_CR51 AM Judith (1193_CR53) 2022 A Subasi (1193_CR22) 2007; 32 1193_CR16 F Ding (1193_CR50) 2015; 8 1193_CR2 H Choubey (1193_CR20) 2021; 15 H Li (1193_CR6) 2021; 77
References_xml	– volume: 38 start-page: 5391 issue: 11 year: 2017 end-page: 5420 ident: CR41 article-title: Deep learning with convolutional neural networks for EEG decoding and visualization publication-title: Hum Brain Mapp doi: 10.1002/hbm.23730 – ident: CR49 – year: 2022 ident: CR53 article-title: Two-phase classification: ANN and A-SVM classifiers on motor imagery BCI publication-title: Asian J Control doi: 10.1002/asjc.2983 – ident: CR16 – volume: 3 start-page: 127 issue: 4 year: 2015 end-page: 130 ident: CR25 article-title: The classification of eye state by using kNN and MLP classification models according to the EEG signals publication-title: Int J Intell Syst Appl Eng doi: 10.18201/ijisae.75836 – ident: CR51 – ident: CR12 – volume: 15 issue: 5 year: 2018 ident: CR40 article-title: EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces publication-title: J Neural Eng doi: 10.1088/1741-2552/aace8c – ident: CR54 – volume: 43 start-page: 1007 issue: 3 year: 2020 end-page: 1018 ident: CR17 article-title: Major depressive disorder assessment via enhanced K-nearest neighbor method and EEG signals publication-title: Phys Eng Sci Med doi: 10.1007/s13246-020-00897-w – ident: CR42 – volume: 21 start-page: 2096 issue: 6 year: 2021 ident: CR39 article-title: Optimization of deep architectures for eeg signal classification: An automl approach using evolutionary algorithms publication-title: Sensors doi: 10.3390/s21062096 – volume: 77 start-page: 11575 issue: 10 year: 2021 end-page: 11596 ident: CR6 article-title: A frequency-aware and energy-saving strategy based on DVFS for spark publication-title: J Supercomput doi: 10.1007/s11227-021-03740-5 – volume: 15 start-page: 149 issue: 1 year: 2016 end-page: 164 ident: CR35 article-title: Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection publication-title: BioMedical Eng OnLine – volume: 99 start-page: 386 issue: 4 year: 2006 end-page: 395 ident: CR29 article-title: Motor imagery publication-title: J Physiol Paris doi: 10.1016/j.jphysparis.2006.03.012 – volume: 250 start-page: 45 year: 2017 end-page: 56 ident: CR34 article-title: A supervised filter method for multi-objective feature selection in EEG classification based on multi-resolution analysis for BCI publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.123 – volume: 51 start-page: 630 issue: 6 year: 2008 end-page: 649 ident: CR55 article-title: A review of SIMD multimedia extensions and their usage in scientific and engineering applications publication-title: Comput J doi: 10.1093/comjnl/bxm099 – volume: 11 start-page: 183 issue: 1 year: 2008 end-page: 195 ident: CR13 article-title: Power capping: a prelude to power shifting publication-title: Clust Comput doi: 10.1007/s10586-007-0045-4 – volume: 8 start-page: 424 issue: 4 year: 2015 end-page: 438 ident: CR50 article-title: Dynamic MPI parallel task scheduling based on a master-worker pattern in cloud computing publication-title: Int J Auton Adapt Commun Syst doi: 10.1504/IJAACS.2015.073191 – year: 2019 ident: CR4 article-title: Energy-aware high-performance computing: Survey of state-of-the-art tools, techniques, and environments publication-title: Sci Progr doi: 10.1155/2019/8348791 – ident: CR11 – ident: CR9 – volume: 195 start-page: 143 year: 2016 end-page: 148 ident: CR45 article-title: Efficient kNN classification algorithm for big data publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.08.112 – volume: 13 start-page: 6114 issue: 11 year: 2021 ident: CR7 article-title: Strategies for improving the sustainability of data centers via energy mix, energy conservation, and circular energy publication-title: Sustainability doi: 10.3390/su13116114 – ident: CR32 – volume: 105 start-page: 104 year: 2018 end-page: 111 ident: CR24 article-title: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram publication-title: Neural Netw doi: 10.1016/j.neunet.2018.04.018 – volume: 26 start-page: 509 issue: S1 year: 2018 end-page: 519 ident: CR26 article-title: Emotion recognition from multichannel EEG signals using K-nearest neighbor classification publication-title: Technol Health Care doi: 10.3233/THC-174836 – volume: 6 start-page: 117 issue: 1 year: 2015 end-page: 157 ident: CR1 article-title: On global electricity usage of communication technology: Trends to 2030 publication-title: Challenges doi: 10.3390/challe6010117 – volume: 69 year: 2021 ident: CR15 article-title: Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2021.102917 – ident: CR43 – ident: CR47 – volume: 92 start-page: 198 year: 2019 end-page: 209 ident: CR5 article-title: Energy optimization of parallel programs in a heterogeneous system by combining processor core-shutdown and dynamic voltage scaling publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2018.09.039 – ident: CR2 – volume: 78 start-page: 87 issue: 2 year: 2005 end-page: 99 ident: CR21 article-title: Classification of EEG signals using neural network and logistic regression publication-title: Comput Method Programs Biomed doi: 10.1016/j.cmpb.2004.10.009 – ident: CR37 – ident: CR10 – ident: CR33 – volume: 10 start-page: 21 issue: 4 year: 2013 end-page: 26 ident: CR30 article-title: Multiresolution analysis over simple graphs for brain computer interfaces publication-title: J Neural Eng doi: 10.1088/1741-2560/10/4/046014 – volume: 6 start-page: 472 issue: 1 year: 2021 end-page: 483 ident: CR56 article-title: Distributed cost-aware fault-tolerant load balancing in geo-distributed data centers publication-title: IEEE Trans Green Commun Netw doi: 10.1109/TGCN.2021.3107915 – volume: 15 issue: 6 year: 2020 ident: CR38 article-title: Deep learning for eeg-based motor imagery classification: accuracy-cost trade-off publication-title: PLoS ONE doi: 10.1371/journal.pone.0234178 – volume: 13 start-page: 252 issue: 3 year: 1991 end-page: 264 ident: CR46 article-title: Small sample size effects in statistical pattern recognition: Recommendations for practitioners publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/34.75512 – ident: CR27 – volume: 32 start-page: 1084 issue: 4 year: 2007 end-page: 1093 ident: CR22 article-title: EEG signal classification using wavelet feature extraction and a mixture of expert model publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2006.02.005 – ident: CR48 – volume: 77 start-page: 116 year: 2016 end-page: 124 ident: CR19 article-title: An algorithm for sleep apnea detection from single-lead ECG using hermite basis functions publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2016.08.012 – volume: 106 start-page: 169 year: 2018 end-page: 182 ident: CR23 article-title: EEG signal classification using universum support vector machine publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.03.053 – volume: 8 start-page: 1790 issue: 4 year: 2017 end-page: 1801 ident: CR3 article-title: Price discrimination for energy trading in smart grid: a game theoretic approach publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2015.2508443 – volume: 463 start-page: 59 year: 2021 end-page: 76 ident: CR36 article-title: A lexicographic cooperative co-evolutionary approach for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.08.003 – ident: CR52 – ident: CR31 – volume: 15 start-page: 475 issue: 3 year: 2021 end-page: 483 ident: CR20 article-title: A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier publication-title: SIViP doi: 10.1007/s11760-020-01767-4 – volume: 2 start-page: 499 issue: 9 year: 2020 end-page: 510 ident: CR8 article-title: Physics for neuromorphic computing publication-title: Nat Rev Phys doi: 10.1038/s42254-020-0208-2 – ident: CR28 – volume: 38 start-page: 16 issue: 1 year: 2018 end-page: 26 ident: CR18 article-title: Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis publication-title: Biocybern Biomed Eng doi: 10.1016/j.bbe.2017.08.006 – volume: 43 start-page: 51 issue: 4 year: 2010 end-page: 56 ident: CR14 article-title: Cloud computing for mobile users: Can offloading computation save energy? publication-title: Computer doi: 10.1109/MC.2010.98 – volume: 8 start-page: 42 issue: 2 year: 2019 ident: CR44 article-title: Improved measures of redundancy and relevance for mRMR feature selection publication-title: Computers doi: 10.3390/computers8020042 – volume: 105 start-page: 104 year: 2018 ident: 1193_CR24 publication-title: Neural Netw doi: 10.1016/j.neunet.2018.04.018 – volume: 8 start-page: 424 issue: 4 year: 2015 ident: 1193_CR50 publication-title: Int J Auton Adapt Commun Syst doi: 10.1504/IJAACS.2015.073191 – volume: 77 start-page: 116 year: 2016 ident: 1193_CR19 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2016.08.012 – volume: 250 start-page: 45 year: 2017 ident: 1193_CR34 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.123 – ident: 1193_CR9 doi: 10.1038/d41586-020-02503-1 – ident: 1193_CR27 doi: 10.1007/3-540-56602-3_152 – volume: 38 start-page: 16 issue: 1 year: 2018 ident: 1193_CR18 publication-title: Biocybern Biomed Eng doi: 10.1016/j.bbe.2017.08.006 – volume: 26 start-page: 509 issue: S1 year: 2018 ident: 1193_CR26 publication-title: Technol Health Care doi: 10.3233/THC-174836 – volume: 2 start-page: 499 issue: 9 year: 2020 ident: 1193_CR8 publication-title: Nat Rev Phys doi: 10.1038/s42254-020-0208-2 – volume: 78 start-page: 87 issue: 2 year: 2005 ident: 1193_CR21 publication-title: Comput Method Programs Biomed doi: 10.1016/j.cmpb.2004.10.009 – ident: 1193_CR54 doi: 10.1109/ISPA.2008.68 – volume: 43 start-page: 51 issue: 4 year: 2010 ident: 1193_CR14 publication-title: Computer doi: 10.1109/MC.2010.98 – volume: 99 start-page: 386 issue: 4 year: 2006 ident: 1193_CR29 publication-title: J Physiol Paris doi: 10.1016/j.jphysparis.2006.03.012 – ident: 1193_CR49 doi: 10.1007/978-3-642-15646-5_24 – ident: 1193_CR48 – volume: 15 issue: 6 year: 2020 ident: 1193_CR38 publication-title: PLoS ONE doi: 10.1371/journal.pone.0234178 – volume: 8 start-page: 42 issue: 2 year: 2019 ident: 1193_CR44 publication-title: Computers doi: 10.3390/computers8020042 – volume: 13 start-page: 252 issue: 3 year: 1991 ident: 1193_CR46 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/34.75512 – ident: 1193_CR43 doi: 10.1109/CSB.2003.1227396 – volume: 8 start-page: 1790 issue: 4 year: 2017 ident: 1193_CR3 publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2015.2508443 – volume: 51 start-page: 630 issue: 6 year: 2008 ident: 1193_CR55 publication-title: Comput J doi: 10.1093/comjnl/bxm099 – volume: 10 start-page: 21 issue: 4 year: 2013 ident: 1193_CR30 publication-title: J Neural Eng doi: 10.1088/1741-2560/10/4/046014 – ident: 1193_CR32 doi: 10.1007/978-3-319-16483-0_35 – volume: 43 start-page: 1007 issue: 3 year: 2020 ident: 1193_CR17 publication-title: Phys Eng Sci Med doi: 10.1007/s13246-020-00897-w – volume: 6 start-page: 117 issue: 1 year: 2015 ident: 1193_CR1 publication-title: Challenges doi: 10.3390/challe6010117 – ident: 1193_CR10 doi: 10.1109/CLOUD.2012.50 – volume: 463 start-page: 59 year: 2021 ident: 1193_CR36 publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.08.003 – volume: 21 start-page: 2096 issue: 6 year: 2021 ident: 1193_CR39 publication-title: Sensors doi: 10.3390/s21062096 – ident: 1193_CR47 doi: 10.1007/978-3-319-58943-5_30 – volume: 69 year: 2021 ident: 1193_CR15 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2021.102917 – volume: 15 start-page: 149 issue: 1 year: 2016 ident: 1193_CR35 publication-title: BioMedical Eng OnLine – ident: 1193_CR28 doi: 10.1007/978-94-017-8996-7_2 – year: 2022 ident: 1193_CR53 publication-title: Asian J Control doi: 10.1002/asjc.2983 – ident: 1193_CR11 doi: 10.1109/IPDPS.2006.1639597 – ident: 1193_CR52 doi: 10.1109/CarpathianCC.2019.8765944 – ident: 1193_CR16 doi: 10.1166/asl.2018.10758 – volume: 32 start-page: 1084 issue: 4 year: 2007 ident: 1193_CR22 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2006.02.005 – volume: 6 start-page: 472 issue: 1 year: 2021 ident: 1193_CR56 publication-title: IEEE Trans Green Commun Netw doi: 10.1109/TGCN.2021.3107915 – ident: 1193_CR12 doi: 10.1109/INFCOM.2011.5934885 – ident: 1193_CR37 doi: 10.1007/978-3-319-59153-7_3 – volume: 13 start-page: 6114 issue: 11 year: 2021 ident: 1193_CR7 publication-title: Sustainability doi: 10.3390/su13116114 – year: 2019 ident: 1193_CR4 publication-title: Sci Progr doi: 10.1155/2019/8348791 – volume: 92 start-page: 198 year: 2019 ident: 1193_CR5 publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2018.09.039 – volume: 77 start-page: 11575 issue: 10 year: 2021 ident: 1193_CR6 publication-title: J Supercomput doi: 10.1007/s11227-021-03740-5 – volume: 3 start-page: 127 issue: 4 year: 2015 ident: 1193_CR25 publication-title: Int J Intell Syst Appl Eng doi: 10.18201/ijisae.75836 – volume: 106 start-page: 169 year: 2018 ident: 1193_CR23 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2018.03.053 – volume: 195 start-page: 143 year: 2016 ident: 1193_CR45 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.08.112 – volume: 38 start-page: 5391 issue: 11 year: 2017 ident: 1193_CR41 publication-title: Hum Brain Mapp doi: 10.1002/hbm.23730 – ident: 1193_CR51 doi: 10.1109/CADSM.2019.8779312 – volume: 15 start-page: 475 issue: 3 year: 2021 ident: 1193_CR20 publication-title: SIViP doi: 10.1007/s11760-020-01767-4 – ident: 1193_CR42 doi: 10.1186/s13638-019-1497-y – ident: 1193_CR31 doi: 10.1007/978-3-319-59153-7_2 – ident: 1193_CR33 doi: 10.1007/978-3-319-19258-1_12 – ident: 1193_CR2 doi: 10.1016/j.patter.2021.100340 – volume: 11 start-page: 183 issue: 1 year: 2008 ident: 1193_CR13 publication-title: Clust Comput doi: 10.1007/s10586-007-0045-4 – volume: 15 issue: 5 year: 2018 ident: 1193_CR40 publication-title: J Neural Eng doi: 10.1088/1741-2552/aace8c
SSID	ssj0002389
Score	2.3733838
Snippet	Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in High-Performance...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	2487
SubjectTerms	Artificial Intelligence Classification Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Datasets Electroencephalography Energy consumption Energy management Energy policy High performance systems Information Systems Applications (incl.Internet) K-nearest neighbors algorithm Parallel programming Redundancy Regular Paper Software Engineering Sustainability Task complexity
SummonAdditionalLinks	– databaseName: Computer Science Database dbid: K7- link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT8MwDLZgcIADgwFiMFAO3CCi78cJTWgDaVBxAGm3KkpTgYS6sW4I_j12mnYCiV249NLUimrHcez4-wDOfenkylMZdyUuN8_3BY-tTHC0J2HHuYjDCl3_PkySaDyOH03CrTTXKmufqB11NpGUI79yooDActCVXk_fObFGUXXVUGisw4btODbZ-SjkjSfG7agKf9HXeJE_Nk0zunWOgEhCjkM4oZ65PPy5MS2jzV8FUr3vDNv_nfEu7JiIk_UrE9mDNVV0oF2zOTCzuDuw_dAguJb78NlnGWHqEh2WypgoMqZ0myBXGnQC9yo2ShKGMS8bDG6ZpCicrh1pTTNK77KsYrtnOBH1xUtBuQs21UDE7LVgL3QTZ4IGrCaLEgUsCLOhPIDn4eDp5o4blgYu3cCd81jGvrBy5foRPiLKqnqukirIqGiXW3YeiJxA5jGWEYGI8EgiiRqHarpC4WnpEFoFzuMIWCyDWFjKym0VoYxI4GlQuk7uYZCJ_1F2wa5VlEoDYU5MGm9pA76s1ZqiWlOt1jTswkXzzbQC8Fg5ulfrMjWLuUyXiuzCZW0Ny9d_SzteLe0Etoi8vups7EFrPluoU9iUH_PXcnamTfkbCQ_4WQ priority: 102 providerName: ProQuest
Title	A distributed and energy-efficient KNN for EEG classification with dynamic money-saving policy in heterogeneous clusters
URI	https://link.springer.com/article/10.1007/s00607-023-01193-7 https://www.proquest.com/docview/2869780718
Volume	105
WOSCitedRecordID	wos001017581100001&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: PRVAVX databaseName: SpringerLink Journals customDbUrl: eissn: 1436-5057 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002389 issn: 0010-485X databaseCode: RSV dateStart: 19970101 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/eLvHCXMwnV1LT8MwDLbY4AAH3ojxmHLgBpG69ZUeYdpAGpRpvAaXKkpTMQkNtG4I_j12-phAgAQXS1VTq4rt2InjzwAHrmom2tExtxWam-O6kgdWLDnqk2wEiQz8DF3_3A9DMRgEvbwoLC1uuxcpSbNSl8VuBB3ic_QxnHDKbO5XYB7dnSBz7F_dlusvOqEs6MUVxhHuIC-V-Z7HZ3c0izG_pEWNt-ms_O8_V2E5jy7ZcaYOazCnR-uwUnRuYLkhr8PSRYnWmm7A2zGLCT-XWl_pmMlRzLQpCeTaAEygX2LdMGQY37J2-5QpirjpipGRKqOjXBZnne0ZarV-56mkcwr2YkCH2XDEHunWzTMqq36epshgSvgM6SbcdNrXrTOed2TgyvbsCQ9U4Eor0bYrkAg6QXVsrbQXU4IusRqJJxMClMe4RXpS4PZDURscyt9KjTujLaiO8D-2gQXKC6SlraShBfIQEnd-ym4mDgaUGPaoGjQKwUQqhyunrhlPUQm0bCY6womOzERHfg0Oy29eMrCOX0fvFfKOcsNNo6bwCJMJPXYNjgr5zl7_zG3nb8N3YZEa12dVjXtQnYyneh8W1OtkmI7rUPHv7uswf9IOe3186voc6YXVItq8RNpzH-pG7T8Afjr0qg
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB5VAYn20EIpakqBOcCJrur344BQVRJSJbU4pFJuZrVei0jghDgB8qf4jcysHxFI5JZDL77YHtm73zx2ducbgNe-cnLt6Uy4itTN830pYiuTgvAk7TiXcVix64_CJIkmk_jTHvxuamH4WGVjE42hzmaKc-SXThQwWQ6Z0vfz74K7RvHuatNCo4LFUK9_0pKtfHfzgeb3jeP0e-Prgai7CgjlBu5SxCr2pZVr14_oEnEW0HO10kHGm0y5ZeeBzJkUnXyvDGREIbTiVi68Byl1yF0iyOQ_8DxSBz4qaF23lp_cXxVuk23zIn9SF-mYUj0mPgkFPSKYZc0V4d-OcBPd_rMha_xc_-i-jdBjOKwjaryqVOAJ7OniGI6abhVYG69jOLhtGWrLp_DrCjPmDOZ2XzpDWWSoTRmk0IZUg3wxDpMEKabHXu8jKl5l8LEqg2Tk9DVm60J-myqkH9drUUrOzeDcEC3jtMAvfNJoRgqqZ6uSBKyYk6I8gbudjMYz6BT0HaeAsQpiaWkrt3VEMiJJq13lOrlHQTTNm-qC3UAiVTVFO3cK-Zq25NIGRinBKDUwSsMuvG3fmVcEJVufPm-wk9bGqkw3wOnCRYO-ze3_SzvbLu0VPBqMb0fp6CYZPod9h8FvqjjPobNcrPQLeKh-LKfl4qVRI4TPu0blH3nPUs4
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9tAEB4hQIgegPIQKZTOoZzKCsdvH6oKlYSigMWBSrmZ1XpXRAInxUnb_DV-XWfWdqIiwY0DF19sj-zdbx47u_MNwOdAuUb7OheeInXzg0CKxMmlIDzJdmJkElXs-hdRmsb9fnK1AI9NLQwfq2xsojXU-VBxjvzYjUMmyyFTemzqYxFXp91vo1-CO0jxTmvTTqOCSE9P_9Dyrfx6fkpzfei63c719x-i7jAglBd6Y5GoJJCO0V4Q0yXmjKDvaaXDnDecjNM2oTRMkE5-WIYypnBacVsX3o-UOuKOEWT-l8gLB6xjvUjMvAC5wir0Jjvnx0G_LtixZXtMghIJekQw45onov-d4jzSfbI5a31ed_0tj9YGrNWRNp5UqvEeFnSxCetNFwusjdomvLucMdeWW_D3BHPmEuY2YDpHWeSobXmk0JZsg3w09tIUKdbHTucMFa8--LiVRThyWhvzaSHvBwrpx_VUlJJzNjiyBMw4KPCWTyANSXH1cFKSgAlzVZTb8PNVRmMHFgv6jl3ARIWJdLRj2jomGbGkVbDyXONTcE1zqFrQbuCRqZq6nTuI3GUz0mkLqYwglVlIZVELvszeGVXEJS8-vd_gKKuNWJnNQdSCowaJ89vPS_vwsrRPsEJgzC7O094erLqsB7a4cx8Wxw8T_RGW1e_xoHw4sBqFcPPaoPwH-ulbdA
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=A+distributed+and+energy-efficient+KNN+for+EEG+classification+with+dynamic+money-saving+policy+in+heterogeneous+clusters&rft.jtitle=Computing&rft.au=Escobar%2C+Juan+Jos%C3%A9&rft.au=Rodr%C3%ADguez%2C+Francisco&rft.au=Prieto%2C+Beatriz&rft.au=Kimovski%2C+Dragi&rft.date=2023-11-01&rft.pub=Springer+Vienna&rft.issn=0010-485X&rft.eissn=1436-5057&rft.volume=105&rft.issue=11&rft.spage=2487&rft.epage=2510&rft_id=info:doi/10.1007%2Fs00607-023-01193-7&rft.externalDocID=10_1007_s00607_023_01193_7
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0010-485X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0010-485X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0010-485X&client=summon